Multi-resolution analysis for region of interest extraction in thermographic nondestructive evaluation

NASA Astrophysics Data System (ADS)

Ortiz-Jaramillo, B.; Fandiño Toro, H. A.; Benitez-Restrepo, H. D.; Orjuela-Vargas, S. A.; Castellanos-Domínguez, G.; Philips, W.

2012-03-01

Infrared Non-Destructive Testing (INDT) is known as an effective and rapid method for nondestructive inspection. It can detect a broad range of near-surface structuring flaws in metallic and composite components. Those flaws are modeled as a smooth contour centered at peaks of stored thermal energy, termed Regions of Interest (ROI). Dedicated methodologies must detect the presence of those ROIs. In this paper, we present a methodology for ROI extraction in INDT tasks. The methodology deals with the difficulties due to the non-uniform heating. The non-uniform heating affects low spatial/frequencies and hinders the detection of relevant points in the image. In this paper, a methodology for ROI extraction in INDT using multi-resolution analysis is proposed, which is robust to ROI low contrast and non-uniform heating. The former methodology includes local correlation, Gaussian scale analysis and local edge detection. In this methodology local correlation between image and Gaussian window provides interest points related to ROIs. We use a Gaussian window because thermal behavior is well modeled by Gaussian smooth contours. Also, the Gaussian scale is used to analyze details in the image using multi-resolution analysis avoiding low contrast, non-uniform heating and selection of the Gaussian window size. Finally, local edge detection is used to provide a good estimation of the boundaries in the ROI. Thus, we provide a methodology for ROI extraction based on multi-resolution analysis that is better or equal compared with the other dedicate algorithms proposed in the state of art.

SU-F-J-183: Interior Region-Of-Interest Tomography by Using Inverse Geometry System

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, K; Kim, D; Kang, S

2016-06-15

Purpose: The inverse geometry computed tomography (IGCT) composed of multiple source and small size detector has several merits such as reduction of scatter effect and large volumetric imaging within one rotation without cone-beam artifact, compared to conventional cone-beam computed tomography (CBCT). By using this multi-source characteristics, we intend to present a selective and multiple interior region-of-interest (ROI) imaging method by using a designed source on-off sequence of IGCT. Methods: All of the IGCT sources are operated one by one sequentially, and each projection in the shape of narrow cone-beam covers its own partial volume of full field of view (FOV)more » determined from system geometry. Thus, through controlling multi source operation, limited irradiation within ROI is possible and selective radon space data for ROI imaging can be acquired without additional X-ray filtration. With this feature, we designed a source on-off sequence for multi ROI-IGCT imaging, and projections of ROI-IGCT were generated by using the on-off sequence. Multi ROI-IGCT images were reconstructed by using filtered back-projection algorithm. All these imaging process of our study has been performed by utilizing digital phantom and patient CT data. ROI-IGCT images of the phantom were compared to CBCT image and the phantom data for the image quality evaluation. Results: Image quality of ROI-IGCT was comparable to that of CBCT. However, the distal axial-plane from the FOV center, large cone-angle region, ROI-IGCT showed uniform image quality without significant cone-beam artifact contrary to CBCT. Conclusion: ROI-IGCT showed comparable image quality and has the capability to provide multi ROI image within a rotation. Projection of ROI-IGCT is performed by selective irradiation, hence unnecessary imaging dose to non-interest region can be reduced. In this regard, it seems to be useful for diagnostic or image guidance purpose in radiotherapy such as low dose target localization and patient alignment. This research was supported by the Mid-career Researcher Program through NRF funded by the Ministry of Science, ICT & Future Planning of Korea (NRF-2014R1A2A1A10050270) and by the Radiation Technology R&D program through the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning (No. 2013M2A2A7038291)« less

Arbitrary shape region-of-interest fluoroscopy system

NASA Astrophysics Data System (ADS)

Xu, Tong; Le, Huy; Molloi, Sabee Y.

2002-05-01

Region-of-interest (ROI) fluoroscopy has previously been investigated as a method to reduce x-ray exposure to the patient and the operator. This ROI fluoroscopy technique allows the operator to arbitrarily determine the shape, size, and location of the ROI. A device was used to generate patient specific x-ray beam filters. The device is comprised of 18 step-motors that control a 16 X 16 matrix of pistons to form the filter from a deformable attenuating material. Patient exposure reductions were measured to be 84 percent for a 65 kVp beam. Operator exposure reduction was measured to be 69 percent. Due to the reduced x-ray scatter, image contrast was improved by 23 percent inside the ROI. The reduced gray level in the periphery was corrected using an experimentally determined compensation ratio. A running average interpolation technique was used to eliminate the artifacts from the ROI edge. As expected, the final corrected images show increased noise in the periphery. However, the anatomical structures in the periphery could still be visualized. This arbitrary shaped region of interest fluoroscopic technique was shown to be effective in terms of its ability to reduce patient and operator exposure without significant reduction in image quality. The ability to define an arbitrary shaped ROI should make the technique more clinically feasible.

Interior region-of-interest reconstruction using a small, nearly piecewise constant subregion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Taguchi, Katsuyuki; Xu Jingyan; Srivastava, Somesh

2011-03-15

Purpose: To develop a method to reconstruct an interior region-of-interest (ROI) image with sufficient accuracy that uses differentiated backprojection (DBP) projection onto convex sets (POCS) [H. Kudo et al., ''Tiny a priori knowledge solves the interior problem in computed tomography'', Phys. Med. Biol. 53, 2207-2231 (2008)] and a tiny knowledge that there exists a nearly piecewise constant subregion. Methods: The proposed method first employs filtered backprojection to reconstruct an image on which a tiny region P with a small variation in the pixel values is identified inside the ROI. Total variation minimization [H. Yu and G. Wang, ''Compressed sensing basedmore » interior tomography'', Phys. Med. Biol. 54, 2791-2805 (2009); W. Han et al., ''A general total variation minimization theorem for compressed sensing based interior tomography'', Int. J. Biomed. Imaging 2009, Article 125871 (2009)] is then employed to obtain pixel values in the subregion P, which serve as a priori knowledge in the next step. Finally, DBP-POCS is performed to reconstruct f(x,y) inside the ROI. Clinical data and the reconstructed image obtained by an x-ray computed tomography system (SOMATOM Definition; Siemens Healthcare) were used to validate the proposed method. The detector covers an object with a diameter of {approx}500 mm. The projection data were truncated either moderately to limit the detector coverage to diameter 350 mm of the object or severely to cover diameter 199 mm. Images were reconstructed using the proposed method. Results: The proposed method provided ROI images with correct pixel values in all areas except near the edge of the ROI. The coefficient of variation, i.e., the root mean square error divided by the mean pixel values, was less than 2.0% or 4.5% with the moderate or severe truncation cases, respectively, except near the boundary of the ROI. Conclusions: The proposed method allows for reconstructing interior ROI images with sufficient accuracy with a tiny knowledge that there exists a nearly piecewise constant subregion.« less

Region of interest extraction based on multiscale visual saliency analysis for remote sensing images

NASA Astrophysics Data System (ADS)

Zhang, Yinggang; Zhang, Libao; Yu, Xianchuan

2015-01-01

Region of interest (ROI) extraction is an important component of remote sensing image processing. However, traditional ROI extraction methods are usually prior knowledge-based and depend on classification, segmentation, and a global searching solution, which are time-consuming and computationally complex. We propose a more efficient ROI extraction model for remote sensing images based on multiscale visual saliency analysis (MVS), implemented in the CIE L*a*b* color space, which is similar to visual perception of the human eye. We first extract the intensity, orientation, and color feature of the image using different methods: the visual attention mechanism is used to eliminate the intensity feature using a difference of Gaussian template; the integer wavelet transform is used to extract the orientation feature; and color information content analysis is used to obtain the color feature. Then, a new feature-competition method is proposed that addresses the different contributions of each feature map to calculate the weight of each feature image for combining them into the final saliency map. Qualitative and quantitative experimental results of the MVS model as compared with those of other models show that it is more effective and provides more accurate ROI extraction results with fewer holes inside the ROI.

SU-E-I-37: Low-Dose Real-Time Region-Of-Interest X-Ray Fluoroscopic Imaging with a GPU-Accelerated Spatially Different Bilateral Filtering

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chung, H; Lee, J; Pua, R

2014-06-01

Purpose: The purpose of our study is to reduce imaging radiation dose while maintaining image quality of region of interest (ROI) in X-ray fluoroscopy. A low-dose real-time ROI fluoroscopic imaging technique which includes graphics-processing-unit- (GPU-) accelerated image processing for brightness compensation and noise filtering was developed in this study. Methods: In our ROI fluoroscopic imaging, a copper filter is placed in front of the X-ray tube. The filter contains a round aperture to reduce radiation dose to outside of the aperture. To equalize the brightness difference between inner and outer ROI regions, brightness compensation was performed by use of amore » simple weighting method that applies selectively to the inner ROI, the outer ROI, and the boundary zone. A bilateral filtering was applied to the images to reduce relatively high noise in the outer ROI images. To speed up the calculation of our technique for real-time application, the GPU-acceleration was applied to the image processing algorithm. We performed a dosimetric measurement using an ion-chamber dosimeter to evaluate the amount of radiation dose reduction. The reduction of calculation time compared to a CPU-only computation was also measured, and the assessment of image quality in terms of image noise and spatial resolution was conducted. Results: More than 80% of dose was reduced by use of the ROI filter. The reduction rate depended on the thickness of the filter and the size of ROI aperture. The image noise outside the ROI was remarkably reduced by the bilateral filtering technique. The computation time for processing each frame image was reduced from 3.43 seconds with single CPU to 9.85 milliseconds with GPU-acceleration. Conclusion: The proposed technique for X-ray fluoroscopy can substantially reduce imaging radiation dose to the patient while maintaining image quality particularly in the ROI region in real-time.« less

Balance the nodule shape and surroundings: a new multichannel image based convolutional neural network scheme on lung nodule diagnosis

NASA Astrophysics Data System (ADS)

Sun, Wenqing; Zheng, Bin; Huang, Xia; Qian, Wei

2017-03-01

Deep learning is a trending promising method in medical image analysis area, but how to efficiently prepare the input image for the deep learning algorithms remains a challenge. In this paper, we introduced a novel artificial multichannel region of interest (ROI) generation procedure for convolutional neural networks (CNN). From LIDC database, we collected 54880 benign nodule samples and 59848 malignant nodule samples based on the radiologists' annotations. The proposed CNN consists of three pairs of convolutional layers and two fully connected layers. For each original ROI, two new ROIs were generated: one contains the segmented nodule which highlighted the nodule shape, and the other one contains the gradient of the original ROI which highlighted the textures. By combining the three channel images into a pseudo color ROI, the CNN was trained and tested on the new multichannel ROIs (multichannel ROI II). For the comparison, we generated another type of multichannel image by replacing the gradient image channel with a ROI contains whitened background region (multichannel ROI I). With the 5-fold cross validation evaluation method, the CNN using multichannel ROI II achieved the ROI based area under the curve (AUC) of 0.8823+/-0.0177, compared to the AUC of 0.8484+/-0.0204 generated by the original ROI. By calculating the average of ROI scores from one nodule, the lesion based AUC using multichannel ROI was 0.8793+/-0.0210. By comparing the convolved features maps from CNN using different types of ROIs, it can be noted that multichannel ROI II contains more accurate nodule shapes and surrounding textures.

Infrared thermography of the pig thorax: an assessment of selected regions of interest by computed tomographical and anatomical parameters.

PubMed

Menzel, A; Siewert, C; Gasse, H; Seifert, H; Hoeltig, D; Hennig-Pauka, I

2015-04-01

Current methods of diagnosis of respiratory diseases in swine are invasive, time-consuming and expensive. Infrared thermography (IRT) of the thorax might provide a new method of high specificity to select swine affected with lung alterations for further diagnostics. In this study, layer thickness of different tissues was determined in frozen thorax slices (FTS) by computed tomography (CT) and then related to skin temperatures measured by IRT in healthy pigs. The aim was to determine appropriate regions of interest (ROI) for evaluation of IRT images. Organ layer thicknesses measured in CT images correspond to those measured in FTS. Temperature differences between lung ROIs and abdomen ROIs were positively correlated with lung layer thickness at certain localizations, and negatively correlated with the thickness of the thorax wall and of inner organ layers. Reference values of differences between skin temperatures were established for two ROIs on the thorax with potential practical use for lung health status determination. Respective ROIs were located on vertical lines crossing the 7th (right) and the 10th (left) thoracic vertebrae. The presence of ribs affected skin temperature significantly. © 2014 Blackwell Verlag GmbH.

Chaotic Image Encryption of Regions of Interest

NASA Astrophysics Data System (ADS)

Xiao, Di; Fu, Qingqing; Xiang, Tao; Zhang, Yushu

Since different regions of an image have different importance, therefore only the important information of the image regions, which the users are really interested in, needs to be encrypted and protected emphatically in some special multimedia applications. However, the regions of interest (ROI) are always some irregular parts, such as the face and the eyes. Assuming the bulk data in transmission without being damaged, we propose a chaotic image encryption algorithm for ROI. ROI with irregular shapes are chosen and detected arbitrarily. Then the chaos-based image encryption algorithm with scrambling, S-box and diffusion parts is used to encrypt the ROI. Further, the whole image is compressed with Huffman coding. At last, a message authentication code (MAC) of the compressed image is generated based on chaotic maps. The simulation results show that the encryption algorithm has a good security level and can resist various attacks. Moreover, the compression method improves the storage and transmission efficiency to some extent, and the MAC ensures the integrity of the transmission data.

A multilevel-ROI-features-based machine learning method for detection of morphometric biomarkers in Parkinson's disease.

PubMed

Peng, Bo; Wang, Suhong; Zhou, Zhiyong; Liu, Yan; Tong, Baotong; Zhang, Tao; Dai, Yakang

2017-06-09

Machine learning methods have been widely used in recent years for detection of neuroimaging biomarkers in regions of interest (ROIs) and assisting diagnosis of neurodegenerative diseases. The innovation of this study is to use multilevel-ROI-features-based machine learning method to detect sensitive morphometric biomarkers in Parkinson's disease (PD). Specifically, the low-level ROI features (gray matter volume, cortical thickness, etc.) and high-level correlative features (connectivity between ROIs) are integrated to construct the multilevel ROI features. Filter- and wrapper- based feature selection method and multi-kernel support vector machine (SVM) are used in the classification algorithm. T1-weighted brain magnetic resonance (MR) images of 69 PD patients and 103 normal controls from the Parkinson's Progression Markers Initiative (PPMI) dataset are included in the study. The machine learning method performs well in classification between PD patients and normal controls with an accuracy of 85.78%, a specificity of 87.79%, and a sensitivity of 87.64%. The most sensitive biomarkers between PD patients and normal controls are mainly distributed in frontal lobe, parental lobe, limbic lobe, temporal lobe, and central region. The classification performance of our method with multilevel ROI features is significantly improved comparing with other classification methods using single-level features. The proposed method shows promising identification ability for detecting morphometric biomarkers in PD, thus confirming the potentiality of our method in assisting diagnosis of the disease. Copyright © 2017 Elsevier B.V. All rights reserved.

Region of interest processing for iterative reconstruction in x-ray computed tomography

NASA Astrophysics Data System (ADS)

Kopp, Felix K.; Nasirudin, Radin A.; Mei, Kai; Fehringer, Andreas; Pfeiffer, Franz; Rummeny, Ernst J.; Noël, Peter B.

2015-03-01

The recent advancements in the graphics card technology raised the performance of parallel computing and contributed to the introduction of iterative reconstruction methods for x-ray computed tomography in clinical CT scanners. Iterative maximum likelihood (ML) based reconstruction methods are known to reduce image noise and to improve the diagnostic quality of low-dose CT. However, iterative reconstruction of a region of interest (ROI), especially ML based, is challenging. But for some clinical procedures, like cardiac CT, only a ROI is needed for diagnostics. A high-resolution reconstruction of the full field of view (FOV) consumes unnecessary computation effort that results in a slower reconstruction than clinically acceptable. In this work, we present an extension and evaluation of an existing ROI processing algorithm. Especially improvements for the equalization between regions inside and outside of a ROI are proposed. The evaluation was done on data collected from a clinical CT scanner. The performance of the different algorithms is qualitatively and quantitatively assessed. Our solution to the ROI problem provides an increase in signal-to-noise ratio and leads to visually less noise in the final reconstruction. The reconstruction speed of our technique was observed to be comparable with other previous proposed techniques. The development of ROI processing algorithms in combination with iterative reconstruction will provide higher diagnostic quality in the near future.

Training sample selection based on self-training for liver cirrhosis classification using ultrasound images

NASA Astrophysics Data System (ADS)

Fujita, Yusuke; Mitani, Yoshihiro; Hamamoto, Yoshihiko; Segawa, Makoto; Terai, Shuji; Sakaida, Isao

2017-03-01

Ultrasound imaging is a popular and non-invasive tool used in the diagnoses of liver disease. Cirrhosis is a chronic liver disease and it can advance to liver cancer. Early detection and appropriate treatment are crucial to prevent liver cancer. However, ultrasound image analysis is very challenging, because of the low signal-to-noise ratio of ultrasound images. To achieve the higher classification performance, selection of training regions of interest (ROIs) is very important that effect to classification accuracy. The purpose of our study is cirrhosis detection with high accuracy using liver ultrasound images. In our previous works, training ROI selection by MILBoost and multiple-ROI classification based on the product rule had been proposed, to achieve high classification performance. In this article, we propose self-training method to select training ROIs effectively. Evaluation experiments were performed to evaluate effect of self-training, using manually selected ROIs and also automatically selected ROIs. Experimental results show that self-training for manually selected ROIs achieved higher classification performance than other approaches, including our conventional methods. The manually ROI definition and sample selection are important to improve classification accuracy in cirrhosis detection using ultrasound images.

Assessing the skeletal age from a hand radiograph: automating the Tanner-Whitehouse method

NASA Astrophysics Data System (ADS)

Niemeijer, Meindert; van Ginneken, Bram; Maas, Casper A.; Beek, Frederik J. A.; Viergever, Max A.

2003-05-01

The skeletal maturity of children is usually assessed from a standard radiograph of the left hand and wrist. An established clinical method to determine the skeletal maturity is the Tanner-Whitehouse (TW2) method. This method divides the skeletal development into several stages (labelled A, B, ...,I). We are developing an automated system based on this method. In this work we focus on assigning a stage to one region of interest (ROI), the middle phalanx of the third finger. We classify each ROI as follows. A number of ROIs which have been assigned a certain stage by a radiologist are used to construct a mean image for that stage. For a new input ROI, landmarks are detected by using an Active Shape Model. These are used to align the mean images with the input image. Subsequently the correlation between each transformed mean stage image and the input is calculated. The input ROI can be assigned to the stage with the highest correlation directly, or the values can be used as features in a classifier. The method was tested on 71 cases ranging from stage E to I. The ROI was staged correctly in 73.2% of all cases and in 97.2% of all incorrectly staged cases the error was not more than one stage.

Region-of-interest image reconstruction in circular cone-beam microCT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cho, Seungryong; Bian, Junguo; Pelizzari, Charles A.

2007-12-15

Cone-beam microcomputed tomography (microCT) is one of the most popular choices for small animal imaging which is becoming an important tool for studying animal models with transplanted diseases. Region-of-interest (ROI) imaging techniques in CT, which can reconstruct an ROI image from the projection data set of the ROI, can be used not only for reducing imaging-radiation exposure to the subject and scatters to the detector but also for potentially increasing spatial resolution of the reconstructed images. Increasing spatial resolution in microCT images can facilitate improved accuracy in many assessment tasks. A method proposed previously for increasing CT image spatial resolutionmore » entails the exploitation of the geometric magnification in cone-beam CT. Due to finite detector size, however, this method can lead to data truncation for a large geometric magnification. The Feldkamp-Davis-Kress (FDK) algorithm yields images with artifacts when truncated data are used, whereas the recently developed backprojection filtration (BPF) algorithm is capable of reconstructing ROI images without truncation artifacts from truncated cone-beam data. We apply the BPF algorithm to reconstructing ROI images from truncated data of three different objects acquired by our circular cone-beam microCT system. Reconstructed images by use of the FDK and BPF algorithms from both truncated and nontruncated cone-beam data are compared. The results of the experimental studies demonstrate that, from certain truncated data, the BPF algorithm can reconstruct ROI images with quality comparable to that reconstructed from nontruncated data. In contrast, the FDK algorithm yields ROI images with truncation artifacts. Therefore, an implication of the studies is that, when truncated data are acquired with a configuration of a large geometric magnification, the BPF algorithm can be used for effective enhancement of the spatial resolution of a ROI image.« less

Joint penalized-likelihood reconstruction of time-activity curves and regions-of-interest from projection data in brain PET

NASA Astrophysics Data System (ADS)

Krestyannikov, E.; Tohka, J.; Ruotsalainen, U.

2008-06-01

This paper presents a novel statistical approach for joint estimation of regions-of-interest (ROIs) and the corresponding time-activity curves (TACs) from dynamic positron emission tomography (PET) brain projection data. It is based on optimizing the joint objective function that consists of a data log-likelihood term and two penalty terms reflecting the available a priori information about the human brain anatomy. The developed local optimization strategy iteratively updates both the ROI and TAC parameters and is guaranteed to monotonically increase the objective function. The quantitative evaluation of the algorithm is performed with numerically and Monte Carlo-simulated dynamic PET brain data of the 11C-Raclopride and 18F-FDG tracers. The results demonstrate that the method outperforms the existing sequential ROI quantification approaches in terms of accuracy, and can noticeably reduce the errors in TACs arising due to the finite spatial resolution and ROI delineation.

A block-based JPEG-LS compression technique with lossless region of interest

NASA Astrophysics Data System (ADS)

Deng, Lihua; Huang, Zhenghua; Yao, Shoukui

2018-03-01

JPEG-LS lossless compression algorithm is used in many specialized applications that emphasize on the attainment of high fidelity for its lower complexity and better compression ratios than the lossless JPEG standard. But it cannot prevent error diffusion because of the context dependence of the algorithm, and have low compression rate when compared to lossy compression. In this paper, we firstly divide the image into two parts: ROI regions and non-ROI regions. Then we adopt a block-based image compression technique to decrease the range of error diffusion. We provide JPEG-LS lossless compression for the image blocks which include the whole or part region of interest (ROI) and JPEG-LS near lossless compression for the image blocks which are included in the non-ROI (unimportant) regions. Finally, a set of experiments are designed to assess the effectiveness of the proposed compression method.

Examining Brain Morphometry Associated with Self-Esteem in Young Adults Using Multilevel-ROI-Features-Based Classification Method

PubMed Central

Peng, Bo; Lu, Jieru; Saxena, Aditya; Zhou, Zhiyong; Zhang, Tao; Wang, Suhong; Dai, Yakang

2017-01-01

Purpose: This study is to exam self-esteem related brain morphometry on brain magnetic resonance (MR) images using multilevel-features-based classification method. Method: The multilevel region of interest (ROI) features consist of two types of features: (i) ROI features, which include gray matter volume, white matter volume, cerebrospinal fluid volume, cortical thickness, and cortical surface area, and (ii) similarity features, which are based on similarity calculation of cortical thickness between ROIs. For each feature type, a hybrid feature selection method, comprising of filter-based and wrapper-based algorithms, is used to select the most discriminating features. ROI features and similarity features are integrated by using multi-kernel support vector machines (SVMs) with appropriate weighting factor. Results: The classification performance is improved by using multilevel ROI features with an accuracy of 96.66%, a specificity of 96.62%, and a sensitivity of 95.67%. The most discriminating ROI features that are related to self-esteem spread over occipital lobe, frontal lobe, parietal lobe, limbic lobe, temporal lobe, and central region, mainly involving white matter and cortical thickness. The most discriminating similarity features are distributed in both the right and left hemisphere, including frontal lobe, occipital lobe, limbic lobe, parietal lobe, and central region, which conveys information of structural connections between different brain regions. Conclusion: By using ROI features and similarity features to exam self-esteem related brain morphometry, this paper provides a pilot evidence that self-esteem is linked to specific ROIs and structural connections between different brain regions. PMID:28588470

Examining Brain Morphometry Associated with Self-Esteem in Young Adults Using Multilevel-ROI-Features-Based Classification Method.

PubMed

Peng, Bo; Lu, Jieru; Saxena, Aditya; Zhou, Zhiyong; Zhang, Tao; Wang, Suhong; Dai, Yakang

2017-01-01

Purpose: This study is to exam self-esteem related brain morphometry on brain magnetic resonance (MR) images using multilevel-features-based classification method. Method: The multilevel region of interest (ROI) features consist of two types of features: (i) ROI features, which include gray matter volume, white matter volume, cerebrospinal fluid volume, cortical thickness, and cortical surface area, and (ii) similarity features, which are based on similarity calculation of cortical thickness between ROIs. For each feature type, a hybrid feature selection method, comprising of filter-based and wrapper-based algorithms, is used to select the most discriminating features. ROI features and similarity features are integrated by using multi-kernel support vector machines (SVMs) with appropriate weighting factor. Results: The classification performance is improved by using multilevel ROI features with an accuracy of 96.66%, a specificity of 96.62%, and a sensitivity of 95.67%. The most discriminating ROI features that are related to self-esteem spread over occipital lobe, frontal lobe, parietal lobe, limbic lobe, temporal lobe, and central region, mainly involving white matter and cortical thickness. The most discriminating similarity features are distributed in both the right and left hemisphere, including frontal lobe, occipital lobe, limbic lobe, parietal lobe, and central region, which conveys information of structural connections between different brain regions. Conclusion: By using ROI features and similarity features to exam self-esteem related brain morphometry, this paper provides a pilot evidence that self-esteem is linked to specific ROIs and structural connections between different brain regions.

Annotating image ROIs with text descriptions for multimodal biomedical document retrieval

NASA Astrophysics Data System (ADS)

You, Daekeun; Simpson, Matthew; Antani, Sameer; Demner-Fushman, Dina; Thoma, George R.

2013-01-01

Regions of interest (ROIs) that are pointed to by overlaid markers (arrows, asterisks, etc.) in biomedical images are expected to contain more important and relevant information than other regions for biomedical article indexing and retrieval. We have developed several algorithms that localize and extract the ROIs by recognizing markers on images. Cropped ROIs then need to be annotated with contents describing them best. In most cases accurate textual descriptions of the ROIs can be found from figure captions, and these need to be combined with image ROIs for annotation. The annotated ROIs can then be used to, for example, train classifiers that separate ROIs into known categories (medical concepts), or to build visual ontologies, for indexing and retrieval of biomedical articles. We propose an algorithm that pairs visual and textual ROIs that are extracted from images and figure captions, respectively. This algorithm based on dynamic time warping (DTW) clusters recognized pointers into groups, each of which contains pointers with identical visual properties (shape, size, color, etc.). Then a rule-based matching algorithm finds the best matching group for each textual ROI mention. Our method yields a precision and recall of 96% and 79%, respectively, when ground truth textual ROI data is used.

Sulcal depth-based cortical shape analysis in normal healthy control and schizophrenia groups

NASA Astrophysics Data System (ADS)

Lyu, Ilwoo; Kang, Hakmook; Woodward, Neil D.; Landman, Bennett A.

2018-03-01

Sulcal depth is an important marker of brain anatomy in neuroscience/neurological function. Previously, sulcal depth has been explored at the region-of-interest (ROI) level to increase statistical sensitivity to group differences. In this paper, we present a fully automated method that enables inferences of ROI properties from a sulcal region- focused perspective consisting of two main components: 1) sulcal depth computation and 2) sulcal curve-based refined ROIs. In conventional statistical analysis, the average sulcal depth measurements are employed in several ROIs of the cortical surface. However, taking the average sulcal depth over the full ROI blurs overall sulcal depth measurements which may result in reduced sensitivity to detect sulcal depth changes in neurological and psychiatric disorders. To overcome such a blurring effect, we focus on sulcal fundic regions in each ROI by filtering out other gyral regions. Consequently, the proposed method results in more sensitive to group differences than a traditional ROI approach. In the experiment, we focused on a cortical morphological analysis to sulcal depth reduction in schizophrenia with a comparison to the normal healthy control group. We show that the proposed method is more sensitivity to abnormalities of sulcal depth in schizophrenia; sulcal depth is significantly smaller in most cortical lobes in schizophrenia compared to healthy controls (p < 0.05).

A texture analysis method for MR images of airway dilator muscles: a feasibility study

PubMed Central

Järnstedt, J; Sikiö, M; Viik, J; Dastidar, P; Peltomäki, T; Eskola, H

2014-01-01

Objectives: Airway dilator muscles play an important role in the analysis of breathing-related symptoms, such as obstructive sleep apnoea. Texture analysis (TA) provides a new non-invasive method for analysing airway dilator muscles. In this study, we propose a TA methodology for airway dilator muscles and prove the robustness of this method. Methods: 15 orthognathic surgery patients underwent 3-T MRI. Computerized TA was performed on 20 regions of interest (ROIs) in the patients' airway dilator muscles. 53 texture parameters were calculated for all ROIs. The robustness of the TA method was analysed by altering the locations, sizes and shapes of the ROIs. Results: Our study shows that there is significant difference in TA results as the size or shape of ROI changes. The change of location of the ROI inside the studied muscle does not affect the TA results. Conclusions: The TA method is valid for airway dilator muscles. We propose a methodology in which the number of co-occurrence parameters is reduced by using mean values from four different directions (0°, 45°, 90° and 135°) with pixel spacing of 1 pixel. PMID:24773626

Graphics to H.264 video encoding for 3D scene representation and interaction on mobile devices using region of interest

NASA Astrophysics Data System (ADS)

Le, Minh Tuan; Nguyen, Congdu; Yoon, Dae-Il; Jung, Eun Ku; Jia, Jie; Kim, Hae-Kwang

2007-12-01

In this paper, we propose a method of 3D graphics to video encoding and streaming that are embedded into a remote interactive 3D visualization system for rapidly representing a 3D scene on mobile devices without having to download it from the server. In particular, a 3D graphics to video framework is presented that increases the visual quality of regions of interest (ROI) of the video by performing more bit allocation to ROI during H.264 video encoding. The ROI are identified by projection 3D objects to a 2D plane during rasterization. The system offers users to navigate the 3D scene and interact with objects of interests for querying their descriptions. We developed an adaptive media streaming server that can provide an adaptive video stream in term of object-based quality to the client according to the user's preferences and the variation of network bandwidth. Results show that by doing ROI mode selection, PSNR of test sample slightly change while visual quality of objects increases evidently.

Semantic transcoding of video based on regions of interest

NASA Astrophysics Data System (ADS)

Lim, Jeongyeon; Kim, Munchurl; Kim, Jong-Nam; Kim, Kyeongsoo

2003-06-01

Traditional transcoding on multimedia has been performed from the perspectives of user terminal capabilities such as display sizes and decoding processing power, and network resources such as available network bandwidth and quality of services (QoS) etc. The adaptation (or transcoding) of multimedia contents to given such constraints has been made by frame dropping and resizing of audiovisual, as well as reduction of SNR (Signal-to-Noise Ratio) values by saving the resulting bitrates. Not only such traditional transcoding is performed from the perspective of user"s environment, but also we incorporate a method of semantic transcoding of audiovisual based on region of interest (ROI) from user"s perspective. Users can designate their interested parts in images or video so that the corresponding video contents can be adapted focused on the user"s ROI. We incorporate the MPEG-21 DIA (Digital Item Adaptation) framework in which such semantic information of the user"s ROI is represented and delivered to the content provider side as XDI (context digital item). Representation schema of our semantic information of the user"s ROI has been adopted in MPEG-21 DIA Adaptation Model. In this paper, we present the usage of semantic information of user"s ROI for transcoding and show our system implementation with experimental results.

Region-of-interest determination and bit-rate conversion for H.264 video transcoding

NASA Astrophysics Data System (ADS)

Huang, Shu-Fen; Chen, Mei-Juan; Tai, Kuang-Han; Li, Mian-Shiuan

2013-12-01

This paper presents a video bit-rate transcoder for baseline profile in H.264/AVC standard to fit the available channel bandwidth for the client when transmitting video bit-streams via communication channels. To maintain visual quality for low bit-rate video efficiently, this study analyzes the decoded information in the transcoder and proposes a Bayesian theorem-based region-of-interest (ROI) determination algorithm. In addition, a curve fitting scheme is employed to find the models of video bit-rate conversion. The transcoded video will conform to the target bit-rate by re-quantization according to our proposed models. After integrating the ROI detection method and the bit-rate transcoding models, the ROI-based transcoder allocates more coding bits to ROI regions and reduces the complexity of the re-encoding procedure for non-ROI regions. Hence, it not only keeps the coding quality but improves the efficiency of the video transcoding for low target bit-rates and makes the real-time transcoding more practical. Experimental results show that the proposed framework gets significantly better visual quality.

SU-E-I-96: A Study About the Influence of ROI Variation On Tumor Segmentation in PET

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, L; Tan, S; Lu, W

2014-06-01

Purpose: To study the influence of different regions of interest (ROI) on tumor segmentation in PET. Methods: The experiments were conducted on a cylindrical phantom. Six spheres with different volumes (0.5ml, 1ml, 6ml, 12ml, 16ml and 20 ml) were placed inside a cylindrical container to mimic tumors of different sizes. The spheres were filled with 11C solution as sources and the cylindrical container was filled with 18F-FDG solution as the background. The phantom was continuously scanned in a Biograph-40 True Point/True View PET/CT scanner, and 42 images were reconstructed with source-to-background ratio (SBR) ranging from 16:1 to 1.8:1. We tookmore » a large and a small ROI for each sphere, both of which contain the whole sphere and does not contain any other spheres. Six other ROIs of different sizes were then taken between the large and the small ROI. For each ROI, all images were segmented by eitht thresholding methods and eight advanced methods, respectively. The segmentation results were evaluated by dice similarity index (DSI), classification error (CE) and volume error (VE). The robustness of different methods to ROI variation was quantified using the interrun variation and a generalized Cohen's kappa. Results: With the change of ROI, the segmentation results of all tested methods changed more or less. Compared with all advanced methods, thresholding methods were less affected by the ROI change. In addition, most of the thresholding methods got more accurate segmentation results for all sphere sizes. Conclusion: The results showed that the segmentation performance of all tested methods was affected by the change of ROI. Thresholding methods were more robust to this change and they can segment the PET image more accurately. This work was supported in part by National Natural Science Foundation of China (NNSFC), under Grant Nos. 60971112 and 61375018, and Fundamental Research Funds for the Central Universities, under Grant No. 2012QN086. Wei Lu was supported in part by the National Institutes of Health (NIH) Grant No. R01 CA172638.« less

Use of spectral analysis with iterative filter for voxelwise determination of regional rates of cerebral protein synthesis with L-[1-11C]leucine PET.

PubMed

Veronese, Mattia; Schmidt, Kathleen C; Smith, Carolyn Beebe; Bertoldo, Alessandra

2012-06-01

A spectral analysis approach was used to estimate kinetic parameters of the L-[1-(11)C]leucine positron emission tomography (PET) method and regional rates of cerebral protein synthesis (rCPS) on a voxel-by-voxel basis. Spectral analysis applies to both heterogeneous and homogeneous tissues; it does not require prior assumptions concerning number of tissue compartments. Parameters estimated with spectral analysis can be strongly affected by noise, but numerical filters improve estimation performance. Spectral analysis with iterative filter (SAIF) was originally developed to improve estimation of leucine kinetic parameters and rCPS in region-of-interest (ROI) data analyses. In the present study, we optimized SAIF for application at the voxel level. In measured L-[1-(11)C]leucine PET data, voxel-level SAIF parameter estimates averaged over all voxels within a ROI (mean voxel-SAIF) generally agreed well with corresponding estimates derived by applying the originally developed SAIF to ROI time-activity curves (ROI-SAIF). Region-of-interest-SAIF and mean voxel-SAIF estimates of rCPS were highly correlated. Simulations showed that mean voxel-SAIF rCPS estimates were less biased and less variable than ROI-SAIF estimates in the whole brain and cortex; biases were similar in white matter. We conclude that estimation of rCPS with SAIF is improved when the method is applied at voxel level than in ROI analysis.

Methodological considerations in region of interest definitions for paraspinal muscles in axial MRIs of the lumbar spine.

PubMed

Berry, David B; Padwal, Jennifer; Johnson, Seth; Parra, Callan L; Ward, Samuel R; Shahidi, Bahar

2018-05-07

Magnetic Resonance Imaging (MRI) is commonly used to assess the health of the lumbar spine and supporting structures. Studies have suggested that fatty infiltration of the posterior lumbar muscles is important in predicting responses to treatment for low back pain. However, methodological differences exist in defining the region of interest (ROI) of a muscle, which limits the ability to compare data between studies. The purpose of this study was to determine reliability and systematic differences within and between two commonly utilized methodologies for ROI definitions of lumbar paraspinal muscle. T2-weighted MRIs of the mid-L4 vertebrae from 37 patients with low back pain who were scheduled for lumbar spine surgery were included from a hospital database. Fatty infiltration for these patients ranged from low to high, based on Kjaer criteria. Two methods were used to define ROI: 1) segmentation of the multifidus and erector spinae based on fascial planes including epimuscular fat, and 2) segmentation of the multifidus and erector spinae based on visible muscle boundaries, which did not include epimuscular fat. Total cross sectional area (tCSA), fat signal fraction (FSF), muscle cross sectional area, and fat cross sectional area were measured. Degree of agreement between raters for each parameter was assessed using intra-class correlation coefficients (ICC) and area fraction of overlapping voxels. Excellent inter-rater agreement (ICC > 0.75) was observed for all measures for both methods. There was no significant difference between area fraction overlap of ROIs between methods. Method 1 demonstrated a greater tCSA for both the erector spinae (14-15%, p < 0.001) and multifidus (4%, p < 0.016) but a greater FSF only for the erector spinae (11-13%, p < 0.001). The two methods of defining lumbar spine muscle ROIs demonstrated excellent inter-rater reliability, although significant differences exist as method 1 showed larger CSA and FSF values compared to method 2. The results of this study confirm the validity of using either method to measure lumbar paraspinal musculature, and that method should be selected based on the primary outcome variables of interest.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ranganathan, V; Kumar, P; Bzdusek, K

Purpose: We propose a novel data-driven method to predict the achievability of clinical objectives upfront before invoking the IMRT optimization. Methods: A new metric called “Geometric Complexity (GC)” is used to estimate the achievability of clinical objectives. Here, GC is the measure of the number of “unmodulated” beamlets or rays that intersect the Region-of-interest (ROI) and the target volume. We first compute the geometric complexity ratio (GCratio) between the GC of a ROI (say, parotid) in a reference plan and the GC of the same ROI in a given plan. The GCratio of a ROI indicates the relative geometric complexitymore » of the ROI as compared to the same ROI in the reference plan. Hence GCratio can be used to predict if a defined clinical objective associated with the ROI can be met by the optimizer for a given case. Basically a higher GCratio indicates a lesser likelihood for the optimizer to achieve the clinical objective defined for a given ROI. Similarly, a lower GCratio indicates a higher likelihood for the optimizer to achieve the clinical objective defined for the given ROI. We have evaluated the proposed method on four Head and Neck cases using Pinnacle3 (version 9.10.0) Treatment Planning System (TPS). Results: Out of the total of 28 clinical objectives from four head and neck cases included in the study, 25 were in agreement with the prediction, which implies an agreement of about 85% between predicted and obtained results. The Pearson correlation test shows a positive correlation between predicted and obtained results (Correlation = 0.82, r2 = 0.64, p < 0.005). Conclusion: The study demonstrates the feasibility of the proposed method in head and neck cases for predicting the achievability of clinical objectives with reasonable accuracy.« less

SIMA: Python software for analysis of dynamic fluorescence imaging data.

PubMed

Kaifosh, Patrick; Zaremba, Jeffrey D; Danielson, Nathan B; Losonczy, Attila

2014-01-01

Fluorescence imaging is a powerful method for monitoring dynamic signals in the nervous system. However, analysis of dynamic fluorescence imaging data remains burdensome, in part due to the shortage of available software tools. To address this need, we have developed SIMA, an open source Python package that facilitates common analysis tasks related to fluorescence imaging. Functionality of this package includes correction of motion artifacts occurring during in vivo imaging with laser-scanning microscopy, segmentation of imaged fields into regions of interest (ROIs), and extraction of signals from the segmented ROIs. We have also developed a graphical user interface (GUI) for manual editing of the automatically segmented ROIs and automated registration of ROIs across multiple imaging datasets. This software has been designed with flexibility in mind to allow for future extension with different analysis methods and potential integration with other packages. Software, documentation, and source code for the SIMA package and ROI Buddy GUI are freely available at http://www.losonczylab.org/sima/.

Reproducibility of CT bone densitometry: operator versus automated ROI definition.

PubMed

Louis, O; Luypaert, R; Kalender, W; Osteaux, M

1988-05-01

Intrasubject reproducibility with repeated determination of vertebral mineral density from a given set of CT images was investigated. The region of interest (ROI) in 10 patient scans was selected by four independent operators either manually or with an automated procedure separating cortical and spongeous bone, the operators being requested to interact in ROI selection. The mean intrasubject variation was found to be much lower with the automated process (0.3 to 0.6%) than with the conventional method (2.5 to 5.2%). In a second study, 10 patients were examined twice to determine the reproducibility of CT slice selection by the operator. The errors were of the same order of magnitude as in ROI selection.

A new efficient method for color image compression based on visual attention mechanism

NASA Astrophysics Data System (ADS)

Shao, Xiaoguang; Gao, Kun; Lv, Lily; Ni, Guoqiang

2010-11-01

One of the key procedures in color image compression is to extract its region of interests (ROIs) and evaluate different compression ratios. A new non-uniform color image compression algorithm with high efficiency is proposed in this paper by using a biology-motivated selective attention model for the effective extraction of ROIs in natural images. When the ROIs have been extracted and labeled in the image, the subsequent work is to encode the ROIs and other regions with different compression ratios via popular JPEG algorithm. Furthermore, experiment results and quantitative and qualitative analysis in the paper show perfect performance when comparing with other traditional color image compression approaches.

Diffusion-weighted imaging of breast lesions: Region-of-interest placement and different ADC parameters influence apparent diffusion coefficient values.

PubMed

Bickel, Hubert; Pinker, Katja; Polanec, Stephan; Magometschnigg, Heinrich; Wengert, Georg; Spick, Claudio; Bogner, Wolfgang; Bago-Horvath, Zsuzsanna; Helbich, Thomas H; Baltzer, Pascal

2017-05-01

To investigate the influence of region-of-interest (ROI) placement and different apparent diffusion coefficient (ADC) parameters on ADC values, diagnostic performance, reproducibility and measurement time in breast tumours. In this IRB-approved, retrospective study, 149 histopathologically proven breast tumours (109 malignant, 40 benign) in 147 women (mean age 53.2) were investigated. Three radiologists independently measured minimum, mean and maximum ADC, each using three ROI placement approaches:1 - small 2D-ROI, 2 - large 2D-ROI and 3 - 3D-ROI covering the whole lesion. One reader performed all measurements twice. Median ADC values, diagnostic performance, reproducibility, and measurement time were calculated and compared between all combinations of ROI placement approaches and ADC parameters. Median ADC values differed significantly between the ROI placement approaches (p < .001). Minimum ADC showed the best diagnostic performance (AUC .928-.956), followed by mean ADC obtained from 2D ROIs (.926-.94). Minimum and mean ADC showed high intra- (ICC .85-.94) and inter-reader reproducibility (ICC .74-.94). Median measurement time was significantly shorter for the 2D ROIs (p < .001). ROI placement significantly influences ADC values measured in breast tumours. Minimum and mean ADC acquired from 2D-ROIs are useful for the differentiation of benign and malignant breast lesions, and are highly reproducible, with rapid measurement. • Region of interest placement significantly influences apparent diffusion coefficient of breast tumours. • Minimum and mean apparent diffusion coefficient perform best and are reproducible. • 2D regions of interest perform best and provide rapid measurement times.

A diffusion tensor imaging study of suicide attempters

PubMed Central

Thapa-Chhetry, Binod; Sublette, M. Elizabeth; Sullivan, Gregory M.; Oquendo, Maria A.; Mann, J. John; Parsey, Ramin V.

2014-01-01

Background Few studies have examined white matter abnormalities in suicide attempters using diffusion tensor imaging (DTI). This study sought to identify white matter regions altered in individuals with a prior suicide attempt. Methods DTI scans were acquired in 13 suicide attempters with major depressive disorder (MDD), 39 non-attempters with MDD, and 46 healthy participants (HP). Fractional anisotropy (FA) and apparent diffusion coefficient (ADC) was determined in the brain using two methods: region of interest (ROI) and tract-based spatial statistics (TBSS). ROIs were limited a priori to white matter adjacent to the caudal anterior cingulate cortex, rostral anterior cingulate cortex, dorsomedial prefrontal cortex, and medial orbitofrontal cortex. Results Using the ROI approach, suicide attempters had lower FA than MDD non-attempters and HP in the dorsomedial prefrontal cortex. Uncorrected TBSS results confirmed a significant cluster within the right dorsomedial prefrontal cortex indicating lower FA in suicide attempters compared to non-attempters. There were no differences in ADC when comparing suicide attempters, non-attempters and HP groups using ROI or TBSS methods. Conclusions Low FA in the dorsomedial prefrontal cortex was associated with a suicide attempt history. Converging findings from other imaging modalities support this finding, making this region of potential interest in determining the diathesis for suicidal behavior. PMID:24462041

Dynamic intensity-weighted region of interest imaging for conebeam CT

PubMed Central

Pearson, Erik; Pan, Xiaochuan; Pelizzari, Charles

2017-01-01

BACKGROUND Patient dose from image guidance in radiotherapy is small compared to the treatment dose. However, the imaging beam is untargeted and deposits dose equally in tumor and healthy tissues. It is desirable to minimize imaging dose while maintaining efficacy. OBJECTIVE Image guidance typically does not require full image quality throughout the patient. Dynamic filtration of the kV beam allows local control of CT image noise for high quality around the target volume and lower quality elsewhere, with substantial dose sparing and reduced scatter fluence on the detector. METHODS The dynamic Intensity-Weighted Region of Interest (dIWROI) technique spatially varies beam intensity during acquisition with copper filter collimation. Fluence is reduced by 95% under the filters with the aperture conformed dynamically to the ROI during cone-beam CT scanning. Preprocessing to account for physical effects of the collimator before reconstruction is described. RESULTS Reconstructions show image quality comparable to a standard scan in the ROI, with higher noise and streak artifacts in the outer region but still adequate quality for patient localization. Monte Carlo modeling shows dose reduction by 10–15% in the ROI due to reduced scatter, and up to 75% outside. CONCLUSIONS The presented technique offers a method to reduce imaging dose by accepting increased image noise outside the ROI, while maintaining full image quality inside the ROI. PMID:27257875

Skeletal maturity determination from hand radiograph by model-based analysis

NASA Astrophysics Data System (ADS)

Vogelsang, Frank; Kohnen, Michael; Schneider, Hansgerd; Weiler, Frank; Kilbinger, Markus W.; Wein, Berthold B.; Guenther, Rolf W.

2000-06-01

Derived from a model based segmentation algorithm for hand radiographs proposed in our former work we now present a method to determine skeletal maturity by an automated analysis of regions of interest (ROI). These ROIs including the epiphyseal and carpal bones, which are most important for skeletal maturity determination, can be extracted out of the radiograph by knowledge based algorithms.

Comparison of Two Methods of Noise Power Spectrum Determinations of Medical Radiography Systems

NASA Astrophysics Data System (ADS)

Hassan, Wan Muhamad Saridan Wan; Ahmed Darwish, Zeki

2011-03-01

Noise in medical images is recognized as an important factor that determines the image quality. Image noise is characterized by noise power spectrum (NPS). We compared two methods of NPS determination namely the methods of Wagner and Dobbins on Lanex Regular TMG screen-film system and Hologic Lorad Selenia full field digital mammography system, with the aim of choosing the better method to use. The methods differ in terms of various parametric choices and algorithm implementations. These parameters include the low pass filtering, low frequency filtering, windowing, smoothing, aperture correction, overlapping of region of interest (ROI), length of fast Fourier transform, ROI size, method of ROI normalization, and slice selection of the NPS. Overall, the two methods agreed to the practical value of noise power spectrum between 10-3-10-6 mm2 over spatial frequency range 0-10 mm-1.

Medical image retrieval system using multiple features from 3D ROIs

NASA Astrophysics Data System (ADS)

Lu, Hongbing; Wang, Weiwei; Liao, Qimei; Zhang, Guopeng; Zhou, Zhiming

2012-02-01

Compared to a retrieval using global image features, features extracted from regions of interest (ROIs) that reflect distribution patterns of abnormalities would benefit more for content-based medical image retrieval (CBMIR) systems. Currently, most CBMIR systems have been designed for 2D ROIs, which cannot reflect 3D anatomical features and region distribution of lesions comprehensively. To further improve the accuracy of image retrieval, we proposed a retrieval method with 3D features including both geometric features such as Shape Index (SI) and Curvedness (CV) and texture features derived from 3D Gray Level Co-occurrence Matrix, which were extracted from 3D ROIs, based on our previous 2D medical images retrieval system. The system was evaluated with 20 volume CT datasets for colon polyp detection. Preliminary experiments indicated that the integration of morphological features with texture features could improve retrieval performance greatly. The retrieval result using features extracted from 3D ROIs accorded better with the diagnosis from optical colonoscopy than that based on features from 2D ROIs. With the test database of images, the average accuracy rate for 3D retrieval method was 76.6%, indicating its potential value in clinical application.

Least-squares dual characterization for ROI assessment in emission tomography

NASA Astrophysics Data System (ADS)

Ben Bouallègue, F.; Crouzet, J. F.; Dubois, A.; Buvat, I.; Mariano-Goulart, D.

2013-06-01

Our aim is to describe an original method for estimating the statistical properties of regions of interest (ROIs) in emission tomography. Drawn upon the works of Louis on the approximate inverse, we propose a dual formulation of the ROI estimation problem to derive the ROI activity and variance directly from the measured data without any image reconstruction. The method requires the definition of an ROI characteristic function that can be extracted from a co-registered morphological image. This characteristic function can be smoothed to optimize the resolution-variance tradeoff. An iterative procedure is detailed for the solution of the dual problem in the least-squares sense (least-squares dual (LSD) characterization), and a linear extrapolation scheme is described to compensate for sampling partial volume effect and reduce the estimation bias (LSD-ex). LSD and LSD-ex are compared with classical ROI estimation using pixel summation after image reconstruction and with Huesman's method. For this comparison, we used Monte Carlo simulations (GATE simulation tool) of 2D PET data of a Hoffman brain phantom containing three small uniform high-contrast ROIs and a large non-uniform low-contrast ROI. Our results show that the performances of LSD characterization are at least as good as those of the classical methods in terms of root mean square (RMS) error. For the three small tumor regions, LSD-ex allows a reduction in the estimation bias by up to 14%, resulting in a reduction in the RMS error of up to 8.5%, compared with the optimal classical estimation. For the large non-specific region, LSD using appropriate smoothing could intuitively and efficiently handle the resolution-variance tradeoff.

Effective Diagnosis of Alzheimer's Disease by Means of Association Rules

NASA Astrophysics Data System (ADS)

Chaves, R.; Ramírez, J.; Górriz, J. M.; López, M.; Salas-Gonzalez, D.; Illán, I.; Segovia, F.; Padilla, P.

In this paper we present a novel classification method of SPECT images for the early diagnosis of the Alzheimer's disease (AD). The proposed method is based on Association Rules (ARs) aiming to discover interesting associations between attributes contained in the database. The system uses firstly voxel-as-features (VAF) and Activation Estimation (AE) to find tridimensional activated brain regions of interest (ROIs) for each patient. These ROIs act as inputs to secondly mining ARs between activated blocks for controls, with a specified minimum support and minimum confidence. ARs are mined in supervised mode, using information previously extracted from the most discriminant rules for centering interest in the relevant brain areas, reducing the computational requirement of the system. Finally classification process is performed depending on the number of previously mined rules verified by each subject, yielding an up to 95.87% classification accuracy, thus outperforming recent developed methods for AD diagnosis.

SU-E-J-32: Dosimetric Evaluation Based On Pre-Treatment Cone Beam CT for Spine Stereotactic Body Radiotherapy: Does Region of Interest Focus Matter?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Magnelli, A; Xia, P

2015-06-15

Purpose: Spine stereotactic body radiotherapy requires very conformal dose distributions and precise delivery. Prior to treatment, a KV cone-beam CT (KV-CBCT) is registered to the planning CT to provide image-guided positional corrections, which depend on selection of the region of interest (ROI) because of imperfect patient positioning and anatomical deformation. Our objective is to determine the dosimetric impact of ROI selections. Methods: Twelve patients were selected for this study with the treatment regions varied from C-spine to T-spine. For each patient, the KV-CBCT was registered to the planning CT three times using distinct ROIs: one encompassing the entire patient, amore » large ROI containing large bony anatomy, and a small target-focused ROI. Each registered CBCT volume, saved as an aligned dataset, was then sent to the planning system. The treated plan was applied to each dataset and dose was recalculated. The tumor dose coverage (percentage of target volume receiving prescription dose), maximum point dose to 0.03 cc of the spinal cord, and dose to 10% of the spinal cord volume (V10) for each alignment were compared to the original plan. Results: The average magnitude of tumor coverage deviation was 3.9%±5.8% with external contour, 1.5%±1.1% with large ROI, 1.3%±1.1% with small ROI. Spinal cord V10 deviation from plan was 6.6%±6.6% with external contour, 3.5%±3.1% with large ROI, and 1.2%±1.0% with small ROI. Spinal cord max point dose deviation from plan was: 12.2%±13.3% with external contour, 8.5%±8.4% with large ROI, and 3.7%±2.8% with small ROI. Conclusion: A small ROI focused on the target results in the smallest deviation from planned dose to target and cord although rotations at large distances from the targets were observed. It is recommended that image fusion during CBCT focus narrowly on the target volume to minimize dosimetric error. Improvement in patient setups may further reduce residual errors.« less

Testing of Haar-Like Feature in Region of Interest Detection for Automated Target Recognition (ATR) System

NASA Technical Reports Server (NTRS)

Zhang, Yuhan; Lu, Dr. Thomas

2010-01-01

The objectives of this project were to develop a ROI (Region of Interest) detector using Haar-like feature similar to the face detection in Intel's OpenCV library, implement it in Matlab code, and test the performance of the new ROI detector against the existing ROI detector that uses Optimal Trade-off Maximum Average Correlation Height filter (OTMACH). The ROI detector included 3 parts: 1, Automated Haar-like feature selection in finding a small set of the most relevant Haar-like features for detecting ROIs that contained a target. 2, Having the small set of Haar-like features from the last step, a neural network needed to be trained to recognize ROIs with targets by taking the Haar-like features as inputs. 3, using the trained neural network from the last step, a filtering method needed to be developed to process the neural network responses into a small set of regions of interests. This needed to be coded in Matlab. All the 3 parts needed to be coded in Matlab. The parameters in the detector needed to be trained by machine learning and tested with specific datasets. Since OpenCV library and Haar-like feature were not available in Matlab, the Haar-like feature calculation needed to be implemented in Matlab. The codes for Adaptive Boosting and max/min filters in Matlab could to be found from the Internet but needed to be integrated to serve the purpose of this project. The performance of the new detector was tested by comparing the accuracy and the speed of the new detector against the existing OTMACH detector. The speed was referred as the average speed to find the regions of interests in an image. The accuracy was measured by the number of false positives (false alarms) at the same detection rate between the two detectors.

Intrinsic feature-based pose measurement for imaging motion compensation

DOEpatents

Baba, Justin S.; Goddard, Jr., James Samuel

2014-08-19

Systems and methods for generating motion corrected tomographic images are provided. A method includes obtaining first images of a region of interest (ROI) to be imaged and associated with a first time, where the first images are associated with different positions and orientations with respect to the ROI. The method also includes defining an active region in the each of the first images and selecting intrinsic features in each of the first images based on the active region. Second, identifying a portion of the intrinsic features temporally and spatially matching intrinsic features in corresponding ones of second images of the ROI associated with a second time prior to the first time and computing three-dimensional (3D) coordinates for the portion of the intrinsic features. Finally, the method includes computing a relative pose for the first images based on the 3D coordinates.

Selective visual region of interest to enhance medical video conferencing

NASA Astrophysics Data System (ADS)

Bonneau, Walt, Jr.; Read, Christopher J.; Shirali, Girish

1998-06-01

The continued economic pressure that is being placed upon the healthcare industry creates both challenge and opportunity to develop cost effective healthcare tools. Tools that provide improvements in the quality of medical care at the same time improve the distribution of efficient care will create product demand. Video Conferencing systems are one of the latest product technologies that are evolving their way into healthcare applications. The systems that provide quality Bi- directional video and imaging at the lowest system and communication cost are creating many possible options for the healthcare industry. A method to use only 128k bits/sec. of ISDN bandwidth while providing quality video images in selected regions will be applied to echocardiograms using a low cost video conferencing system operating within a basic rate ISDN line bandwidth. Within a given display area (frame) it has been observed that only selected informational areas of the frame of are of value when viewing for detail and precision within an image. Much in the same manner that a photograph is cropped. If a method to accomplish Region Of Interest (ROI) was applied to video conferencing using H.320 with H.263 (compression) and H.281 (camera control) international standards, medical image quality could be achieved in a cost-effective manner. For example, the cardiologist could be provided with a selectable three to eight end-point viewable ROI polygon that defines the ROI in the image. This is achieved by the video system calculating the selected regional end-points and creating an alpha mask to signify the importance of the ROI to the compression processor. This region is then applied to the compression algorithm in a manner that the majority of the video conferencing processor cycles are focused on the ROI of the image. An occasional update of the non-ROI area is processed to maintain total image coherence. The user could control the non-ROI area updates. Providing encoder side ROI specification is of value. However, the power of this capability is improved if remote access and selection of the ROI is also provided. Using the H.281 camera standard and proposing an additional option to the standard to allow for remote ROI selection would make this possible. When ROI is applied the ability to reach the equivalent of 384K bits/sec ISDN rates may be achieved or exceeded depending upon the size of the selected ROI using 128K bits/sec. This opens additional opportunity to establish international calling and reduced call rates by up to sixty- six percent making reoccurring communication costs attractive. Rates of twenty to thirty quality ROI updates could be achieved. It is however important to understand that this technique is still under development.

Analyzing multimodality tomographic images and associated regions of interest with MIDAS

NASA Astrophysics Data System (ADS)

Tsui, Wai-Hon; Rusinek, Henry; Van Gelder, Peter; Lebedev, Sergey

2001-07-01

This paper outlines the design and features incorporated in a software package for analyzing multi-modality tomographic images. The package MIDAS has been evolving for the past 15 years and is in wide use by researchers at New York University School of Medicine and a number of collaborating research sites. It was written in the C language and runs on Sun workstations and Intel PCs under the Solaris operating system. A unique strength of the MIDAS package lies in its ability to generate, manipulate and analyze a practically unlimited number of regions of interest (ROIs). These regions are automatically saved in an efficient data structure and linked to associated images. A wide selection of set theoretical (e.g. union, xor, difference), geometrical (e.g. move, rotate) and morphological (grow, peel) operators can be applied to an arbitrary selection of ROIs. ROIs are constructed as a result of image segmentation algorithms incorporated in MIDAS; they also can be drawn interactively. These ROI editing operations can be applied in either 2D or 3D mode. ROI statistics generated by MIDAS include means, standard deviations, centroids and histograms. Other image manipulation tools incorporated in MIDAS are multimodality and within modality coregistration methods (including landmark matching, surface fitting and Woods' correlation methods) and image reformatting methods (using nearest-neighbor, tri-linear or sinc interpolation). Applications of MIDAS include: (1) neuroanatomy research: marking anatomical structures in one orientation, reformatting marks to another orientation; (2) tissue volume measurements: brain structures (PET, MRI, CT), lung nodules (low dose CT), breast density (MRI); (3) analysis of functional (SPECT, PET) experiments by overlaying corresponding structural scans; (4) longitudinal studies: regional measurement of atrophy.

Quantitative Immunofluorescence Analysis of Nucleolus-Associated Chromatin.

PubMed

Dillinger, Stefan; Németh, Attila

2016-01-01

The nuclear distribution of eu- and heterochromatin is nonrandom, heterogeneous, and dynamic, which is mirrored by specific spatiotemporal arrangements of histone posttranslational modifications (PTMs). Here we describe a semiautomated method for the analysis of histone PTM localization patterns within the mammalian nucleus using confocal laser scanning microscope images of fixed, immunofluorescence stained cells as data source. The ImageJ-based process includes the segmentation of the nucleus, furthermore measurements of total fluorescence intensities, the heterogeneity of the staining, and the frequency of the brightest pixels in the region of interest (ROI). In the presented image analysis pipeline, the perinucleolar chromatin is selected as primary ROI, and the nuclear periphery as secondary ROI.

Reconstructing cone-beam CT with spatially varying qualities for adaptive radiotherapy: a proof-of-principle study.

PubMed

Lu, Wenting; Yan, Hao; Gu, Xuejun; Tian, Zhen; Luo, Ouyang; Yang, Liu; Zhou, Linghong; Cervino, Laura; Wang, Jing; Jiang, Steve; Jia, Xun

2014-10-21

With the aim of maximally reducing imaging dose while meeting requirements for adaptive radiation therapy (ART), we propose in this paper a new cone beam CT (CBCT) acquisition and reconstruction method that delivers images with a low noise level inside a region of interest (ROI) and a relatively high noise level outside the ROI. The acquired projection images include two groups: densely sampled projections at a low exposure with a large field of view (FOV) and sparsely sampled projections at a high exposure with a small FOV corresponding to the ROI. A new algorithm combining the conventional filtered back-projection algorithm and the tight-frame iterative reconstruction algorithm is also designed to reconstruct the CBCT based on these projection data. We have validated our method on a simulated head-and-neck (HN) patient case, a semi-real experiment conducted on a HN cancer patient under a full-fan scan mode, as well as a Catphan phantom under a half-fan scan mode. Relative root-mean-square errors (RRMSEs) of less than 3% for the entire image and ~1% within the ROI compared to the ground truth have been observed. These numbers demonstrate the ability of our proposed method to reconstruct high-quality images inside the ROI. As for the part outside ROI, although the images are relatively noisy, it can still provide sufficient information for radiation dose calculations in ART. Dose distributions calculated on our CBCT image and on a standard CBCT image are in agreement, with a mean relative difference of 0.082% inside the ROI and 0.038% outside the ROI. Compared with the standard clinical CBCT scheme, an imaging dose reduction of approximately 3-6 times inside the ROI was achieved, as well as an 8 times outside the ROI. Regarding computational efficiency, it takes 1-3 min to reconstruct a CBCT image depending on the number of projections used. These results indicate that the proposed method has the potential for application in ART.

People detection method using graphics processing units for a mobile robot with an omnidirectional camera

NASA Astrophysics Data System (ADS)

Kang, Sungil; Roh, Annah; Nam, Bodam; Hong, Hyunki

2011-12-01

This paper presents a novel vision system for people detection using an omnidirectional camera mounted on a mobile robot. In order to determine regions of interest (ROI), we compute a dense optical flow map using graphics processing units, which enable us to examine compliance with the ego-motion of the robot in a dynamic environment. Shape-based classification algorithms are employed to sort ROIs into human beings and nonhumans. The experimental results show that the proposed system detects people more precisely than previous methods.

MRI-determined liver proton density fat fraction, with MRS validation: Comparison of regions of interest sampling methods in patients with type 2 diabetes.

PubMed

Vu, Kim-Nhien; Gilbert, Guillaume; Chalut, Marianne; Chagnon, Miguel; Chartrand, Gabriel; Tang, An

2016-05-01

To assess the agreement between published magnetic resonance imaging (MRI)-based regions of interest (ROI) sampling methods using liver mean proton density fat fraction (PDFF) as the reference standard. This retrospective, internal review board-approved study was conducted in 35 patients with type 2 diabetes. Liver PDFF was measured by magnetic resonance spectroscopy (MRS) using a stimulated-echo acquisition mode sequence and MRI using a multiecho spoiled gradient-recalled echo sequence at 3.0T. ROI sampling methods reported in the literature were reproduced and liver mean PDFF obtained by whole-liver segmentation was used as the reference standard. Intraclass correlation coefficients (ICCs), Bland-Altman analysis, repeated-measures analysis of variance (ANOVA), and paired t-tests were performed. ICC between MRS and MRI-PDFF was 0.916. Bland-Altman analysis showed excellent intermethod agreement with a bias of -1.5 ± 2.8%. The repeated-measures ANOVA found no systematic variation of PDFF among the nine liver segments. The correlation between liver mean PDFF and ROI sampling methods was very good to excellent (0.873 to 0.975). Paired t-tests revealed significant differences (P < 0.05) with ROI sampling methods that exclusively or predominantly sampled the right lobe. Significant correlations with mean PDFF were found with sampling methods that included higher number of segments, total area equal or larger than 5 cm(2) , or sampled both lobes (P = 0.001, 0.023, and 0.002, respectively). MRI-PDFF quantification methods should sample each liver segment in both lobes and include a total surface area equal or larger than 5 cm(2) to provide a close estimate of the liver mean PDFF. © 2015 Wiley Periodicals, Inc.

Dynamic scanpaths: eye movement analysis methods

NASA Astrophysics Data System (ADS)

Blackmon, Theodore T.; Ho, Yeuk F.; Chernyak, Dimitri A.; Azzariti, Michela; Stark, Lawrence W.

1999-05-01

An eye movements sequence, or scanpath, during viewing of a stationary stimulus has been described as a set of fixations onto regions-of-interest, ROIs, and the saccades or transitions between them. Such scanpaths have high similarity for the same subject and stimulus both in the spatial loci of the ROIs and their sequence; scanpaths also take place during recollection of a previously viewed stimulus, suggesting that they play a similar role in visual memory and recall.

Medical Ultrasound Video Coding with H.265/HEVC Based on ROI Extraction

PubMed Central

Wu, Yueying; Liu, Pengyu; Gao, Yuan; Jia, Kebin

2016-01-01

High-efficiency video compression technology is of primary importance to the storage and transmission of digital medical video in modern medical communication systems. To further improve the compression performance of medical ultrasound video, two innovative technologies based on diagnostic region-of-interest (ROI) extraction using the high efficiency video coding (H.265/HEVC) standard are presented in this paper. First, an effective ROI extraction algorithm based on image textural features is proposed to strengthen the applicability of ROI detection results in the H.265/HEVC quad-tree coding structure. Second, a hierarchical coding method based on transform coefficient adjustment and a quantization parameter (QP) selection process is designed to implement the otherness encoding for ROIs and non-ROIs. Experimental results demonstrate that the proposed optimization strategy significantly improves the coding performance by achieving a BD-BR reduction of 13.52% and a BD-PSNR gain of 1.16 dB on average compared to H.265/HEVC (HM15.0). The proposed medical video coding algorithm is expected to satisfy low bit-rate compression requirements for modern medical communication systems. PMID:27814367

Medical Ultrasound Video Coding with H.265/HEVC Based on ROI Extraction.

PubMed

Wu, Yueying; Liu, Pengyu; Gao, Yuan; Jia, Kebin

2016-01-01

High-efficiency video compression technology is of primary importance to the storage and transmission of digital medical video in modern medical communication systems. To further improve the compression performance of medical ultrasound video, two innovative technologies based on diagnostic region-of-interest (ROI) extraction using the high efficiency video coding (H.265/HEVC) standard are presented in this paper. First, an effective ROI extraction algorithm based on image textural features is proposed to strengthen the applicability of ROI detection results in the H.265/HEVC quad-tree coding structure. Second, a hierarchical coding method based on transform coefficient adjustment and a quantization parameter (QP) selection process is designed to implement the otherness encoding for ROIs and non-ROIs. Experimental results demonstrate that the proposed optimization strategy significantly improves the coding performance by achieving a BD-BR reduction of 13.52% and a BD-PSNR gain of 1.16 dB on average compared to H.265/HEVC (HM15.0). The proposed medical video coding algorithm is expected to satisfy low bit-rate compression requirements for modern medical communication systems.

Analysis of iterative region-of-interest image reconstruction for x-ray computed tomography

PubMed Central

Sidky, Emil Y.; Kraemer, David N.; Roth, Erin G.; Ullberg, Christer; Reiser, Ingrid S.; Pan, Xiaochuan

2014-01-01

Abstract. One of the challenges for iterative image reconstruction (IIR) is that such algorithms solve an imaging model implicitly, requiring a complete representation of the scanned subject within the viewing domain of the scanner. This requirement can place a prohibitively high computational burden for IIR applied to x-ray computed tomography (CT), especially when high-resolution tomographic volumes are required. In this work, we aim to develop an IIR algorithm for direct region-of-interest (ROI) image reconstruction. The proposed class of IIR algorithms is based on an optimization problem that incorporates a data fidelity term, which compares a derivative of the estimated data with the available projection data. In order to characterize this optimization problem, we apply it to computer-simulated two-dimensional fan-beam CT data, using both ideal noiseless data and realistic data containing a level of noise comparable to that of the breast CT application. The proposed method is demonstrated for both complete field-of-view and ROI imaging. To demonstrate the potential utility of the proposed ROI imaging method, it is applied to actual CT scanner data. PMID:25685824

Analysis of iterative region-of-interest image reconstruction for x-ray computed tomography.

PubMed

Sidky, Emil Y; Kraemer, David N; Roth, Erin G; Ullberg, Christer; Reiser, Ingrid S; Pan, Xiaochuan

2014-10-03

One of the challenges for iterative image reconstruction (IIR) is that such algorithms solve an imaging model implicitly, requiring a complete representation of the scanned subject within the viewing domain of the scanner. This requirement can place a prohibitively high computational burden for IIR applied to x-ray computed tomography (CT), especially when high-resolution tomographic volumes are required. In this work, we aim to develop an IIR algorithm for direct region-of-interest (ROI) image reconstruction. The proposed class of IIR algorithms is based on an optimization problem that incorporates a data fidelity term, which compares a derivative of the estimated data with the available projection data. In order to characterize this optimization problem, we apply it to computer-simulated two-dimensional fan-beam CT data, using both ideal noiseless data and realistic data containing a level of noise comparable to that of the breast CT application. The proposed method is demonstrated for both complete field-of-view and ROI imaging. To demonstrate the potential utility of the proposed ROI imaging method, it is applied to actual CT scanner data.

Breast mass segmentation in mammograms combining fuzzy c-means and active contours

NASA Astrophysics Data System (ADS)

Hmida, Marwa; Hamrouni, Kamel; Solaiman, Basel; Boussetta, Sana

2018-04-01

Segmentation of breast masses in mammograms is a challenging issue due to the nature of mammography and the characteristics of masses. In fact, mammographic images are poor in contrast and breast masses have various shapes and densities with fuzzy and ill-defined borders. In this paper, we propose a method based on a modified Chan-Vese active contour model for mass segmentation in mammograms. We conduct the experiment on mass Regions of Interest (ROI) extracted from the MIAS database. The proposed method consists of mainly three stages: Firstly, the ROI is preprocessed to enhance the contrast. Next, two fuzzy membership maps are generated from the preprocessed ROI based on fuzzy C-Means algorithm. These fuzzy membership maps are finally used to modify the energy of the Chan-Vese model and to perform the final segmentation. Experimental results indicate that the proposed method yields good mass segmentation results.

Detection scheme for a partially occluded pedestrian based on occluded depth in lidar-radar sensor fusion

NASA Astrophysics Data System (ADS)

Kwon, Seong Kyung; Hyun, Eugin; Lee, Jin-Hee; Lee, Jonghun; Son, Sang Hyuk

2017-11-01

Object detections are critical technologies for the safety of pedestrians and drivers in autonomous vehicles. Above all, occluded pedestrian detection is still a challenging topic. We propose a new detection scheme for occluded pedestrian detection by means of lidar-radar sensor fusion. In the proposed method, the lidar and radar regions of interest (RoIs) have been selected based on the respective sensor measurement. Occluded depth is a new means to determine whether an occluded target exists or not. The occluded depth is a region projected out by expanding the longitudinal distance with maintaining the angle formed by the outermost two end points of the lidar RoI. The occlusion RoI is the overlapped region made by superimposing the radar RoI and the occluded depth. The object within the occlusion RoI is detected by the radar measurement information and the occluded object is estimated as a pedestrian based on human Doppler distribution. Additionally, various experiments are performed in detecting a partially occluded pedestrian in outdoor as well as indoor environments. According to experimental results, the proposed sensor fusion scheme has much better detection performance compared to the case without our proposed method.

Clinical MR-mammography: are computer-assisted methods superior to visual or manual measurements for curve type analysis? A systematic approach.

PubMed

Baltzer, Pascal Andreas Thomas; Freiberg, Christian; Beger, Sebastian; Vag, Tibor; Dietzel, Matthias; Herzog, Aimee B; Gajda, Mieczyslaw; Camara, Oumar; Kaiser, Werner A

2009-09-01

Enhancement characteristics after administration of a contrast agent are regarded as a major criterion for differential diagnosis in magnetic resonance mammography (MRM). However, no consensus exists about the best measurement method to assess contrast enhancement kinetics. This systematic investigation was performed to compare visual estimation with manual region of interest (ROI) and computer-aided diagnosis (CAD) analysis for time curve measurements in MRM. A total of 329 patients undergoing surgery after MRM (1.5 T) were analyzed prospectively. Dynamic data were measured using visual estimation, including ROI as well as CAD methods, and classified depending on initial signal increase and delayed enhancement. Pathology revealed 469 lesions (279 malignant, 190 benign). Kappa agreement between the methods ranged from 0.78 to 0.81. Diagnostic accuracies of 74.4% (visual), 75.7% (ROI), and 76.6% (CAD) were found without statistical significant differences. According to our results, curve type measurements are useful as a diagnostic criterion in breast lesions irrespective of the method used.

Automated PET-only quantification of amyloid deposition with adaptive template and empirically pre-defined ROI

NASA Astrophysics Data System (ADS)

Akamatsu, G.; Ikari, Y.; Ohnishi, A.; Nishida, H.; Aita, K.; Sasaki, M.; Yamamoto, Y.; Sasaki, M.; Senda, M.

2016-08-01

Amyloid PET is useful for early and/or differential diagnosis of Alzheimer’s disease (AD). Quantification of amyloid deposition using PET has been employed to improve diagnosis and to monitor AD therapy, particularly in research. Although MRI is often used for segmentation of gray matter and for spatial normalization into standard Montreal Neurological Institute (MNI) space where region-of-interest (ROI) template is defined, 3D MRI is not always available in clinical practice. The purpose of this study was to examine the feasibility of PET-only amyloid quantification with an adaptive template and a pre-defined standard ROI template that has been empirically generated from typical cases. A total of 68 subjects who underwent brain 11C-PiB PET were examined. The 11C-PiB images were non-linearly spatially normalized to the standard MNI T1 atlas using the same transformation parameters of MRI-based normalization. The automatic-anatomical-labeling-ROI (AAL-ROI) template was applied to the PET images. All voxel values were normalized by the mean value of cerebellar cortex to generate the SUVR-scaled images. Eleven typical positive images and eight typical negative images were normalized and averaged, respectively, and were used as the positive and negative template. Positive and negative masks which consist of voxels with SUVR  ⩾1.7 were extracted from both templates. Empirical PiB-prone ROI (EPP-ROI) was generated by subtracting the negative mask from the positive mask. The 11C-PiB image of each subject was non-rigidly normalized to the positive and negative template, respectively, and the one with higher cross-correlation was adopted. The EPP-ROI was then inversely transformed to individual PET images. We evaluated differences of SUVR between standard MRI-based method and PET-only method. We additionally evaluated whether the PET-only method would correctly categorize 11C-PiB scans as positive or negative. Significant correlation was observed between the SUVRs obtained with AAL-ROI and those with EPP-ROI when MRI-based normalization was used, the latter providing higher SUVR. When EPP-ROI was used, MRI-based method and PET-only method provided almost identical SUVR. All 11C-PiB scans were correctly categorized into positive and negative using a cutoff value of 1.7 as compared to visual interpretation. The 11C-PiB SUVR were 2.30  ±  0.24 and 1.25  ±  0.11 for the positive and negative images. PET-only amyloid quantification method with adaptive templates and EPP-ROI can provide accurate, robust and simple amyloid quantification without MRI.

Individual Functional ROI Optimization via Maximization of Group-wise Consistency of Structural and Functional Profiles

PubMed Central

Li, Kaiming; Guo, Lei; Zhu, Dajiang; Hu, Xintao; Han, Junwei; Liu, Tianming

2013-01-01

Studying connectivities among functional brain regions and the functional dynamics on brain networks has drawn increasing interest. A fundamental issue that affects functional connectivity and dynamics studies is how to determine the best possible functional brain regions or ROIs (regions of interest) for a group of individuals, since the connectivity measurements are heavily dependent on ROI locations. Essentially, identification of accurate, reliable and consistent corresponding ROIs is challenging due to the unclear boundaries between brain regions, variability across individuals, and nonlinearity of the ROIs. In response to these challenges, this paper presents a novel methodology to computationally optimize ROIs locations derived from task-based fMRI data for individuals so that the optimized ROIs are more consistent, reproducible and predictable across brains. Our computational strategy is to formulate the individual ROI location optimization as a group variance minimization problem, in which group-wise consistencies in functional/structural connectivity patterns and anatomic profiles are defined as optimization constraints. Our experimental results from multimodal fMRI and DTI data show that the optimized ROIs have significantly improved consistency in structural and functional profiles across individuals. These improved functional ROIs with better consistency could contribute to further study of functional interaction and dynamics in the human brain. PMID:22281931

Quantifying Digital Ulcers in Systemic Sclerosis: Reliability of Computer-Assisted Planimetry in Measuring Lesion Size.

PubMed

Simpson, V; Hughes, M; Wilkinson, J; Herrick, A L; Dinsdale, G

2018-03-01

Digital ulcers are a major problem in patients with systemic sclerosis (SSc), causing severe pain and impairment of hand function. In addition, digital ulcers heal slowly and sometimes become infected, which can lead to gangrene and necessitate amputation if appropriate intervention is not taken. A reliable, objective method for assessing digital ulcer healing or progression is needed in both the clinical and research arenas. This study was undertaken to compare 2 computer-assisted planimetry methods of measurement of digital ulcer area on photographs (ellipse and freehand regions of interest [ROIs]), and to assess the reliability of photographic calibration and the 2 methods of area measurement. Photographs were taken of 107 digital ulcers in 36 patients with SSc spectrum disease. Three raters assessed the photographs. Custom software allowed raters to calibrate photograph dimensions and draw ellipse or freehand ROIs. The shapes and dimensions of the ROIs were saved for further analysis. Calibration (by a single rater performing 5 repeats per image) produced an intraclass correlation coefficient (intrarater reliability) of 0.99. The mean ± SD areas of digital ulcers assessed using ellipse and freehand ROIs were 18.7 ± 20.2 mm 2 and 17.6 ± 19.3 mm 2 , respectively. Intrarater and interrater reliability of the ellipse ROI were 0.97 and 0.77, respectively. For the freehand ROI, the intrarater and interrater reliability were 0.98 and 0.76, respectively. Our findings indicate that computer-assisted planimetry methods applied to SSc-related digital ulcers can be extremely reliable. Further work is needed to move toward applying these methods as outcome measures for clinical trials and in clinical settings. © 2017, American College of Rheumatology.

Improving cervical region of interest by eliminating vaginal walls and cotton-swabs for automated image analysis

NASA Astrophysics Data System (ADS)

Venkataraman, Sankar; Li, Wenjing

2008-03-01

Image analysis for automated diagnosis of cervical cancer has attained high prominence in the last decade. Automated image analysis at all levels requires a basic segmentation of the region of interest (ROI) within a given image. The precision of the diagnosis is often reflected by the precision in detecting the initial region of interest, especially when some features outside the ROI mimic the ones within the same. Work described here discusses algorithms that are used to improve the cervical region of interest as a part of automated cervical image diagnosis. A vital visual aid in diagnosing cervical cancer is the aceto-whitening of the cervix after the application of acetic acid. Color and texture are used to segment acetowhite regions within the cervical ROI. Vaginal walls along with cottonswabs sometimes mimic these essential features leading to several false positives. Work presented here is focused towards detecting in-focus vaginal wall boundaries and then extrapolating them to exclude vaginal walls from the cervical ROI. In addition, discussed here is a marker-controlled watershed segmentation that is used to detect cottonswabs from the cervical ROI. A dataset comprising 50 high resolution images of the cervix acquired after 60 seconds of acetic acid application were used to test the algorithm. Out of the 50 images, 27 benefited from a new cervical ROI. Significant improvement in overall diagnosis was observed in these images as false positives caused by features outside the actual ROI mimicking acetowhite region were eliminated.

Non-contact measurement of oxygen saturation with an RGB camera

PubMed Central

Guazzi, Alessandro R.; Villarroel, Mauricio; Jorge, João; Daly, Jonathan; Frise, Matthew C.; Robbins, Peter A.; Tarassenko, Lionel

2015-01-01

A novel method (Sophia) is presented to track oxygen saturation changes in a controlled environment using an RGB camera placed approximately 1.5 m away from the subject. The method is evaluated on five healthy volunteers (Fitzpatrick skin phenotypes II, III, and IV) whose oxygen saturations were varied between 80% and 100% in a purpose-built chamber over 40 minutes each. The method carefully selects regions of interest (ROI) in the camera image by calculating signal-to-noise ratios for each ROI. This allows it to track changes in oxygen saturation accurately with respect to a conventional pulse oximeter (median coefficient of determination, 0.85). PMID:26417504

Automated processing of first-pass radionuclide angiocardiography by factor analysis of dynamic structures.

PubMed

Cavailloles, F; Bazin, J P; Capderou, A; Valette, H; Herbert, J L; Di Paola, R

1987-05-01

A method for automatic processing of cardiac first-pass radionuclide study is presented. This technique, factor analysis of dynamic structures (FADS) provides an automatic separation of anatomical structures according to their different temporal behaviour, even if they are superimposed. FADS has been applied to 76 studies. A description of factor patterns obtained in various pathological categories is presented. FADS provides easy diagnosis of shunts and tricuspid insufficiency. Quantitative information derived from the factors (cardiac output and mean transit time) were compared to those obtained by the region of interest method. Using FADS, a higher correlation with cardiac catheterization was found for cardiac output calculation. Thus compared to the ROI method, FADS presents obvious advantages: a good separation of overlapping cardiac chambers is obtained; this operator independant method provides more objective and reproducible results. A number of parameters of the cardio-pulmonary function can be assessed by first-pass radionuclide angiocardiography (RNA) [1,2]. Usually, they are calculated using time-activity curves (TAC) from regions of interest (ROI) drawn on the cardiac chambers and the lungs. This method has two main drawbacks: (1) the lack of inter and intra-observers reproducibility; (2) the problem of crosstalk which affects the evaluation of the cardio-pulmonary performance. The crosstalk on planar imaging is due to anatomical superimposition of the cardiac chambers and lungs. The activity measured in any ROI is the sum of the activity in several organs and 'decontamination' of the TAC cannot easily be performed using the ROI method [3]. Factor analysis of dynamic structures (FADS) [4,5] can solve the two problems mentioned above. It provides an automatic separation of anatomical structures according to their different temporal behaviour, even if they are superimposed. The resulting factors are estimates of the time evolution of the activity in each structure (underlying physiological components), and the associated factor images are estimates of the spatial distribution of each factor. The aim of this study was to assess the reliability of FADS in first pass RNA and compare the results to those obtained by the ROI method which is generally considered as the routine procedure.

System and method for generating motion corrected tomographic images

DOEpatents

Gleason, Shaun S [Knoxville, TN; Goddard, Jr., James S.

2012-05-01

A method and related system for generating motion corrected tomographic images includes the steps of illuminating a region of interest (ROI) to be imaged being part of an unrestrained live subject and having at least three spaced apart optical markers thereon. Simultaneous images are acquired from a first and a second camera of the markers from different angles. Motion data comprising 3D position and orientation of the markers relative to an initial reference position is then calculated. Motion corrected tomographic data obtained from the ROI using the motion data is then obtained, where motion corrected tomographic images obtained therefrom.

A simple derivation and analysis of a helical cone beam tomographic algorithm for long object imaging via a novel definition of region of interest

NASA Astrophysics Data System (ADS)

Hu, Jicun; Tam, Kwok; Johnson, Roger H.

2004-01-01

We derive and analyse a simple algorithm first proposed by Kudo et al (2001 Proc. 2001 Meeting on Fully 3D Image Reconstruction in Radiology and Nuclear Medicine (Pacific Grove, CA) pp 7-10) for long object imaging from truncated helical cone beam data via a novel definition of region of interest (ROI). Our approach is based on the theory of short object imaging by Kudo et al (1998 Phys. Med. Biol. 43 2885-909). One of the key findings in their work is that filtering of the truncated projection can be divided into two parts: one, finite in the axial direction, results from ramp filtering the data within the Tam window. The other, infinite in the z direction, results from unbounded filtering of ray sums over PI lines only. We show that for an ROI defined by PI lines emanating from the initial and final source positions on a helical segment, the boundary data which would otherwise contaminate the reconstruction of the ROI can be completely excluded. This novel definition of the ROI leads to a simple algorithm for long object imaging. The overscan of the algorithm is analytically calculated and it is the same as that of the zero boundary method. The reconstructed ROI can be divided into two regions: one is minimally contaminated by the portion outside the ROI, while the other is reconstructed free of contamination. We validate the algorithm with a 3D Shepp-Logan phantom and a disc phantom.

Classification of mass and normal breast tissue: A convolution neural network classifier with spatial domain and texture images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sahiner, B.; Chan, H.P.; Petrick, N.

1996-10-01

The authors investigated the classification of regions of interest (ROI`s) on mammograms as either mass or normal tissue using a convolution neural network (CNN). A CNN is a back-propagation neural network with two-dimensional (2-D) weight kernels that operate on images. A generalized, fast and stable implementation of the CNN was developed. The input images to the CNN were obtained form the ROI`s using two techniques. The first technique employed averaging and subsampling. The second technique employed texture feature extraction methods applied to small subregions inside the ROI. Features computed over different subregions were arranged as texture images, which were subsequentlymore » used as CNN inputs. The effects of CNN architecture and texture feature parameters on classification accuracy were studied. Receiver operating characteristic (ROC) methodology was used to evaluate the classification accuracy. A data set consisting of 168 ROI`s containing biopsy-proven masses and 504 ROI`s containing normal breast tissue was extracted from 168 mammograms by radiologists experienced in mammography. This data set was used for training and testing the CNN. With the best combination of CNN architecture and texture feature parameters, the area under the test ROC curve reached 0.87, which corresponded to a true-positive fraction of 90% at a false positive fraction of 31%. The results demonstrate the feasibility of using a CNN for classification of masses and normal tissue on mammograms.« less

Effect of various binning methods and ROI sizes on the accuracy of the automatic classification system for differentiation between diffuse infiltrative lung diseases on the basis of texture features at HRCT

NASA Astrophysics Data System (ADS)

Kim, Namkug; Seo, Joon Beom; Sung, Yu Sub; Park, Bum-Woo; Lee, Youngjoo; Park, Seong Hoon; Lee, Young Kyung; Kang, Suk-Ho

2008-03-01

To find optimal binning, variable binning size linear binning (LB) and non-linear binning (NLB) methods were tested. In case of small binning size (Q <= 10), NLB shows significant better accuracy than the LB. K-means NLB (Q = 26) is statistically significant better than every LB. To find optimal binning method and ROI size of the automatic classification system for differentiation between diffuse infiltrative lung diseases on the basis of textural analysis at HRCT Six-hundred circular regions of interest (ROI) with 10, 20, and 30 pixel diameter, comprising of each 100 ROIs representing six regional disease patterns (normal, NL; ground-glass opacity, GGO; reticular opacity, RO; honeycombing, HC; emphysema, EMPH; and consolidation, CONS) were marked by an experienced radiologist from HRCT images. Histogram (mean) and co-occurrence matrix (mean and SD of angular second moment, contrast, correlation, entropy, and inverse difference momentum) features were employed to test binning and ROI effects. To find optimal binning, variable binning size LB (bin size Q: 4~30, 32, 64, 128, 144, 196, 256, 384) and NLB (Q: 4~30) methods (K-means, and Fuzzy C-means clustering) were tested. For automated classification, a SVM classifier was implemented. To assess cross-validation of the system, a five-folding method was used. Each test was repeatedly performed twenty times. Overall accuracies with every combination of variable ROIs, and binning sizes were statistically compared. In case of small binning size (Q <= 10), NLB shows significant better accuracy than the LB. K-means NLB (Q = 26) is statistically significant better than every LB. In case of 30x30 ROI size and most of binning size, the K-means method showed better than other NLB and LB methods. When optimal binning and other parameters were set, overall sensitivity of the classifier was 92.85%. The sensitivity and specificity of the system for each class were as follows: NL, 95%, 97.9%; GGO, 80%, 98.9%; RO 85%, 96.9%; HC, 94.7%, 97%; EMPH, 100%, 100%; and CONS, 100%, 100%, respectively. We determined the optimal binning method and ROI size of the automatic classification system for differentiation between diffuse infiltrative lung diseases on the basis of texture features at HRCT.

A study on the measurement of the core body temperature change after radiofrequency ablation (RFA) through MR temperature mapping

NASA Astrophysics Data System (ADS)

Kim, Chang-Bok; Dong, Kyung-Rae; Yu, Young; Chung, Woon-Kwan; Cho, Jae-Hwan; Joo, Kyu-Ji

2013-09-01

This study examined the change in the heat generated during radiofrequency ablation (RFA) using a self-manufactured phantom and used magnetic resonance imaging (MRI) to analyze the change in the temperature of the core body and the tissues surrounding the phantom. In this experiment, the image and the phase image were obtained simultaneously from a gradient echo-based sequence using 1.5-Tesla MRI equipment and a 12-channel head coil. The temperature mapping technique was used to calculate the change in temperature. The regions of interest (ROIs) (ROI 1 - ROI 6) were set with a focus on the area where the RFA was performed, according to the temperature distribution, before monitoring the temperature change for one hour in time intervals of five minutes. The results showed that the temperature change in the ROI with time was largest in the ROI 1 and smallest in the ROI 5. In addition, after the RFA procedure, the temperature decreased from the initial value to 0 °C in one hour. The temperature changes in the core body and the surrounding tissues were confirmed by MRI temperature mapping, which is a noninvasive method.

Assessment of the Spectral Stability of Libya 4, Libya 1, and Mauritania 2 Sites Using Earth Observing One Hyperion

NASA Technical Reports Server (NTRS)

Choi, Taeyoung; Xiong, Xiaoxiong; Angal, Amit; Chander, Gyanesh; Qu, John J.

2014-01-01

The objective of this paper is to formulate a methodology to assess the spectral stability of the Libya 4, Libya 1, and Mauritania 2 pseudo-invariant calibration sites (PICS) using Earth Observing One (EO-1) Hyperion sensor. All the available Hyperion collections, downloaded from the Earth Explorer website, were utilized for the three PICS. In each site, a reference spectrum is selected at a specific day in the vicinity of the region of interest (ROI) defined by Committee on Earth Observation Satellites (CEOS). A series of ROIs are predefined in the along-track direction with 196 spectral top-of-atmosphere reflectance values in each ROI. Based on the reference ROI spectrum, the spectral stability of these ROIs is evaluated by average deviations (ADs) and spectral angle mapper (SAM) methods in the specific ranges of time and geo-spatial locations. Time and ROI location-dependent SAM and AD results are very stable within +/- 2 deg and +/-1.7% of 1sigma standard deviations. Consequently, the Libya 4, Mauritania 2, and Libya 1 CEOS selected PICS are spectrally stable targets within the time and spatial swath ranges of the Hyperion collections.

FISSA: A neuropil decontamination toolbox for calcium imaging signals.

PubMed

Keemink, Sander W; Lowe, Scott C; Pakan, Janelle M P; Dylda, Evelyn; van Rossum, Mark C W; Rochefort, Nathalie L

2018-02-22

In vivo calcium imaging has become a method of choice to image neuronal population activity throughout the nervous system. These experiments generate large sequences of images. Their analysis is computationally intensive and typically involves motion correction, image segmentation into regions of interest (ROIs), and extraction of fluorescence traces from each ROI. Out of focus fluorescence from surrounding neuropil and other cells can strongly contaminate the signal assigned to a given ROI. In this study, we introduce the FISSA toolbox (Fast Image Signal Separation Analysis) for neuropil decontamination. Given pre-defined ROIs, the FISSA toolbox automatically extracts the surrounding local neuropil and performs blind-source separation with non-negative matrix factorization. Using both simulated and in vivo data, we show that this toolbox performs similarly or better than existing published methods. FISSA requires only little RAM, and allows for fast processing of large datasets even on a standard laptop. The FISSA toolbox is available in Python, with an option for MATLAB format outputs, and can easily be integrated into existing workflows. It is available from Github and the standard Python repositories.

Classification of visual signs in abdominal CT image figures in biomedical literature

NASA Astrophysics Data System (ADS)

Xue, Zhiyun; You, Daekeun; Antani, Sameer; Long, L. Rodney; Demner-Fushman, Dina; Thoma, George R.

2014-03-01

"Imaging signs" are a critical part of radiology's language. They not only are important for conveying diagnosis, but may also aid in indexing radiology literature and retrieving relevant cases and images. Here we report our work towards representing and categorizing imaging signs of abdominal abnormalities in figures in the radiology literature. Given a region-of-interest (ROI) from a figure, our goal was to assign a correct imaging sign label to that ROI from the following seven: accordion, comb, ring, sandwich, small bowel feces, target, or whirl. As training and test data, we created our own "gold standard" dataset of regions containing imaging signs. We computed 2997 feature attributes to represent imaging sign characteristics for each ROI in training and test sets. Following feature selection they were reduced to 70 attributes and were input to a Support Vector Machine classifier. We applied image-enhancement methods to compensate for variable quality of the images in radiology articles. In particular we developed a method for automatic detection and removal of pointers/markers (arrows, arrowheads, and asterisk symbols) on the images. These pointers/markers are valuable for approximately locating ROIs; however, they degrade the classification because they are often (partially) included in the training ROIs. On a test set of 283 ROIs, our method achieved an overall accuracy of 70% in labeling the seven signs, which we believe is a promising result for using imaging signs to search/retrieve radiology literature. This work is also potentially valuable for the creation of a visual ontology of biomedical imaging entities.

MO-FG-CAMPUS-JeP3-02: A Novel Setup Approach to Improve C-Spine Curvature Reproducibility for Head and Neck Radiotherapy Using Optical Surface Imaging with Two Regions of Interest

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ryan, K; Gil, M; Li, G

Purpose: To develop a novel approach to improve cervical spine (c-spine) curvature reproducibility for head and neck (HN) patients using optical surface imaging (OSI) with two regions of interests (ROIs). Methods: The OSI-guided, two-step setup procedure requires two ROIs: ROI-1 of the shoulders and ROI-2 of the face. The neck can be stretched or squeezed in superior-inferior (SI) direction using a specially-designed sliding head support. We hypothesize that when these two ROIs are aligned, the c-spine should fall into a naturally reproducible position under same setup conditions. An anthropomorphous phantom test was performed to examine neck pitch angles comparing withmore » the calculated angles. Three volunteers participated in the experiments, which start with conventional HN setup using skin markers and room lasers. An OSI image and lateral photo-picture were acquired as the references. In each of the three replicate tests, conventional setup was first applied after volunteers got on the couch. ROI-1 was aligned by moving the body, followed by ROI-2 alignment via adjusting head position and orientation under real-time OSI guidance. A final static OSI image and lateral picture were taken to evaluate both anterior and posterior surface alignments. Three degrees of freedom can be adjusted if an open-face mask was applied, including head SI shift using the sliding head support and pitch-and-roll rotations using a commercial couch extension. Surface alignment was analyzed comparing with conventional setup. Results: The neck pitch angle measured by OSI is consistent with the calculated (0.2±0.6°). Volunteer study illustrated improved c-spine setup reproducibility using OSI comparing with conventional setup. ROI alignments with 2mm/1° tolerance are achieved within 3 minutes. Identical knee support is important to achieve ROI-1 pitch alignment. Conclusion: The feasibility of this novel approach has been demonstrated for c-spine curvature setup reproducibility. Further evaluation is necessary with bony alignment variation in patient studies. This study is in part supported by the NIH (U54CA137788).« less

Evaluation of a Region-of-Interest Approach for Detecting Progressive Glaucomatous Macular Damage on Optical Coherence Tomography

PubMed Central

Weng, Denis S. D.; Thenappan, Abinaya; Ritch, Robert; Hood, Donald C.

2018-01-01

Purpose To evaluate a manual region-of-interest (ROI) approach for detecting progressive macular ganglion cell complex (GCC) changes on optical coherence tomography (OCT) imaging. Methods One hundred forty-six eyes with a clinical diagnosis of glaucoma or suspected glaucoma with macular OCT scans obtained at least 1 year apart were evaluated. Changes in the GCC thickness were identified using a manual ROI approach (ROIM), whereby region(s) of observed or suspected glaucomatous damage were manually identified when using key features from the macular OCT scan on the second visit. Progression was also evaluated using the global GCC thickness and an automatic ROI approach (ROIA), where contiguous region(s) that fell below the 1% lower normative limit and exceeded 288 μm2 in size were evaluated. Longitudinal signal-to-noise ratios (SNRs) were calculated for progressive changes detected by each of these methods using individualized estimates of test–retest variability and age-related changes, obtained from 303 glaucoma and 394 healthy eyes, respectively. Results On average, the longitudinal SNR for the global thickness, ROIA and ROIM methods were −0.90 y−1, −0.91 y−1, and −1.03 y−1, respectively, and was significantly more negative for the ROIM compared with the global thickness (P = 0.003) and ROIA methods (P = 0.021). Conclusions Progressive glaucomatous macular GCC changes were optimally detected with a manual ROI approach. Translational Relevance These findings suggests that an approach based on a qualitative evaluation of OCT imaging information and consideration of known patterns of damage can improve the detection of progressive glaucomatous macular damage. PMID:29616153

Functional MRI Preprocessing in Lesioned Brains: Manual Versus Automated Region of Interest Analysis

PubMed Central

Garrison, Kathleen A.; Rogalsky, Corianne; Sheng, Tong; Liu, Brent; Damasio, Hanna; Winstein, Carolee J.; Aziz-Zadeh, Lisa S.

2015-01-01

Functional magnetic resonance imaging (fMRI) has significant potential in the study and treatment of neurological disorders and stroke. Region of interest (ROI) analysis in such studies allows for testing of strong a priori clinical hypotheses with improved statistical power. A commonly used automated approach to ROI analysis is to spatially normalize each participant’s structural brain image to a template brain image and define ROIs using an atlas. However, in studies of individuals with structural brain lesions, such as stroke, the gold standard approach may be to manually hand-draw ROIs on each participant’s non-normalized structural brain image. Automated approaches to ROI analysis are faster and more standardized, yet are susceptible to preprocessing error (e.g., normalization error) that can be greater in lesioned brains. The manual approach to ROI analysis has high demand for time and expertise, but may provide a more accurate estimate of brain response. In this study, commonly used automated and manual approaches to ROI analysis were directly compared by reanalyzing data from a previously published hypothesis-driven cognitive fMRI study, involving individuals with stroke. The ROI evaluated is the pars opercularis of the inferior frontal gyrus. Significant differences were identified in task-related effect size and percent-activated voxels in this ROI between the automated and manual approaches to ROI analysis. Task interactions, however, were consistent across ROI analysis approaches. These findings support the use of automated approaches to ROI analysis in studies of lesioned brains, provided they employ a task interaction design. PMID:26441816

Evaluation of Deformable Image Coregistration in Adaptive Dose Painting by Numbers for Head-and-Neck Cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Olteanu, Luiza A.M., E-mail: AnaMariaLuiza.Olteanu@uzgent.be; Madani, Indira; De Neve, Wilfried

Purpose: To assess the accuracy of contour deformation and feasibility of dose summation applying deformable image coregistration in adaptive dose painting by numbers (DPBN) for head and neck cancer. Methods and Materials: Data of 12 head-and-neck-cancer patients treated within a Phase I trial on adaptive {sup 18}F-FDG positron emission tomography (PET)-guided DPBN were used. Each patient had two DPBN treatment plans: the initial plan was based on a pretreatment PET/CT scan; the second adapted plan was based on a PET/CT scan acquired after 8 fractions. The median prescription dose to the dose-painted volume was 30 Gy for both DPBN plans.more » To obtain deformed contours and dose distributions, pretreatment CT was deformed to per-treatment CT using deformable image coregistration. Deformed contours of regions of interest (ROI{sub def}) were visually inspected and, if necessary, adjusted (ROI{sub def{sub ad}}) and both compared with manually redrawn ROIs (ROI{sub m}) using Jaccard (JI) and overlap indices (OI). Dose summation was done on the ROI{sub m}, ROI{sub def{sub ad}}, or their unions with the ROI{sub def}. Results: Almost all deformed ROIs were adjusted. The largest adjustment was made in patients with substantially regressing tumors: ROI{sub def} = 11.8 {+-} 10.9 cm{sup 3} vs. ROI{sub def{sub ad}} = 5.9 {+-} 7.8 cm{sup 3} vs. ROI{sub m} = 7.7 {+-} 7.2 cm{sup 3} (p = 0.57). The swallowing structures were the most frequently adjusted ROIs with the lowest indices for the upper esophageal sphincter: JI = 0.3 (ROI{sub def}) and 0.4 (ROI{sub def{sub ad}}); OI = 0.5 (both ROIs). The mandible needed the least adjustment with the highest indices: JI = 0.8 (both ROIs), OI = 0.9 (ROI{sub def}), and 1.0 (ROI{sub def{sub ad}}). Summed doses differed non-significantly. There was a trend of higher doses in the targets and lower doses in the spinal cord when doses were summed on unions. Conclusion: Visual inspection and adjustment were necessary for most ROIs. Fast automatic ROI propagation followed by user-driven adjustment appears to be more efficient than labor-intensive de novo drawing. Dose summation using deformable image coregistration was feasible. Biological uncertainties of dose summation strategies warrant further investigation.« less

Interface and permittivity simultaneous reconstruction in electrical capacitance tomography based on boundary and finite-elements coupling method

PubMed Central

Ren, Shangjie; Dong, Feng

2016-01-01

Electrical capacitance tomography (ECT) is a non-destructive detection technique for imaging the permittivity distributions inside an observed domain from the capacitances measurements on its boundary. Owing to its advantages of non-contact, non-radiation, high speed and low cost, ECT is promising in the measurements of many industrial or biological processes. However, in the practical industrial or biological systems, a deposit is normally seen in the inner wall of its pipe or vessel. As the actual region of interest (ROI) of ECT is surrounded by the deposit layer, the capacitance measurements become weakly sensitive to the permittivity perturbation occurring at the ROI. When there is a major permittivity difference between the deposit and the ROI, this kind of shielding effect is significant, and the permittivity reconstruction becomes challenging. To deal with the issue, an interface and permittivity simultaneous reconstruction approach is proposed. Both the permittivity at the ROI and the geometry of the deposit layer are recovered using the block coordinate descent method. The boundary and finite-elements coupling method is employed to improve the computational efficiency. The performance of the proposed method is evaluated with the simulation tests. This article is part of the themed issue ‘Supersensing through industrial process tomography’. PMID:27185960

Remote pedestrians detection at night time in FIR Image using contrast filtering and locally projected region based CNN

NASA Astrophysics Data System (ADS)

Kim, Taehwan; Kim, Sungho

2017-02-01

This paper presents a novel method to detect the remote pedestrians. After producing the human temperature based brightness enhancement image using the temperature data input, we generates the regions of interest (ROIs) by the multiscale contrast filtering based approach including the biased hysteresis threshold and clustering, remote pedestrian's height, pixel area and central position information. Afterwards, we conduct local vertical and horizontal projection based ROI refinement and weak aspect ratio based ROI limitation to solve the problem of region expansion in the contrast filtering stage. Finally, we detect the remote pedestrians by validating the final ROIs using transfer learning with convolutional neural network (CNN) feature, following non-maximal suppression (NMS) with strong aspect ratio limitation to improve the detection performance. In the experimental results, we confirmed that the proposed contrast filtering and locally projected region based CNN (CFLP-CNN) outperforms the baseline method by 8% in term of logaveraged miss rate. Also, the proposed method is more effective than the baseline approach and the proposed method provides the better regions that are suitably adjusted to the shape and appearance of remote pedestrians, which makes it detect the pedestrian that didn't find in the baseline approach and are able to help detect pedestrians by splitting the people group into a person.

Alzheimer disease: Quantitative analysis of I-123-iodoamphetamine SPECT brain imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hellman, R.S.; Tikofsky, R.S.; Collier, B.D.

1989-07-01

To enable a more quantitative diagnosis of senile dementia of the Alzheimer type (SDAT), the authors developed and tested a semiautomated method to define regions of interest (ROIs) to be used in quantitating results from single photon emission computed tomography (SPECT) of regional cerebral blood flow performed with N-isopropyl iodine-123-iodoamphetamine. SPECT/IMP imaging was performed in ten patients with probable SDAT and seven healthy subjects. Multiple ROIs were manually and semiautomatically generated, and uptake was quantitated for each ROI. Mean cortical activity was estimated as the average of the mean activity in 24 semiautomatically generated ROIs; mean cerebellar activity was determinedmore » from the mean activity in separate ROIs. A ratio of parietal to cerebellar activity less than 0.60 and a ratio of parietal to mean cortical activity less than 0.90 allowed correct categorization of nine of ten and eight of ten patients, respectively, with SDAT and all control subjects. The degree of diminished mental status observed in patients with SDAT correlated with both global and regional changes in IMP uptake.« less

Optimization of region-of-interest sampling strategies for hepatic MRI proton density fat fraction quantification.

PubMed

Hong, Cheng William; Wolfson, Tanya; Sy, Ethan Z; Schlein, Alexandra N; Hooker, Jonathan C; Fazeli Dehkordy, Soudabeh; Hamilton, Gavin; Reeder, Scott B; Loomba, Rohit; Sirlin, Claude B

2018-04-01

Clinical trials utilizing proton density fat fraction (PDFF) as an imaging biomarker for hepatic steatosis have used a laborious region-of-interest (ROI) sampling strategy of placing an ROI in each hepatic segment. To identify a strategy with the fewest ROIs that consistently achieves close agreement with the nine-ROI strategy. Retrospective secondary analysis of prospectively acquired clinical research data. A total of 391 adults (173 men, 218 women) with known or suspected NAFLD. Confounder-corrected chemical-shift-encoded 3T MRI using a 2D multiecho gradient-recalled echo technique. An ROI was placed in each hepatic segment. Mean nine-ROI PDFF and segmental PDFF standard deviation were computed. Segmental and lobar PDFF were compared. PDFF was estimated using every combinatorial subset of ROIs and compared to the nine-ROI average. Mean nine-ROI PDFF and segmental PDFF standard deviation were summarized descriptively. Segmental PDFF was compared using a one-way analysis of variance, and lobar PDFF was compared using a paired t-test and a Bland-Altman analysis. The PDFF estimated by every subset of ROIs was informally compared to the nine-ROI average using median intraclass correlation coefficients (ICCs) and Bland-Altman analyses. The study population's mean whole-liver PDFF was 10.1 ± 8.9% (range: 1.1-44.1%). Although there was no significant difference in average segmental (P = 0.452) or lobar (P = 0.154) PDFF, left and right lobe PDFF differed by at least 1.5 percentage points in 25.1% (98/391) of patients. Any strategy with ≥4 ROIs had ICC >0.995. 115 of 126 four-ROI strategies (91%) had limits of agreement (LOA) <1.5%, including four-ROI strategies with two ROIs from each lobe, which all had LOA <1.5%. 14/36 (39%) of two-ROI strategies and 74/84 (88%) of three-ROI strategies had ICC >0.995, and 2/36 (6%) of two-ROI strategies and 46/84 (55%) of three-ROI strategies had LOA <1.5%. Four-ROI sampling strategies with two ROIs in the left and right lobes achieve close agreement with nine-ROI PDFF. 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:988-994. © 2017 International Society for Magnetic Resonance in Medicine.

Optimization of Region-of-Interest Sampling Strategies for Hepatic MRI Proton Density Fat Fraction Quantification

PubMed Central

Hong, Cheng William; Wolfson, Tanya; Sy, Ethan Z.; Schlein, Alexandra N.; Hooker, Jonathan C.; Dehkordy, Soudabeh Fazeli; Hamilton, Gavin; Reeder, Scott B.; Loomba, Rohit; Sirlin, Claude B.

2017-01-01

BACKGROUND Clinical trials utilizing proton density fat fraction (PDFF) as an imaging biomarker for hepatic steatosis have used a laborious region-of-interest (ROI) sampling strategy of placing an ROI in each hepatic segment. PURPOSE To identify a strategy with the fewest ROIs that consistently achieves close agreement with the nine-ROI strategy. STUDY TYPE Retrospective secondary analysis of prospectively acquired clinical research data. POPULATION A total of 391 adults (173 men, 218 women) with known or suspected NAFLD. FIELD STRENGTH/SEQUENCE Confounder-corrected chemical-shift-encoded 3T MRI using a 2D multiecho gradientrecalled echo technique. ASSESSMENT An ROI was placed in each hepatic segment. Mean nine-ROI PDFF and segmental PDFF standard deviation were computed. Segmental and lobar PDFF were compared. PDFF was estimated using every combinatorial subset of ROIs and compared to the nine-ROI average. STATISTICAL TESTING Mean nine-ROI PDFF and segmental PDFF standard deviation were summarized descriptively. Segmental PDFF was compared using a one-way analysis of variance, and lobar PDFF was compared using a paired t-test and a Bland–Altman analysis. The PDFF estimated by every subset of ROIs was informally compared to the nine-ROI average using median intraclass correlation coefficients (ICCs) and Bland–Altman analyses. RESULTS The study population’s mean whole-liver PDFF was 10.1±8.9% (range: 1.1–44.1%). Although there was no significant difference in average segmental (P=0.452) or lobar (P=0.154) PDFF, left and right lobe PDFF differed by at least 1.5 percentage points in 25.1% (98/391) of patients. Any strategy with ≥ 4 ROIs had ICC >0.995. 115 of 126 four-ROI strategies (91%) had limits of agreement (LOA) <1.5%, including four-ROI strategies with two ROIs from each lobe, which all had LOA <1.5%. 14/36 (39%) of two-ROI strategies and 74/84 (88%) of three-ROI strategies had ICC >0.995, and 2/36 (6%) of two-ROI strategies and 46/84 (55%) of three-ROI strategies had LOA <1.5%. DATA CONCLUSION Four-ROI sampling strategies with two ROIs in the left and right lobes achieve close agreement with nine-ROI PDFF. Level of Evidence 3 Technical Efficacy Stage 2 PMID:28842937

A multiscale decomposition approach to detect abnormal vasculature in the optic disc.

PubMed

Agurto, Carla; Yu, Honggang; Murray, Victor; Pattichis, Marios S; Nemeth, Sheila; Barriga, Simon; Soliz, Peter

2015-07-01

This paper presents a multiscale method to detect neovascularization in the optic disc (NVD) using fundus images. Our method is applied to a manually selected region of interest (ROI) containing the optic disc. All the vessels in the ROI are segmented by adaptively combining contrast enhancement methods with a vessel segmentation technique. Textural features extracted using multiscale amplitude-modulation frequency-modulation, morphological granulometry, and fractal dimension are used. A linear SVM is used to perform the classification, which is tested by means of 10-fold cross-validation. The performance is evaluated using 300 images achieving an AUC of 0.93 with maximum accuracy of 88%. Copyright © 2015 Elsevier Ltd. All rights reserved.

Improved 3D live-wire method with application to 3D CT chest image analysis

NASA Astrophysics Data System (ADS)

Lu, Kongkuo; Higgins, William E.

2006-03-01

The definition of regions of interests (ROIs), such as suspect cancer nodules or lymph nodes in 3D CT chest images, is often difficult because of the complexity of the phenomena that give rise to them. Manual slice tracing has been used widely for years for such problems, because it is easy to implement and guaranteed to work. But the manual method is extremely time-consuming, especially for high-solution 3D images which may have hundreds of slices, and it is subject to operator biases. Numerous automated image-segmentation methods have been proposed, but they are generally strongly application dependent, and even the "most robust" methods have difficulty in defining complex anatomical ROIs. To address this problem, the semi-automatic interactive paradigm referred to as "live wire" segmentation has been proposed by researchers. In live-wire segmentation, the human operator interactively defines an ROI's boundary guided by an active automated method which suggests what to define. This process in general is far faster, more reproducible and accurate than manual tracing, while, at the same time, permitting the definition of complex ROIs having ill-defined boundaries. We propose a 2D live-wire method employing an improved cost over previous works. In addition, we define a new 3D live-wire formulation that enables rapid definition of 3D ROIs. The method only requires the human operator to consider a few slices in general. Experimental results indicate that the new 2D and 3D live-wire approaches are efficient, allow for high reproducibility, and are reliable for 2D and 3D object segmentation.

Comparison of 133 xenon ventilation equilibrium scan (XV) and 99m technetium transmission (TT) scan for use in regional lung analysis by 2D gamma scintigraphy in healthy and cystic fibrosis lungs.

PubMed

Zeman, Kirby L; Wu, Jihong; Donaldson, Scott H; Bennett, William D

2013-04-01

Quantification of particle deposition in the lung by gamma scintigraphy requires a reference image for location of regions of interest (ROIs) and normalization to lung thickness. In various laboratories, the reference image is made by a transmission scan ((57)Co or (99m)Tc) or gas ventilation scan ((133)Xe or (81)Kr). There has not been a direct comparison of measures from the two methods. We compared (99m)Tc transmission scans to (133)Xe equilibrium ventilation scans as reference images for 38 healthy subjects and 14 cystic fibrosis (CF) patients for their effects on measures of regional particle deposition: the central-to-peripheral ratio of lung counts (C/P); and ROI area versus forced vital capacity. Whole right lung ROI was based on either an isocontour threshold of three times the soft tissue transmission (TT) or a threshold of 20% of peak xenon ventilation counts (XV). We used a central ROI drawn to 50% of height and of width of the whole right lung ROI and placed along the left lung margin and centered vertically. In general, the correlation of normalized C/P (nC/P) between the two methods was strong. However, the value of nC/P was significantly smaller for the XV method than the TT method. Regression equations for the relationship of nC/P between the two methods were, for healthy subjects, y=0.75x+0.61, R(2)=0.64 using rectangular ROIs and y=0.76x+0.45, R(2)=0.66 using isocontour ROIs; and for CF patients, y=0.94x+0.46, R(2)=0.43 and y=0.85x+0.42, R(2)=0.41, respectively. (1) A transmission scan with an isocontour outline in combination with a rectangular central region to define the lung borders may be more useful than a ventilation scan. (2) Close correlation of nC/Ps measured by transmission or gas ventilation should allow confident comparison of values determined by the two methods.

Content-based image retrieval applied to bone age assessment

NASA Astrophysics Data System (ADS)

Fischer, Benedikt; Brosig, André; Welter, Petra; Grouls, Christoph; Günther, Rolf W.; Deserno, Thomas M.

2010-03-01

Radiological bone age assessment is based on local image regions of interest (ROI), such as the epiphysis or the area of carpal bones. These are compared to a standardized reference and scores determining the skeletal maturity are calculated. For computer-aided diagnosis, automatic ROI extraction and analysis is done so far mainly by heuristic approaches. Due to high variations in the imaged biological material and differences in age, gender and ethnic origin, automatic analysis is difficult and frequently requires manual interactions. On the contrary, epiphyseal regions (eROIs) can be compared to previous cases with known age by content-based image retrieval (CBIR). This requires a sufficient number of cases with reliable positioning of the eROI centers. In this first approach to bone age assessment by CBIR, we conduct leaving-oneout experiments on 1,102 left hand radiographs and 15,428 metacarpal and phalangeal eROIs from the USC hand atlas. The similarity of the eROIs is assessed by cross-correlation of 16x16 scaled eROIs. The effects of the number of eROIs, two age computation methods as well as the number of considered CBIR references are analyzed. The best results yield an error rate of 1.16 years and a standard deviation of 0.85 years. As the appearance of the hand varies naturally by up to two years, these results clearly demonstrate the applicability of the CBIR approach for bone age estimation.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kang, H; Malin, M; Chmura, S

Purpose: For African-American patients receiving breast radiotherapy with a bolus, skin darkening can affect the surface visualization when using optical imaging for daily positioning and gating at deep-inspiration breath holds (DIBH). Our goal is to identify a region-of-interest (ROI) that is robust against deteriorating surface image quality due to skin darkening. Methods: We study four patients whose post-mastectomy surfaces are imaged daily with AlignRT (VisionRT, UK) for DIBH radiotherapy and whose surface image quality is degraded toward the end of treatment. To simulate the effects of skin darkening, surfaces from the first ten fractions of each patient are systematically degradedmore » by 25–35%, 40–50% and 65–75% of the total area of the clinically used ROI-ipsilateral-chestwall. The degraded surfaces are registered to the reference surface in six degrees-of-freedom. To identify a robust ROI, three additional reference ROIs — ROI-chest+abdomen, ROI-bilateral-chest and ROI-extended-ipsilateral-chestwall are created and registered to the degraded surfaces. Differences in registration using these ROIs are compared to that using ROI-ipsilateral-chestwall. Results: For three patients, the deviations in the registrations to ROI-ipsilateral-chestwall are > 2.0, 3.1 and 7.9mm on average for 25–35%, 40–50% and 65–75% degraded surfaces, respectively. Rotational deviations reach 11.1° in pitch. For the last patient, registration is consistent to within 2.6mm even on the 65–75% degraded surfaces, possibly because the surface topography has more distinct features. For ROI-bilateral-chest and ROI-extended-ipsilateral-chest registrations deviate in a similar pattern. However, registration on ROI-chest+abdomen is robust to deteriorating image qualities to within 4.2mm for all four patients. Conclusion: Registration deviations using ROI-ipsilateral-chestwall can reach 9.8mm on the 40–50% degraded surfaces. Caution is required when using AlignRT for patients experiencing skin darkening since the accuracy of AlignRT registration deteriorates. To avoid this inaccuracy, we recommend use of ROI-chest+abdomen, on which registration is consistent within 4.2mm even for highly degraded surfaces.« less

Automated segmentation of ultrasonic breast lesions using statistical texture classification and active contour based on probability distance.

PubMed

Liu, Bo; Cheng, H D; Huang, Jianhua; Tian, Jiawei; Liu, Jiafeng; Tang, Xianglong

2009-08-01

Because of its complicated structure, low signal/noise ratio, low contrast and blurry boundaries, fully automated segmentation of a breast ultrasound (BUS) image is a difficult task. In this paper, a novel segmentation method for BUS images without human intervention is proposed. Unlike most published approaches, the proposed method handles the segmentation problem by using a two-step strategy: ROI generation and ROI segmentation. First, a well-trained texture classifier categorizes the tissues into different classes, and the background knowledge rules are used for selecting the regions of interest (ROIs) from them. Second, a novel probability distance-based active contour model is applied for segmenting the ROIs and finding the accurate positions of the breast tumors. The active contour model combines both global statistical information and local edge information, using a level set approach. The proposed segmentation method was performed on 103 BUS images (48 benign and 55 malignant). To validate the performance, the results were compared with the corresponding tumor regions marked by an experienced radiologist. Three error metrics, true-positive ratio (TP), false-negative ratio (FN) and false-positive ratio (FP) were used for measuring the performance of the proposed method. The final results (TP = 91.31%, FN = 8.69% and FP = 7.26%) demonstrate that the proposed method can segment BUS images efficiently, quickly and automatically.

Thoracic cavity definition for 3D PET/CT analysis and visualization.

PubMed

Cheirsilp, Ronnarit; Bascom, Rebecca; Allen, Thomas W; Higgins, William E

2015-07-01

X-ray computed tomography (CT) and positron emission tomography (PET) serve as the standard imaging modalities for lung-cancer management. CT gives anatomical details on diagnostic regions of interest (ROIs), while PET gives highly specific functional information. During the lung-cancer management process, a patient receives a co-registered whole-body PET/CT scan pair and a dedicated high-resolution chest CT scan. With these data, multimodal PET/CT ROI information can be gleaned to facilitate disease management. Effective image segmentation of the thoracic cavity, however, is needed to focus attention on the central chest. We present an automatic method for thoracic cavity segmentation from 3D CT scans. We then demonstrate how the method facilitates 3D ROI localization and visualization in patient multimodal imaging studies. Our segmentation method draws upon digital topological and morphological operations, active-contour analysis, and key organ landmarks. Using a large patient database, the method showed high agreement to ground-truth regions, with a mean coverage=99.2% and leakage=0.52%. Furthermore, it enabled extremely fast computation. For PET/CT lesion analysis, the segmentation method reduced ROI search space by 97.7% for a whole-body scan, or nearly 3 times greater than that achieved by a lung mask. Despite this reduction, we achieved 100% true-positive ROI detection, while also reducing the false-positive (FP) detection rate by >5 times over that achieved with a lung mask. Finally, the method greatly improved PET/CT visualization by eliminating false PET-avid obscurations arising from the heart, bones, and liver. In particular, PET MIP views and fused PET/CT renderings depicted unprecedented clarity of the lesions and neighboring anatomical structures truly relevant to lung-cancer assessment. Copyright © 2015 Elsevier Ltd. All rights reserved.

Thoracic Cavity Definition for 3D PET/CT Analysis and Visualization

PubMed Central

Cheirsilp, Ronnarit; Bascom, Rebecca; Allen, Thomas W.; Higgins, William E.

2015-01-01

X-ray computed tomography (CT) and positron emission tomography (PET) serve as the standard imaging modalities for lung-cancer management. CT gives anatomical detail on diagnostic regions of interest (ROIs), while PET gives highly specific functional information. During the lung-cancer management process, a patient receives a co-registered whole-body PET/CT scan pair and a dedicated high-resolution chest CT scan. With these data, multimodal PET/CT ROI information can be gleaned to facilitate disease management. Effective image segmentation of the thoracic cavity, however, is needed to focus attention on the central chest. We present an automatic method for thoracic cavity segmentation from 3D CT scans. We then demonstrate how the method facilitates 3D ROI localization and visualization in patient multimodal imaging studies. Our segmentation method draws upon digital topological and morphological operations, active-contour analysis, and key organ landmarks. Using a large patient database, the method showed high agreement to ground-truth regions, with a mean coverage = 99.2% and leakage = 0.52%. Furthermore, it enabled extremely fast computation. For PET/CT lesion analysis, the segmentation method reduced ROI search space by 97.7% for a whole-body scan, or nearly 3 times greater than that achieved by a lung mask. Despite this reduction, we achieved 100% true-positive ROI detection, while also reducing the false-positive (FP) detection rate by >5 times over that achieved with a lung mask. Finally, the method greatly improved PET/CT visualization by eliminating false PET-avid obscurations arising from the heart, bones, and liver. In particular, PET MIP views and fused PET/CT renderings depicted unprecedented clarity of the lesions and neighboring anatomical structures truly relevant to lung-cancer assessment. PMID:25957746

Nodule Detection in a Lung Region that's Segmented with Using Genetic Cellular Neural Networks and 3D Template Matching with Fuzzy Rule Based Thresholding

PubMed Central

Osman, Onur; Ucan, Osman N.

2008-01-01

Objective The purpose of this study was to develop a new method for automated lung nodule detection in serial section CT images with using the characteristics of the 3D appearance of the nodules that distinguish themselves from the vessels. Materials and Methods Lung nodules were detected in four steps. First, to reduce the number of region of interests (ROIs) and the computation time, the lung regions of the CTs were segmented using Genetic Cellular Neural Networks (G-CNN). Then, for each lung region, ROIs were specified with using the 8 directional search; +1 or -1 values were assigned to each voxel. The 3D ROI image was obtained by combining all the 2-Dimensional (2D) ROI images. A 3D template was created to find the nodule-like structures on the 3D ROI image. Convolution of the 3D ROI image with the proposed template strengthens the shapes that are similar to those of the template and it weakens the other ones. Finally, fuzzy rule based thresholding was applied and the ROI's were found. To test the system's efficiency, we used 16 cases with a total of 425 slices, which were taken from the Lung Image Database Consortium (LIDC) dataset. Results The computer aided diagnosis (CAD) system achieved 100% sensitivity with 13.375 FPs per case when the nodule thickness was greater than or equal to 5.625 mm. Conclusion Our results indicate that the detection performance of our algorithm is satisfactory, and this may well improve the performance of computer-aided detection of lung nodules. PMID:18253070

Improved operator agreement and efficiency using the minimum area contour change method for delineation of hyperintense multiple sclerosis lesions on FLAIR MRI

PubMed Central

2013-01-01

Background Activity of disease in patients with multiple sclerosis (MS) is monitored by detecting and delineating hyper-intense lesions on MRI scans. The Minimum Area Contour Change (MACC) algorithm has been created with two main goals: a) to improve inter-operator agreement on outlining regions of interest (ROIs) and b) to automatically propagate longitudinal ROIs from the baseline scan to a follow-up scan. Methods The MACC algorithm first identifies an outer bound for the solution path, forms a high number of iso-contour curves based on equally spaced contour values, and then selects the best contour value to outline the lesion. The MACC software was tested on a set of 17 FLAIR MRI images evaluated by a pair of human experts and a longitudinal dataset of 12 pairs of T2-weighted Fluid Attenuated Inversion Recovery (FLAIR) images that had lesion analysis ROIs drawn by a single expert operator. Results In the tests where two human experts evaluated the same MRI images, the MACC program demonstrated that it could markedly reduce inter-operator outline error. In the longitudinal part of the study, the MACC program created ROIs on follow-up scans that were in close agreement to the original expert’s ROIs. Finally, in a post-hoc analysis of 424 follow-up scans 91% of propagated MACC were accepted by an expert and only 9% of the final accepted ROIS had to be created or edited by the expert. Conclusion When used with an expert operator's verification of automatically created ROIs, MACC can be used to improve inter- operator agreement and decrease analysis time, which should improve data collected and analyzed in multicenter clinical trials. PMID:24004511

Improvement of light penetration based silkworm gender identification with confined regions of interest

NASA Astrophysics Data System (ADS)

Kamtongdee, Chakkrit; Sumriddetchkajorn, Sarun; Sa-ngiamsak, Chiranut

2013-06-01

Based on our previous work on light penetration-based silkworm gender identification, we find that unwanted optical noises scattering from the surrounding area near the silkworm pupa and the transparent support are sometimes analyzed and misinterpreted leading to incorrect silkworm gender identification. To alleviate this issue, we place a small rectangular hole on a transparent support so that it not only helps the user precisely place the silkworm pupa but also functions as a region of interest (ROI) for blocking unwanted optical noises and for roughly locating the abdomen region in the image for ease of image processing. Apart from the external ROI, we also assign a smaller ROI inside the image in order to remove strong scattering light from all edges of the external ROI and at the same time speed up our image processing operations. With only the external ROI in function, our experiment shows a measured 86% total accuracy in identifying gender of 120 silkworm pupae with a measured average processing time of 38 ms. Combining the external ROI and the image ROI together revamps the total accuracy in identifying the silkworm gender to 95% with a measured faster 18 ms processing time.

Fast and effective characterization of 3D region of interest in medical image data

NASA Astrophysics Data System (ADS)

Kontos, Despina; Megalooikonomou, Vasileios

2004-05-01

We propose a framework for detecting, characterizing and classifying spatial Regions of Interest (ROIs) in medical images, such as tumors and lesions in MRI or activation regions in fMRI. A necessary step prior to classification is efficient extraction of discriminative features. For this purpose, we apply a characterization technique especially designed for spatial ROIs. The main idea of this technique is to extract a k-dimensional feature vector using concentric spheres in 3D (or circles in 2D) radiating out of the ROI's center of mass. These vectors form characterization signatures that can be used to represent the initial ROIs. We focus on classifying fMRI ROIs obtained from a study that explores neuroanatomical correlates of semantic processing in Alzheimer's disease (AD). We detect a ROI highly associated with AD and apply the feature extraction technique with different experimental settings. We seek to distinguish control from patient samples. We study how classification can be performed using the extracted signatures as well as how different experimental parameters affect classification accuracy. The obtained classification accuracy ranged from 82% to 87% (based on the selected ROI) suggesting that the proposed classification framework can be potentially useful in supporting medical decision-making.

Ultrasound estimates of muscle quality in older adults: reliability and comparison of Photoshop and ImageJ for the grayscale analysis of muscle echogenicity

PubMed Central

Seamon, Bryant A.; Teixeira, Carla; Ismail, Catheeja

2016-01-01

Background. Quantitative diagnostic ultrasound imaging has been proposed as a method of estimating muscle quality using measures of echogenicity. The Rectangular Marquee Tool (RMT) and the Free Hand Tool (FHT) are two types of editing features used in Photoshop and ImageJ for determining a region of interest (ROI) within an ultrasound image. The primary objective of this study is to determine the intrarater and interrater reliability of Photoshop and ImageJ for the estimate of muscle tissue echogenicity in older adults via grayscale histogram analysis. The secondary objective is to compare the mean grayscale values obtained using both the RMT and FHT methods across both image analysis platforms. Methods. This cross-sectional observational study features 18 community-dwelling men (age = 61.5 ± 2.32 years). Longitudinal views of the rectus femoris were captured using B-mode ultrasound. The ROI for each scan was selected by 2 examiners using the RMT and FHT methods from each software program. Their reliability is assessed using intraclass correlation coefficients (ICCs) and the standard error of the measurement (SEM). Measurement agreement for these values is depicted using Bland-Altman plots. A paired t-test is used to determine mean differences in echogenicity expressed as grayscale values using the RMT and FHT methods to select the post-image acquisition ROI. The degree of association among ROI selection methods and image analysis platforms is analyzed using the coefficient of determination (R2). Results. The raters demonstrated excellent intrarater and interrater reliability using the RMT and FHT methods across both platforms (lower bound 95% CI ICC = .97–.99, p < .001). Mean differences between the echogenicity estimates obtained with the RMT and FHT methods was .87 grayscale levels (95% CI [.54–1.21], p < .0001) using data obtained with both programs. The SEM for Photoshop was .97 and 1.05 grayscale levels when using the RMT and FHT ROI selection methods, respectively. Comparatively, the SEM values were .72 and .81 grayscale levels, respectively, when using the RMT and FHT ROI selection methods in ImageJ. Uniform coefficients of determination (R2 = .96–.99, p < .001) indicate strong positive associations among the grayscale histogram analysis measurement conditions independent of the ROI selection methods and imaging platform. Conclusion. Our method for evaluating muscle echogenicity demonstrated a high degree of intrarater and interrater reliability using both the RMT and FHT methods across 2 common image analysis platforms. The minimal measurement error exhibited by the examiners demonstrates that the ROI selection methods used with Photoshop and ImageJ are suitable for the post-acquisition image analysis of tissue echogenicity in older adults. PMID:26925339

Reduced field-of-view imaging for single-shot MRI with an amplitude-modulated chirp pulse excitation and Fourier transform reconstruction.

PubMed

Li, Jing; Zhang, Miao; Chen, Lin; Cai, Congbo; Sun, Huijun; Cai, Shuhui

2015-06-01

We employ an amplitude-modulated chirp pulse to selectively excite spins in one or more regions of interest (ROIs) to realize reduced field-of-view (rFOV) imaging based on single-shot spatiotemporally encoded (SPEN) sequence and Fourier transform reconstruction. The proposed rFOV imaging method was theoretically analyzed and illustrated with numerical simulation and tested with phantom experiments and in vivo rat experiments. In addition, point spread function was applied to demonstrate the feasibility of the proposed method. To evaluate the proposed method, the rFOV results were compared with those obtained using the EPI method with orthogonal RF excitation. The simulation and experimental results show that the proposed method can image one or two separated ROIs along the SPEN dimension in a single shot with higher spatial resolution, less sensitive to field inhomogeneity, and practically no aliasing artifacts. In addition, the proposed method may produce rFOV images with comparable signal-to-noise ratio to the rFOV EPI images. The proposed method is promising for the applications under severe susceptibility heterogeneities and for imaging separate ROIs simultaneously. Copyright © 2015 Elsevier Inc. All rights reserved.

Detection of lobular structures in normal breast tissue.

PubMed

Apou, Grégory; Schaadt, Nadine S; Naegel, Benoît; Forestier, Germain; Schönmeyer, Ralf; Feuerhake, Friedrich; Wemmert, Cédric; Grote, Anne

2016-07-01

Ongoing research into inflammatory conditions raises an increasing need to evaluate immune cells in histological sections in biologically relevant regions of interest (ROIs). Herein, we compare different approaches to automatically detect lobular structures in human normal breast tissue in digitized whole slide images (WSIs). This automation is required to perform objective and consistent quantitative studies on large data sets. In normal breast tissue from nine healthy patients immunohistochemically stained for different markers, we evaluated and compared three different image analysis methods to automatically detect lobular structures in WSIs: (1) a bottom-up approach using the cell-based data for subsequent tissue level classification, (2) a top-down method starting with texture classification at tissue level analysis of cell densities in specific ROIs, and (3) a direct texture classification using deep learning technology. All three methods result in comparable overall quality allowing automated detection of lobular structures with minor advantage in sensitivity (approach 3), specificity (approach 2), or processing time (approach 1). Combining the outputs of the approaches further improved the precision. Different approaches of automated ROI detection are feasible and should be selected according to the individual needs of biomarker research. Additionally, detected ROIs could be used as a basis for quantification of immune infiltration in lobular structures. Copyright © 2016 Elsevier Ltd. All rights reserved.

Composite Wavelet Filters for Enhanced Automated Target Recognition

NASA Technical Reports Server (NTRS)

Chiang, Jeffrey N.; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin

2012-01-01

Automated Target Recognition (ATR) systems aim to automate target detection, recognition, and tracking. The current project applies a JPL ATR system to low-resolution sonar and camera videos taken from unmanned vehicles. These sonar images are inherently noisy and difficult to interpret, and pictures taken underwater are unreliable due to murkiness and inconsistent lighting. The ATR system breaks target recognition into three stages: 1) Videos of both sonar and camera footage are broken into frames and preprocessed to enhance images and detect Regions of Interest (ROIs). 2) Features are extracted from these ROIs in preparation for classification. 3) ROIs are classified as true or false positives using a standard Neural Network based on the extracted features. Several preprocessing, feature extraction, and training methods are tested and discussed in this paper.

Dynamic Contrast-Enhanced MRI in Head-and-Neck Cancer: The Impact of Region of Interest Selection on the Intra- and Interpatient Variability of Pharmacokinetic Parameters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Craciunescu, Oana I., E-mail: oana.craciunescu@duke.edu; Yoo, David S.; Cleland, Esi

2012-03-01

Purpose: Dynamic contrast-enhanced (DCE) MRI-extracted parameters measure tumor microvascular physiology and are usually calculated from an intratumor region of interest (ROI). Optimal ROI delineation is not established. The valid clinical use of DCE-MRI requires that the variation for any given parameter measured within a tumor be less than that observed between tumors in different patients. This work evaluates the impact of tumor ROI selection on the assessment of intra- and interpatient variability. Method and Materials: Head and neck cancer patients received initial targeted therapy (TT) treatment with erlotinib and/or bevacizumab, followed by radiotherapy and concurrent cisplatin with synchronous TT. DCE-MRImore » data from Baseline and the end of the TT regimen (Lead-In) were analyzed to generate the vascular transfer function (K{sup trans}), the extracellular volume fraction (v{sub e}), and the initial area under the concentration time curve (iAUC{sub 1min}). Four ROI sampling strategies were used: whole tumor or lymph node (Whole), the slice containing the most enhancing voxels (SliceMax), three slices centered in SliceMax (Partial), and the 5% most enhancing contiguous voxels within SliceMax (95Max). The average coefficient of variation (aCV) was calculated to establish intrapatient variability among ROI sets and interpatient variability for each ROI type. The average ratio between each intrapatient CV and the interpatient CV was calculated (aRCV). Results: Baseline primary/nodes aRCVs for different ROIs not including 95Max were, for all three MR parameters, in the range of 0.14-0.24, with Lead-In values between 0.09 and 0.2, meaning a low intrapatient vs. interpatient variation. For 95Max, intrapatient CVs approximated interpatient CVs, meaning similar data dispersion and higher aRCVs (0.6-1.27 for baseline) and 0.54-0.95 for Lead-In. Conclusion: Distinction between different patient's primary tumors and/or nodes cannot be made using 95Max ROIs. The other three strategies are viable and equivalent for using DCE-MRI to measure head and neck cancer physiology.« less

Towards standardization of 18F-FET PET imaging: do we need a consistent method of background activity assessment?

PubMed

Unterrainer, Marcus; Vettermann, Franziska; Brendel, Matthias; Holzgreve, Adrien; Lifschitz, Michael; Zähringer, Matthias; Suchorska, Bogdana; Wenter, Vera; Illigens, Ben M; Bartenstein, Peter; Albert, Nathalie L

2017-12-01

PET with O-(2- 18 F-fluoroethyl)-L-tyrosine ( 18 F-FET) has reached increasing clinical significance for patients with brain neoplasms. For quantification of standard PET-derived parameters such as the tumor-to-background ratio, the background activity is assessed using a region of interest (ROI) or volume of interest (VOI) in unaffected brain tissue. However, there is no standardized approach regarding the assessment of the background reference. Therefore, we evaluated the intra- and inter-reader variability of commonly applied approaches for clinical 18 F-FET PET reading. The background activity of 20 18 F-FET PET scans was independently evaluated by 6 readers using a (i) simple 2D-ROI, (ii) spherical VOI with 3.0 cm diameter, and (iii) VOI consisting of crescent-shaped ROIs; each in the contralateral, non-affected hemisphere including white and gray matter in line with the European Association of Nuclear Medicine (EANM) and German guidelines. To assess intra-reader variability, each scan was evaluated 10 times by each reader. The coefficient of variation (CoV) was assessed for determination of intra- and inter-reader variability. In a second step, the best method was refined by instructions for a guided background activity assessment and validated by 10 further scans. Compared to the other approaches, the crescent-shaped VOIs revealed most stable results with the lowest intra-reader variabilities (median CoV 1.52%, spherical VOI 4.20%, 2D-ROI 3.69%; p < 0.001) and inter-reader variabilities (median CoV 2.14%, spherical VOI 4.02%, 2D-ROI 3.83%; p = 0.001). Using the guided background assessment, both intra-reader variabilities (median CoV 1.10%) and inter-reader variabilities (median CoV 1.19%) could be reduced even more. The commonly applied methods for background activity assessment show different variability which might hamper 18 F-FET PET quantification and comparability in multicenter settings. The proposed background activity assessment using a (guided) crescent-shaped VOI allows minimization of both intra- and inter-reader variability and might facilitate comprehensive methodological standardization of amino acid PET which is of interest in the light of the anticipated EANM technical guidelines.

A novel background field removal method for MRI using projection onto dipole fields (PDF).

PubMed

Liu, Tian; Khalidov, Ildar; de Rochefort, Ludovic; Spincemaille, Pascal; Liu, Jing; Tsiouris, A John; Wang, Yi

2011-11-01

For optimal image quality in susceptibility-weighted imaging and accurate quantification of susceptibility, it is necessary to isolate the local field generated by local magnetic sources (such as iron) from the background field that arises from imperfect shimming and variations in magnetic susceptibility of surrounding tissues (including air). Previous background removal techniques have limited effectiveness depending on the accuracy of model assumptions or information input. In this article, we report an observation that the magnetic field for a dipole outside a given region of interest (ROI) is approximately orthogonal to the magnetic field of a dipole inside the ROI. Accordingly, we propose a nonparametric background field removal technique based on projection onto dipole fields (PDF). In this PDF technique, the background field inside an ROI is decomposed into a field originating from dipoles outside the ROI using the projection theorem in Hilbert space. This novel PDF background removal technique was validated on a numerical simulation and a phantom experiment and was applied in human brain imaging, demonstrating substantial improvement in background field removal compared with the commonly used high-pass filtering method. Copyright © 2011 John Wiley & Sons, Ltd.

Classification of cirrhotic liver in Gadolinium-enhanced MR images

NASA Astrophysics Data System (ADS)

Lee, Gobert; Uchiyama, Yoshikazu; Zhang, Xuejun; Kanematsu, Masayuki; Zhou, Xiangrong; Hara, Takeshi; Kato, Hiroki; Kondo, Hiroshi; Fujita, Hiroshi; Hoshi, Hiroaki

2007-03-01

Cirrhosis of the liver is characterized by the presence of widespread nodules and fibrosis in the liver. The fibrosis and nodules formation causes distortion of the normal liver architecture, resulting in characteristic texture patterns. Texture patterns are commonly analyzed with the use of co-occurrence matrix based features measured on regions-of-interest (ROIs). A classifier is subsequently used for the classification of cirrhotic or non-cirrhotic livers. Problem arises if the classifier employed falls into the category of supervised classifier which is a popular choice. This is because the 'true disease states' of the ROIs are required for the training of the classifier but is, generally, not available. A common approach is to adopt the 'true disease state' of the liver as the 'true disease state' of all ROIs in that liver. This paper investigates the use of a nonsupervised classifier, the k-means clustering method in classifying livers as cirrhotic or non-cirrhotic using unlabelled ROI data. A preliminary result with a sensitivity and specificity of 72% and 60%, respectively, demonstrates the feasibility of using the k-means non-supervised clustering method in generating a characteristic cluster structure that could facilitate the classification of cirrhotic and non-cirrhotic livers.

A voxel-based approach to gray matter asymmetries.

PubMed

Luders, E; Gaser, C; Jancke, L; Schlaug, G

2004-06-01

Voxel-based morphometry (VBM) was used to analyze gray matter (GM) asymmetries in a large sample (n = 60) of male and female professional musicians with and without absolute pitch (AP). We chose to examine these particular groups because previous studies using traditional region-of-interest (ROI) analyses have shown differences in hemispheric asymmetry related to AP and gender. Voxel-based methods may have advantages over traditional ROI-based methods since the analysis can be performed across the whole brain with minimal user bias. After determining that the VBM method was sufficiently sensitive for the detection of differences in GM asymmetries between groups, we found that male AP musicians were more leftward lateralized in the anterior region of the planum temporale (PT) than male non-AP musicians. This confirmed the results of previous studies using ROI-based methods that showed an association between PT asymmetry and the AP phenotype. We further observed that male non-AP musicians revealed an increased leftward GM asymmetry in the postcentral gyrus compared to female non-AP musicians, again corroborating results of a previously published study using ROI-based methods. By analyzing hemispheric GM differences across our entire sample, we were able to partially confirm findings of previous studies using traditional morphometric techniques, as well as more recent, voxel-based analyses. In addition, we found some unusually pronounced GM asymmetries in our musician sample not previously detected in subjects unselected for musical training. Since we were able to validate gender- and AP-related brain asymmetries previously described using traditional ROI-based morphometric techniques, the results of our analyses support the use of VBM for examinations of GM asymmetries.

Evaluation of fatty proportion in fatty liver using least squares method with constraints.

PubMed

Li, Xingsong; Deng, Yinhui; Yu, Jinhua; Wang, Yuanyuan; Shamdasani, Vijay

2014-01-01

Backscatter and attenuation parameters are not easily measured in clinical applications due to tissue inhomogeneity in the region of interest (ROI). A least squares method(LSM) that fits the echo signal power spectra from a ROI to a 3-parameter tissue model was used to get attenuation coefficient imaging in fatty liver. Since fat's attenuation value is higher than normal liver parenchyma, a reasonable threshold was chosen to evaluate the fatty proportion in fatty liver. Experimental results using clinical data of fatty liver illustrate that the least squares method can get accurate attenuation estimates. It is proved that the attenuation values have a positive correlation with the fatty proportion, which can be used to evaluate the syndrome of fatty liver.

Opacity annotation of diffuse lung diseases using deep convolutional neural network with multi-channel information

NASA Astrophysics Data System (ADS)

Mabu, Shingo; Kido, Shoji; Hashimoto, Noriaki; Hirano, Yasushi; Kuremoto, Takashi

2018-02-01

This research proposes a multi-channel deep convolutional neural network (DCNN) for computer-aided diagnosis (CAD) that classifies normal and abnormal opacities of diffuse lung diseases in Computed Tomography (CT) images. Because CT images are gray scale, DCNN usually uses one channel for inputting image data. On the other hand, this research uses multi-channel DCNN where each channel corresponds to the original raw image or the images transformed by some preprocessing techniques. In fact, the information obtained only from raw images is limited and some conventional research suggested that preprocessing of images contributes to improving the classification accuracy. Thus, the combination of the original and preprocessed images is expected to show higher accuracy. The proposed method realizes region of interest (ROI)-based opacity annotation. We used lung CT images taken in Yamaguchi University Hospital, Japan, and they are divided into 32 × 32 ROI images. The ROIs contain six kinds of opacities: consolidation, ground-glass opacity (GGO), emphysema, honeycombing, nodular, and normal. The aim of the proposed method is to classify each ROI into one of the six opacities (classes). The DCNN structure is based on VGG network that secured the first and second places in ImageNet ILSVRC-2014. From the experimental results, the classification accuracy of the proposed method was better than the conventional method with single channel, and there was a significant difference between them.

SamuROI, a Python-Based Software Tool for Visualization and Analysis of Dynamic Time Series Imaging at Multiple Spatial Scales.

PubMed

Rueckl, Martin; Lenzi, Stephen C; Moreno-Velasquez, Laura; Parthier, Daniel; Schmitz, Dietmar; Ruediger, Sten; Johenning, Friedrich W

2017-01-01

The measurement of activity in vivo and in vitro has shifted from electrical to optical methods. While the indicators for imaging activity have improved significantly over the last decade, tools for analysing optical data have not kept pace. Most available analysis tools are limited in their flexibility and applicability to datasets obtained at different spatial scales. Here, we present SamuROI (Structured analysis of multiple user-defined ROIs), an open source Python-based analysis environment for imaging data. SamuROI simplifies exploratory analysis and visualization of image series of fluorescence changes in complex structures over time and is readily applicable at different spatial scales. In this paper, we show the utility of SamuROI in Ca 2+ -imaging based applications at three spatial scales: the micro-scale (i.e., sub-cellular compartments including cell bodies, dendrites and spines); the meso-scale, (i.e., whole cell and population imaging with single-cell resolution); and the macro-scale (i.e., imaging of changes in bulk fluorescence in large brain areas, without cellular resolution). The software described here provides a graphical user interface for intuitive data exploration and region of interest (ROI) management that can be used interactively within Jupyter Notebook: a publicly available interactive Python platform that allows simple integration of our software with existing tools for automated ROI generation and post-processing, as well as custom analysis pipelines. SamuROI software, source code and installation instructions are publicly available on GitHub and documentation is available online. SamuROI reduces the energy barrier for manual exploration and semi-automated analysis of spatially complex Ca 2+ imaging datasets, particularly when these have been acquired at different spatial scales.

SamuROI, a Python-Based Software Tool for Visualization and Analysis of Dynamic Time Series Imaging at Multiple Spatial Scales

PubMed Central

Rueckl, Martin; Lenzi, Stephen C.; Moreno-Velasquez, Laura; Parthier, Daniel; Schmitz, Dietmar; Ruediger, Sten; Johenning, Friedrich W.

2017-01-01

The measurement of activity in vivo and in vitro has shifted from electrical to optical methods. While the indicators for imaging activity have improved significantly over the last decade, tools for analysing optical data have not kept pace. Most available analysis tools are limited in their flexibility and applicability to datasets obtained at different spatial scales. Here, we present SamuROI (Structured analysis of multiple user-defined ROIs), an open source Python-based analysis environment for imaging data. SamuROI simplifies exploratory analysis and visualization of image series of fluorescence changes in complex structures over time and is readily applicable at different spatial scales. In this paper, we show the utility of SamuROI in Ca2+-imaging based applications at three spatial scales: the micro-scale (i.e., sub-cellular compartments including cell bodies, dendrites and spines); the meso-scale, (i.e., whole cell and population imaging with single-cell resolution); and the macro-scale (i.e., imaging of changes in bulk fluorescence in large brain areas, without cellular resolution). The software described here provides a graphical user interface for intuitive data exploration and region of interest (ROI) management that can be used interactively within Jupyter Notebook: a publicly available interactive Python platform that allows simple integration of our software with existing tools for automated ROI generation and post-processing, as well as custom analysis pipelines. SamuROI software, source code and installation instructions are publicly available on GitHub and documentation is available online. SamuROI reduces the energy barrier for manual exploration and semi-automated analysis of spatially complex Ca2+ imaging datasets, particularly when these have been acquired at different spatial scales. PMID:28706482

SU-D-202-03: Statistical Segmentation On Quantitative CT for Assessing Spatial Tumor Response During Radiation Therapy Delivery

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schott, D; Chen, X; Klawikowski, S

2016-06-15

Purpose: Develop a method to segment regions of interest (ROIs) in tumor with statistically similar Hounsfield unit (HU) values and/or HU changes during chemoradiation therapy (CRT) delivery, to assess spatial tumor treatment response based on daily CTs during CRT delivery. Methods: Generate a three region map of ROIs with differential HUs, by sampling neighboring voxels around a selected voxel and comparing to the mean of the entire ROI using a t-test. The cumulative distribution function, P, is calculated from the t-test. The P value is assigned to be the value at the selected voxel, and this is repeated over allmore » voxels in the initial ROI. Three regions are defined as: (1-P) < 0.00001 (mid region), and 0.00001 < (1-P) (mean greater than baseline and mean lower than baseline). The test is then expanded to compare daily CT sets acquired during routine CT-guided RT delivery using a CT-on-rails. The first fraction CT is used as the baseline for comparison. We tested 15 pancreatic head tumor cases undergoing CRT, to identify the ROIs and changes corresponding to normal, fibrotic, and tumor tissue. The obtained ROIs were compared with MRI-ADC maps acquired pre- and post-CRT. Results: The ROIs in 13 out of 15 patients’ first fraction CTs and pre-CRT MRIs matched the general region and slices covered, as well as in 6 out of the 9 patients with post-CRT MRIs. The high HU region designated by the t-test was seen to correlate with the tumor region in MR, and these ROIs are positioned within the same region over the course of treatment. In patients with poorly delineated tumors in MR, the t-test was inconclusive. Conclusion: The proposed statistical segmentation technique shows the potential to identify regions in tumor with differential HUs and HU changes during CRT delivery for patients with pancreas head cancer.« less

Unsupervised classification of cirrhotic livers using MRI data

NASA Astrophysics Data System (ADS)

Lee, Gobert; Kanematsu, Masayuki; Kato, Hiroki; Kondo, Hiroshi; Zhou, Xiangrong; Hara, Takeshi; Fujita, Hiroshi; Hoshi, Hiroaki

2008-03-01

Cirrhosis of the liver is a chronic disease. It is characterized by the presence of widespread nodules and fibrosis in the liver which results in characteristic texture patterns. Computerized analysis of hepatic texture patterns is usually based on regions-of-interest (ROIs). However, not all ROIs are typical representatives of the disease stage of the liver from which the ROIs originated. This leads to uncertainties in the ROI labels (diseased or non-diseased). On the other hand, supervised classifiers are commonly used in determining the assignment rule. This presents a problem as the training of a supervised classifier requires the correct labels of the ROIs. The main purpose of this paper is to investigate the use of an unsupervised classifier, the k-means clustering, in classifying ROI based data. In addition, a procedure for generating a receiver operating characteristic (ROC) curve depicting the classification performance of k-means clustering is also reported. Hepatic MRI images of 44 patients (16 cirrhotic; 28 non-cirrhotic) are used in this study. The MRI data are derived from gadolinium-enhanced equilibrium phase images. For each patient, 10 ROIs selected by an experienced radiologist and 7 texture features measured on each ROI are included in the MRI data. Results of the k-means classifier are depicted using an ROC curve. The area under the curve (AUC) has a value of 0.704. This is slightly lower than but comparable to that of LDA and ANN classifiers which have values 0.781 and 0.801, respectively. Methods in constructing ROC curve in relation to k-means clustering have not been previously reported in the literature.

Dental panoramic image analysis for enhancement biomarker of mandibular condyle for osteoporosis early detection

NASA Astrophysics Data System (ADS)

Suprijanto; Azhari; Juliastuti, E.; Septyvergy, A.; Setyagar, N. P. P.

2016-03-01

Osteoporosis is a degenerative disease characterized by low Bone Mineral Density (BMD). Currently, a BMD level is determined by Dual Energy X-ray Absorptiometry (DXA) at the lumbar vertebrae and femur. Previous studies reported that dental panoramic radiography image has potential information for early osteoporosis detection. This work reported alternative scheme, that consists of the determination of the Region of Interest (ROI) the condyle mandibular in the image as biomarker and feature extraction from ROI and classification of bone conditions. The minimum value of intensity in the cavity area is used to compensate an offset on the ROI. For feature extraction, the fraction of intensity values in the ROI that represent high bone density and the ROI total area is perfomed. The classification will be evaluated from the ability of each feature and its combinations for the BMD detection in 2 classes (normal and abnormal), with the artificial neural network method. The evaluation system used 105 panoramic image data from menopause women which consist of 36 training data and 69 test data that were divided into 2 classes. The 2 classes of classification obtained 88.0% accuracy rate and 88.0% sensitivity rate.

Automatic detection of regions of interest in mammographic images

NASA Astrophysics Data System (ADS)

Cheng, Erkang; Ling, Haibin; Bakic, Predrag R.; Maidment, Andrew D. A.; Megalooikonomou, Vasileios

2011-03-01

This work is a part of our ongoing study aimed at comparing the topology of anatomical branching structures with the underlying image texture. Detection of regions of interest (ROIs) in clinical breast images serves as the first step in development of an automated system for image analysis and breast cancer diagnosis. In this paper, we have investigated machine learning approaches for the task of identifying ROIs with visible breast ductal trees in a given galactographic image. Specifically, we have developed boosting based framework using the AdaBoost algorithm in combination with Haar wavelet features for the ROI detection. Twenty-eight clinical galactograms with expert annotated ROIs were used for training. Positive samples were generated by resampling near the annotated ROIs, and negative samples were generated randomly by image decomposition. Each detected ROI candidate was given a confidences core. Candidate ROIs with spatial overlap were merged and their confidence scores combined. We have compared three strategies for elimination of false positives. The strategies differed in their approach to combining confidence scores by summation, averaging, or selecting the maximum score.. The strategies were compared based upon the spatial overlap with annotated ROIs. Using a 4-fold cross-validation with the annotated clinical galactographic images, the summation strategy showed the best performance with 75% detection rate. When combining the top two candidates, the selection of maximum score showed the best performance with 96% detection rate.

A method to estimate the fractional fat volume within a ROI of a breast biopsy for WAXS applications: Animal tissue evaluation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tang, Robert Y., E-mail: rx-tang@laurentian.ca; McDonald, Nancy, E-mail: mcdnancye@gmail.com; Laamanen, Curtis, E-mail: cx-laamanen@laurentian.ca

Purpose: To develop a method to estimate the mean fractional volume of fat (ν{sup ¯}{sub fat}) within a region of interest (ROI) of a tissue sample for wide-angle x-ray scatter (WAXS) applications. A scatter signal from the ROI was obtained and use of ν{sup ¯}{sub fat} in a WAXS fat subtraction model provided a way to estimate the differential linear scattering coefficient μ{sub s} of the remaining fatless tissue. Methods: The efficacy of the method was tested using animal tissue from a local butcher shop. Formalin fixed samples, 5 mm in diameter 4 mm thick, were prepared. The two mainmore » tissue types were fat and meat (fibrous). Pure as well as composite samples consisting of a mixture of the two tissue types were analyzed. For the latter samples, ν{sub fat} for the tissue columns of interest were extracted from corresponding pixels in CCD digital x-ray images using a calibration curve. The means ν{sup ¯}{sub fat} were then calculated for use in a WAXS fat subtraction model. For the WAXS measurements, the samples were interrogated with a 2.7 mm diameter 50 kV beam and the 6° scattered photons were detected with a CdTe detector subtending a solid angle of 7.75 × 10{sup −5} sr. Using the scatter spectrum, an estimate of the incident spectrum, and a scatter model, μ{sub s} was determined for the tissue in the ROI. For the composite samples, a WAXS fat subtraction model was used to estimate the μ{sub s} of the fibrous tissue in the ROI. This signal was compared to μ{sub s} of fibrous tissue obtained using a pure fibrous sample. Results: For chicken and beef composites, ν{sup ¯}{sub fat}=0.33±0.05 and 0.32 ± 0.05, respectively. The subtractions of these fat components from the WAXS composite signals provided estimates of μ{sub s} for chicken and beef fibrous tissue. The differences between the estimates and μ{sub s} of fibrous obtained with a pure sample were calculated as a function of the momentum transfer x. A t-test showed that the mean of the differences did not vary from zero in a statistically significant way thereby validating the methods. Conclusions: The methodology to estimate ν{sup ¯}{sub fat} in a ROI of a tissue sample via CCD x-ray imaging was quantitatively accurate. The WAXS fat subtraction model allowed μ{sub s} of fibrous tissue to be obtained from a ROI which had some fat. The fat estimation method coupled with the WAXS models can be used to compare μ{sub s} coefficients of fibroglandular and cancerous breast tissue.« less

2D image classification for 3D anatomy localization: employing deep convolutional neural networks

NASA Astrophysics Data System (ADS)

de Vos, Bob D.; Wolterink, Jelmer M.; de Jong, Pim A.; Viergever, Max A.; Išgum, Ivana

2016-03-01

Localization of anatomical regions of interest (ROIs) is a preprocessing step in many medical image analysis tasks. While trivial for humans, it is complex for automatic methods. Classic machine learning approaches require the challenge of hand crafting features to describe differences between ROIs and background. Deep convolutional neural networks (CNNs) alleviate this by automatically finding hierarchical feature representations from raw images. We employ this trait to detect anatomical ROIs in 2D image slices in order to localize them in 3D. In 100 low-dose non-contrast enhanced non-ECG synchronized screening chest CT scans, a reference standard was defined by manually delineating rectangular bounding boxes around three anatomical ROIs -- heart, aortic arch, and descending aorta. Every anatomical ROI was automatically identified using a combination of three CNNs, each analyzing one orthogonal image plane. While single CNNs predicted presence or absence of a specific ROI in the given plane, the combination of their results provided a 3D bounding box around it. Classification performance of each CNN, expressed in area under the receiver operating characteristic curve, was >=0.988. Additionally, the performance of ROI localization was evaluated. Median Dice scores for automatically determined bounding boxes around the heart, aortic arch, and descending aorta were 0.89, 0.70, and 0.85 respectively. The results demonstrate that accurate automatic 3D localization of anatomical structures by CNN-based 2D image classification is feasible.

An Algorithm to Detect the Retinal Region of Interest

NASA Astrophysics Data System (ADS)

Şehirli, E.; Turan, M. K.; Demiral, E.

2017-11-01

Retina is one of the important layers of the eyes, which includes sensitive cells to colour and light and nerve fibers. Retina can be displayed by using some medical devices such as fundus camera, ophthalmoscope. Hence, some lesions like microaneurysm, haemorrhage, exudate with many diseases of the eye can be detected by looking at the images taken by devices. In computer vision and biomedical areas, studies to detect lesions of the eyes automatically have been done for a long time. In order to make automated detections, the concept of ROI may be utilized. ROI which stands for region of interest generally serves the purpose of focusing on particular targets. The main concentration of this paper is the algorithm to automatically detect retinal region of interest belonging to different retinal images on a software application. The algorithm consists of three stages such as pre-processing stage, detecting ROI on processed images and overlapping between input image and obtained ROI of the image.

Influence of region of interest size and ultrasound lesion size on the performance of 2D shear wave elastography (SWE) in solid breast masses.

PubMed

Skerl, K; Vinnicombe, S; Giannotti, E; Thomson, K; Evans, A

2015-12-01

To evaluate the influence of the region of interest (ROI) size and lesion diameter on the diagnostic performance of 2D shear wave elastography (SWE) of solid breast lesions. A study group of 206 consecutive patients (age range 21-92 years) with 210 solid breast lesions (70 benign, 140 malignant) who underwent core biopsy or surgical excision was evaluated. Lesions were divided into small (diameter <15 mm, n=112) and large lesions (diameter ≥15 mm, n=98). An ROI with a diameter of 1, 2, and 3 mm was positioned over the stiffest part of the lesion. The maximum elasticity (Emax), mean elasticity (Emean) and standard deviation (SD) for each ROI size were compared to the pathological outcome. Statistical analysis was undertaken using the chi-square test and receiver operating characteristic (ROC) analysis. The ROI size used has a significant impact on the performance of Emean and SD but not on Emax. Youden's indices show a correlation with the ROI size and lesion size: generally, the benign/malignant threshold is lower with increasing ROI size but higher with increasing lesion size. No single SWE parameter has superior performance. Lesion size and ROI size influence diagnostic performance. Copyright © 2015. Published by Elsevier Ltd.

A voxelwise approach to determine consensus regions-of-interest for the study of brain network plasticity.

PubMed

Rajtmajer, Sarah M; Roy, Arnab; Albert, Reka; Molenaar, Peter C M; Hillary, Frank G

2015-01-01

Despite exciting advances in the functional imaging of the brain, it remains a challenge to define regions of interest (ROIs) that do not require investigator supervision and permit examination of change in networks over time (or plasticity). Plasticity is most readily examined by maintaining ROIs constant via seed-based and anatomical-atlas based techniques, but these approaches are not data-driven, requiring definition based on prior experience (e.g., choice of seed-region, anatomical landmarks). These approaches are limiting especially when functional connectivity may evolve over time in areas that are finer than known anatomical landmarks or in areas outside predetermined seeded regions. An ideal method would permit investigators to study network plasticity due to learning, maturation effects, or clinical recovery via multiple time point data that can be compared to one another in the same ROI while also preserving the voxel-level data in those ROIs at each time point. Data-driven approaches (e.g., whole-brain voxelwise approaches) ameliorate concerns regarding investigator bias, but the fundamental problem of comparing the results between distinct data sets remains. In this paper we propose an approach, aggregate-initialized label propagation (AILP), which allows for data at separate time points to be compared for examining developmental processes resulting in network change (plasticity). To do so, we use a whole-brain modularity approach to parcellate the brain into anatomically constrained functional modules at separate time points and then apply the AILP algorithm to form a consensus set of ROIs for examining change over time. To demonstrate its utility, we make use of a known dataset of individuals with traumatic brain injury sampled at two time points during the first year of recovery and show how the AILP procedure can be applied to select regions of interest to be used in a graph theoretical analysis of plasticity.

Quantitative computer-aided diagnostic algorithm for automated detection of peak lesion attenuation in differentiating clear cell from papillary and chromophobe renal cell carcinoma, oncocytoma, and fat-poor angiomyolipoma on multiphasic multidetector computed tomography.

PubMed

Coy, Heidi; Young, Jonathan R; Douek, Michael L; Brown, Matthew S; Sayre, James; Raman, Steven S

2017-07-01

To evaluate the performance of a novel, quantitative computer-aided diagnostic (CAD) algorithm on four-phase multidetector computed tomography (MDCT) to detect peak lesion attenuation to enable differentiation of clear cell renal cell carcinoma (ccRCC) from chromophobe RCC (chRCC), papillary RCC (pRCC), oncocytoma, and fat-poor angiomyolipoma (fp-AML). We queried our clinical databases to obtain a cohort of histologically proven renal masses with preoperative MDCT with four phases [unenhanced (U), corticomedullary (CM), nephrographic (NP), and excretory (E)]. A whole lesion 3D contour was obtained in all four phases. The CAD algorithm determined a region of interest (ROI) of peak lesion attenuation within the 3D lesion contour. For comparison, a manual ROI was separately placed in the most enhancing portion of the lesion by visual inspection for a reference standard, and in uninvolved renal cortex. Relative lesion attenuation for both CAD and manual methods was obtained by normalizing the CAD peak lesion attenuation ROI (and the reference standard manually placed ROI) to uninvolved renal cortex with the formula [(peak lesion attenuation ROI - cortex ROI)/cortex ROI] × 100%. ROC analysis and area under the curve (AUC) were used to assess diagnostic performance. Bland-Altman analysis was used to compare peak ROI between CAD and manual method. The study cohort comprised 200 patients with 200 unique renal masses: 106 (53%) ccRCC, 32 (16%) oncocytomas, 18 (9%) chRCCs, 34 (17%) pRCCs, and 10 (5%) fp-AMLs. In the CM phase, CAD-derived ROI enabled characterization of ccRCC from chRCC, pRCC, oncocytoma, and fp-AML with AUCs of 0.850 (95% CI 0.732-0.968), 0.959 (95% CI 0.930-0.989), 0.792 (95% CI 0.716-0.869), and 0.825 (95% CI 0.703-0.948), respectively. On Bland-Altman analysis, there was excellent agreement of CAD and manual methods with mean differences between 14 and 26 HU in each phase. A novel, quantitative CAD algorithm enabled robust peak HU lesion detection and discrimination of ccRCC from other renal lesions with similar performance compared to the manual method.

Finger crease pattern recognition using Legendre moments and principal component analysis

NASA Astrophysics Data System (ADS)

Luo, Rongfang; Lin, Tusheng

2007-03-01

The finger joint lines defined as finger creases and its distribution can identify a person. In this paper, we propose a new finger crease pattern recognition method based on Legendre moments and principal component analysis (PCA). After obtaining the region of interest (ROI) for each finger image in the pre-processing stage, Legendre moments under Radon transform are applied to construct a moment feature matrix from the ROI, which greatly decreases the dimensionality of ROI and can represent principal components of the finger creases quite well. Then, an approach to finger crease pattern recognition is designed based on Karhunen-Loeve (K-L) transform. The method applies PCA to a moment feature matrix rather than the original image matrix to achieve the feature vector. The proposed method has been tested on a database of 824 images from 103 individuals using the nearest neighbor classifier. The accuracy up to 98.584% has been obtained when using 4 samples per class for training. The experimental results demonstrate that our proposed approach is feasible and effective in biometrics.

QR-on-a-chip: a computer-recognizable micro-pattern engraved microfluidic device for high-throughput image acquisition.

PubMed

Yun, Kyungwon; Lee, Hyunjae; Bang, Hyunwoo; Jeon, Noo Li

2016-02-21

This study proposes a novel way to achieve high-throughput image acquisition based on a computer-recognizable micro-pattern implemented on a microfluidic device. We integrated the QR code, a two-dimensional barcode system, onto the microfluidic device to simplify imaging of multiple ROIs (regions of interest). A standard QR code pattern was modified to arrays of cylindrical structures of polydimethylsiloxane (PDMS). Utilizing the recognition of the micro-pattern, the proposed system enables: (1) device identification, which allows referencing additional information of the device, such as device imaging sequences or the ROIs and (2) composing a coordinate system for an arbitrarily located microfluidic device with respect to the stage. Based on these functionalities, the proposed method performs one-step high-throughput imaging for data acquisition in microfluidic devices without further manual exploration and locating of the desired ROIs. In our experience, the proposed method significantly reduced the time for the preparation of an acquisition. We expect that the method will innovatively improve the prototype device data acquisition and analysis.

High-performance Chinese multiclass traffic sign detection via coarse-to-fine cascade and parallel support vector machine detectors

NASA Astrophysics Data System (ADS)

Chang, Faliang; Liu, Chunsheng

2017-09-01

The high variability of sign colors and shapes in uncontrolled environments has made the detection of traffic signs a challenging problem in computer vision. We propose a traffic sign detection (TSD) method based on coarse-to-fine cascade and parallel support vector machine (SVM) detectors to detect Chinese warning and danger traffic signs. First, a region of interest (ROI) extraction method is proposed to extract ROIs using color contrast features in local regions. The ROI extraction can reduce scanning regions and save detection time. For multiclass TSD, we propose a structure that combines a coarse-to-fine cascaded tree with a parallel structure of histogram of oriented gradients (HOG) + SVM detectors. The cascaded tree is designed to detect different types of traffic signs in a coarse-to-fine process. The parallel HOG + SVM detectors are designed to do fine detection of different types of traffic signs. The experiments demonstrate the proposed TSD method can rapidly detect multiclass traffic signs with different colors and shapes in high accuracy.

Monocular Vision-Based Underwater Object Detection

PubMed Central

Zhang, Zhen; Dai, Fengzhao; Bu, Yang; Wang, Huibin

2017-01-01

In this paper, we propose an underwater object detection method using monocular vision sensors. In addition to commonly used visual features such as color and intensity, we investigate the potential of underwater object detection using light transmission information. The global contrast of various features is used to initially identify the region of interest (ROI), which is then filtered by the image segmentation method, producing the final underwater object detection results. We test the performance of our method with diverse underwater datasets. Samples of the datasets are acquired by a monocular camera with different qualities (such as resolution and focal length) and setups (viewing distance, viewing angle, and optical environment). It is demonstrated that our ROI detection method is necessary and can largely remove the background noise and significantly increase the accuracy of our underwater object detection method. PMID:28771194

Testing of a Composite Wavelet Filter to Enhance Automated Target Recognition in SONAR

NASA Technical Reports Server (NTRS)

Chiang, Jeffrey N.

2011-01-01

Automated Target Recognition (ATR) systems aim to automate target detection, recognition, and tracking. The current project applies a JPL ATR system to low resolution SONAR and camera videos taken from Unmanned Underwater Vehicles (UUVs). These SONAR images are inherently noisy and difficult to interpret, and pictures taken underwater are unreliable due to murkiness and inconsistent lighting. The ATR system breaks target recognition into three stages: 1) Videos of both SONAR and camera footage are broken into frames and preprocessed to enhance images and detect Regions of Interest (ROIs). 2) Features are extracted from these ROIs in preparation for classification. 3) ROIs are classified as true or false positives using a standard Neural Network based on the extracted features. Several preprocessing, feature extraction, and training methods are tested and discussed in this report.

Optimization of Region of Interest Drawing for Quantitative Analysis: Differentiation Between Benign and Malignant Breast Lesions on Contrast-Enhanced Sonography.

PubMed

Nakata, Norio; Ohta, Tomoyuki; Nishioka, Makiko; Takeyama, Hiroshi; Toriumi, Yasuo; Kato, Kumiko; Nogi, Hiroko; Kamio, Makiko; Fukuda, Kunihiko

2015-11-01

This study was performed to evaluate the diagnostic utility of quantitative analysis of benign and malignant breast lesions using contrast-enhanced sonography. Contrast-enhanced sonography using the perflubutane-based contrast agent Sonazoid (Daiichi Sankyo, Tokyo, Japan) was performed in 94 pathologically proven palpable breast mass lesions, which could be depicted with B-mode sonography. Quantitative analyses using the time-intensity curve on contrast-enhanced sonography were performed in 5 region of interest (ROI) types (manually traced ROI and circular ROIs of 5, 10, 15, and 20 mm in diameter). The peak signal intensity, initial slope, time to peak, positive enhancement integral, and wash-out ratio were investigated in each ROI. There were significant differences between benign and malignant lesions in the time to peak (P < .05), initial slope (P < .001), and positive enhancement integral (P < .05) for the manual ROI. Significant differences were found between benign and malignant lesions in the time to peak (P < .05) for the 5-mm ROI; the time to peak (P < .05) and initial slope (P< .05) for the 10-mm ROI; absolute values of the peak signal intensity (P< .05), time to peak (P< .01), and initial slope (P< .005) for the 15-mm ROI; and the time to peak (P < .05) and initial slope (P < .05) for the 20-mm ROI. There were no statistically significant differences in any wash-out ratio values for the 5 ROI types. Kinetic analysis using contrast-enhanced sonography is useful for differentiation between benign and malignant breast lesions. © 2015 by the American Institute of Ultrasound in Medicine.

Definition of the thermographic regions of interest in cycling by using a factor analysis

NASA Astrophysics Data System (ADS)

Priego Quesada, Jose Ignacio; Lucas-Cuevas, Angel Gabriel; Salvador Palmer, Rosario; Pérez-Soriano, Pedro; Cibrián Ortiz de Anda, Rosa M.^a.

2016-03-01

Research in exercise physiology using infrared thermography has increased in the last years. However, the definition of the Regions of Interest (ROIs) varies strongly between studies. Therefore, the aim of this study was to use a factor analysis approach to define highly correlated groups of thermographic ROIs during a cycling test. Factor analyses were performed based on the moment of measurement and on the variation of skin temperatures as a result of the cycling exercise. 19 male participants cycled during 45 min at 50% of their individual peak power output with a cadence of 90 rpm. Infrared thermography was used to measure skin temperatures in sixteen ROIs of the trunk and lower limbs at three moments: before, immediately after and 10 min after the cycling test. Factor analyses were used to identify groups of ROIs based on the skin absolute temperatures at each moment of measurement as well as on skin temperature variations between moments. All the factor analyses performed for each moment and skin temperature variation explained more than the 80% of the variance. Different groups of ROIs were obtained when the analysis was based on the moment of measurement or on the effect of exercise on the skin temperature. Furthermore, some ROIs were grouped in the same way in both analyses (e.g. the ROIs of the trunk), whereas other regions (legs and their joints) were grouped differently in each analysis. Differences between groups of ROIs are related to their tissue composition, muscular activity and capacity of sweating. In conclusion, the resultant groups of ROIs were coherent and could help researchers to define the ROIs in future thermal studies.

Open versus percutaneous instrumentation in thoracolumbar fractures: magnetic resonance imaging comparison of paravertebral muscles after implant removal.

PubMed

Ntilikina, Yves; Bahlau, David; Garnon, Julien; Schuller, Sébastien; Walter, Axel; Schaeffer, Mickaël; Steib, Jean-Paul; Charles, Yann Philippe

2017-08-01

OBJECTIVE Percutaneous instrumentation in thoracolumbar fractures is intended to decrease paravertebral muscle damage by avoiding dissection. The aim of this study was to compare muscles at instrumented levels in patients who were treated by open or percutaneous surgery. METHODS Twenty-seven patients underwent open instrumentation, and 65 were treated percutaneously. A standardized MRI protocol using axial T1-weighted sequences was performed at a minimum 1-year follow-up after implant removal. Two independent observers measured cross-sectional areas (CSAs, in cm 2 ) and region of interest (ROI) signal intensity (in pixels) of paravertebral muscles by using OsiriX at the fracture level, and at cranial and caudal instrumented pedicle levels. An interobserver comparison was made using the Bland-Altman method. Reference ROI muscle was assessed in the psoas and ROI fat subcutaneously. The ratio ROI-CSA/ROI-fat was compared for patients treated with open versus percutaneous procedures by using a linear mixed model. A linear regression analyzed additional factors: age, sex, body mass index (BMI), Pfirrmann grade of adjacent discs, and duration of instrumentation in situ. RESULTS The interobserver agreement was good for all CSAs. The average CSA for the entire spine was 15.7 cm 2 in the open surgery group and 18.5 cm 2 in the percutaneous group (p = 0.0234). The average ROI-fat and ROI-muscle signal intensities were comparable: 497.1 versus 483.9 pixels for ROI-fat and 120.4 versus 111.7 pixels for ROI-muscle in open versus percutaneous groups. The ROI-CSA varied between 154 and 226 for open, and between 154 and 195 for percutaneous procedures, depending on instrumented levels. A significant difference of the ROI-CSA/ROI-fat ratio (0.4 vs 0.3) was present at fracture levels T12-L1 (p = 0.0329) and at adjacent cranial (p = 0.0139) and caudal (p = 0.0100) instrumented levels. Differences were not significant at thoracic levels. When adjusting based on age, BMI, and Pfirrmann grade, a significant difference between open and percutaneous procedures regarding the ROI-CSA/ROI-fat ratio was present in the lumbar spine (p < 0.01). Sex and duration of instrumentation had no significant influence. CONCLUSIONS Percutaneous instrumentation decreased muscle atrophy compared with open surgery. The MRI signal differences for T-12 and L-1 fractures indicated less fat infiltration within CSAs in patients who received percutaneous treatment. Differences were not evidenced at thoracic levels, where CSAs were smaller. Fat infiltration was not significantly different at lumbar levels with either procedure in elderly patients with associated discopathy and higher BMI. In younger patients, there was less fat infiltration of lumbar paravertebral muscles with percutaneous procedures.

A longitudinal magnetic resonance elastography study of murine brain tumors following radiation therapy

NASA Astrophysics Data System (ADS)

Feng, Y.; Clayton, E. H.; Okamoto, R. J.; Engelbach, J.; Bayly, P. V.; Garbow, J. R.

2016-08-01

An accurate and noninvasive method for assessing treatment response following radiotherapy is needed for both treatment monitoring and planning. Measurement of solid tumor volume alone is not sufficient for reliable early detection of therapeutic response, since changes in physiological and/or biomechanical properties can precede tumor volume change following therapy. In this study, we use magnetic resonance elastography to evaluate the treatment effect after radiotherapy in a murine brain tumor model. Shear modulus was calculated and compared between the delineated tumor region of interest (ROI) and its contralateral, mirrored counterpart. We also compared the shear modulus from both the irradiated and non-irradiated tumor and mirror ROIs longitudinally, sampling four time points spanning 9-19 d post tumor implant. Results showed that the tumor ROI had a lower shear modulus than that of the mirror ROI, independent of radiation. The shear modulus of the tumor ROI decreased over time for both the treated and untreated groups. By contrast, the shear modulus of the mirror ROI appeared to be relatively constant for the treated group, while an increasing trend was observed for the untreated group. The results provide insights into the tumor properties after radiation treatment and demonstrate the potential of using the mechanical properties of the tumor as a biomarker. In future studies, more closely spaced time points will be employed for detailed analysis of the radiation effect.

Comparison of demons deformable registration-based methods for texture analysis of serial thoracic CT scans

NASA Astrophysics Data System (ADS)

Cunliffe, Alexandra R.; Al-Hallaq, Hania A.; Fei, Xianhan M.; Tuohy, Rachel E.; Armato, Samuel G.

2013-02-01

To determine how 19 image texture features may be altered by three image registration methods, "normal" baseline and follow-up computed tomography (CT) scans from 27 patients were analyzed. Nineteen texture feature values were calculated in over 1,000 32x32-pixel regions of interest (ROIs) randomly placed in each baseline scan. All three methods used demons registration to map baseline scan ROIs to anatomically matched locations in the corresponding transformed follow-up scan. For the first method, the follow-up scan transformation was subsampled to achieve a voxel size identical to that of the baseline scan. For the second method, the follow-up scan was transformed through affine registration to achieve global alignment with the baseline scan. For the third method, the follow-up scan was directly deformed to the baseline scan using demons deformable registration. Feature values in matched ROIs were compared using Bland- Altman 95% limits of agreement. For each feature, the range spanned by the 95% limits was normalized to the mean feature value to obtain the normalized range of agreement, nRoA. Wilcoxon signed-rank tests were used to compare nRoA values across features for the three methods. Significance for individual tests was adjusted using the Bonferroni method. nRoA was significantly smaller for affine-registered scans than for the resampled scans (p=0.003), indicating lower feature value variability between baseline and follow-up scan ROIs using this method. For both of these methods, however, nRoA was significantly higher than when feature values were calculated directly on demons-deformed followup scans (p<0.001). Across features and methods, nRoA values remained below 26%.

Dosimetry and prescription in liver radioembolization with 90Y microspheres: 3D calculation of tumor-to-liver ratio from global 99mTc-MAA SPECT information

NASA Astrophysics Data System (ADS)

Mañeru, Fernando; Abós, Dolores; Bragado, Laura; Fuentemilla, Naiara; Caudepón, Fernando; Pellejero, Santiago; Miquelez, Santiago; Rubio, Anastasio; Goñi, Elena; Hernández-Vitoria, Araceli

2017-12-01

Dosimetry in liver radioembolization with 90Y microspheres is a fundamental tool, both for the optimization of each treatment and for improving knowledge of the treatment effects in the tissues. Different options are available for estimating the administered activity and the tumor/organ dose, among them the so-called partition method. The key factor in the partition method is the tumor/normal tissue activity uptake ratio (T/N), which is obtained by a single-photon emission computed tomography (SPECT) scan during a pre-treatment simulation. The less clear the distinction between healthy and tumor parenchyma within the liver, the more difficult it becomes to estimate the T/N ratio; therefore the use of the method is limited. This study presents a methodology to calculate the T/N ratio using global information from the SPECT. The T/N ratio is estimated by establishing uptake thresholds consistent with previously performed volumetry. This dose calculation method was validated against 3D voxel dosimetry, and was also compared with the standard partition method based on freehand regions of interest (ROI) outlining on SPECT slices. Both comparisons were done on a sample of 20 actual cases of hepatocellular carcinoma treated with resin microspheres. The proposed method and the voxel dosimetry method yield similar results, while the ROI-based method tends to over-estimate the dose to normal tissues. In addition, the variability associated with the ROI-based method is more extreme than the other methods. The proposed method is simpler than either the ROI or voxel dosimetry approaches and avoids the subjectivity associated with the manual selection of regions.

PDE based scheme for multi-modal medical image watermarking.

PubMed

Aherrahrou, N; Tairi, H

2015-11-25

This work deals with copyright protection of digital images, an issue that needs protection of intellectual property rights. It is an important issue with a large number of medical images interchanged on the Internet every day. So, it is a challenging task to ensure the integrity of received images as well as authenticity. Digital watermarking techniques have been proposed as valid solution for this problem. It is worth mentioning that the Region Of Interest (ROI)/Region Of Non Interest (RONI) selection can be seen as a significant limitation from which suffers most of ROI/RONI based watermarking schemes and that in turn affects and limit their applicability in an effective way. Generally, the ROI/RONI is defined by a radiologist or a computer-aided selection tool. And thus, this will not be efficient for an institute or health care system, where one has to process a large number of images. Therefore, developing an automatic ROI/RONI selection is a challenge task. The major aim of this work is to develop an automatic selection algorithm of embedding region based on the so called Partial Differential Equation (PDE) method. Thus avoiding ROI/RONI selection problems including: (1) computational overhead, (2) time consuming, and (3) modality dependent selection. The algorithm is evaluated in terms of imperceptibility, robustness, tamper localization and recovery using MRI, Ultrasound, CT and X-ray grey scale medical images. From experimental results that we have conducted on a database of 100 medical images of four modalities, it can be inferred that our method can achieve high imperceptibility, while showing good robustness against attacks. Furthermore, the experiment results confirm the effectiveness of the proposed algorithm in detecting and recovering the various types of tampering. The highest PSNR value reached over the 100 images is 94,746 dB, while the lowest PSNR value is 60,1272 dB, which demonstrates the higher imperceptibility nature of the proposed method. Moreover, the Normalized Correlation (NC) between the original watermark and the corresponding extracted watermark for 100 images is computed. We get a NC value greater than or equal to 0.998. This indicates that the extracted watermark is very similar to the original watermark for all modalities. The key features of our proposed method are to (1) increase the robustness of the watermark against attacks; (2) provide more transparency to the embedded watermark. (3) provide more authenticity and integrity protection of the content of medical images. (4) provide minimum ROI/RONI selection complexity.

Optimization of focality and direction in dense electrode array transcranial direct current stimulation (tDCS)

NASA Astrophysics Data System (ADS)

Guler, Seyhmus; Dannhauer, Moritz; Erem, Burak; Macleod, Rob; Tucker, Don; Turovets, Sergei; Luu, Phan; Erdogmus, Deniz; Brooks, Dana H.

2016-06-01

Objective. Transcranial direct current stimulation (tDCS) aims to alter brain function non-invasively via electrodes placed on the scalp. Conventional tDCS uses two relatively large patch electrodes to deliver electrical current to the brain region of interest (ROI). Recent studies have shown that using dense arrays containing up to 512 smaller electrodes may increase the precision of targeting ROIs. However, this creates a need for methods to determine effective and safe stimulus patterns as the number of degrees of freedom is much higher with such arrays. Several approaches to this problem have appeared in the literature. In this paper, we describe a new method for calculating optimal electrode stimulus patterns for targeted and directional modulation in dense array tDCS which differs in some important aspects with methods reported to date. Approach. We optimize stimulus pattern of dense arrays with fixed electrode placement to maximize the current density in a particular direction in the ROI. We impose a flexible set of safety constraints on the current power in the brain, individual electrode currents, and total injected current, to protect subject safety. The proposed optimization problem is convex and thus efficiently solved using existing optimization software to find unique and globally optimal electrode stimulus patterns. Main results. Solutions for four anatomical ROIs based on a realistic head model are shown as exemplary results. To illustrate the differences between our approach and previously introduced methods, we compare our method with two of the other leading methods in the literature. We also report on extensive simulations that show the effect of the values chosen for each proposed safety constraint bound on the optimized stimulus patterns. Significance. The proposed optimization approach employs volume based ROIs, easily adapts to different sets of safety constraints, and takes negligible time to compute. An in-depth comparison study gives insight into the relationship between different objective criteria and optimized stimulus patterns. In addition, the analysis of the interaction between optimized stimulus patterns and safety constraint bounds suggests that more precise current localization in the ROI, with improved safety criterion, may be achieved by careful selection of the constraint bounds.

panelcn.MOPS: Copy-number detection in targeted NGS panel data for clinical diagnostics.

PubMed

Povysil, Gundula; Tzika, Antigoni; Vogt, Julia; Haunschmid, Verena; Messiaen, Ludwine; Zschocke, Johannes; Klambauer, Günter; Hochreiter, Sepp; Wimmer, Katharina

2017-07-01

Targeted next-generation-sequencing (NGS) panels have largely replaced Sanger sequencing in clinical diagnostics. They allow for the detection of copy-number variations (CNVs) in addition to single-nucleotide variants and small insertions/deletions. However, existing computational CNV detection methods have shortcomings regarding accuracy, quality control (QC), incidental findings, and user-friendliness. We developed panelcn.MOPS, a novel pipeline for detecting CNVs in targeted NGS panel data. Using data from 180 samples, we compared panelcn.MOPS with five state-of-the-art methods. With panelcn.MOPS leading the field, most methods achieved comparably high accuracy. panelcn.MOPS reliably detected CNVs ranging in size from part of a region of interest (ROI), to whole genes, which may comprise all ROIs investigated in a given sample. The latter is enabled by analyzing reads from all ROIs of the panel, but presenting results exclusively for user-selected genes, thus avoiding incidental findings. Additionally, panelcn.MOPS offers QC criteria not only for samples, but also for individual ROIs within a sample, which increases the confidence in called CNVs. panelcn.MOPS is freely available both as R package and standalone software with graphical user interface that is easy to use for clinical geneticists without any programming experience. panelcn.MOPS combines high sensitivity and specificity with user-friendliness rendering it highly suitable for routine clinical diagnostics. © 2017 The Authors. Human Mutation published by Wiley Periodicals, Inc.

panelcn.MOPS: Copy‐number detection in targeted NGS panel data for clinical diagnostics

PubMed Central

Povysil, Gundula; Tzika, Antigoni; Vogt, Julia; Haunschmid, Verena; Messiaen, Ludwine; Zschocke, Johannes; Klambauer, Günter; Wimmer, Katharina

2017-01-01

Abstract Targeted next‐generation‐sequencing (NGS) panels have largely replaced Sanger sequencing in clinical diagnostics. They allow for the detection of copy‐number variations (CNVs) in addition to single‐nucleotide variants and small insertions/deletions. However, existing computational CNV detection methods have shortcomings regarding accuracy, quality control (QC), incidental findings, and user‐friendliness. We developed panelcn.MOPS, a novel pipeline for detecting CNVs in targeted NGS panel data. Using data from 180 samples, we compared panelcn.MOPS with five state‐of‐the‐art methods. With panelcn.MOPS leading the field, most methods achieved comparably high accuracy. panelcn.MOPS reliably detected CNVs ranging in size from part of a region of interest (ROI), to whole genes, which may comprise all ROIs investigated in a given sample. The latter is enabled by analyzing reads from all ROIs of the panel, but presenting results exclusively for user‐selected genes, thus avoiding incidental findings. Additionally, panelcn.MOPS offers QC criteria not only for samples, but also for individual ROIs within a sample, which increases the confidence in called CNVs. panelcn.MOPS is freely available both as R package and standalone software with graphical user interface that is easy to use for clinical geneticists without any programming experience. panelcn.MOPS combines high sensitivity and specificity with user‐friendliness rendering it highly suitable for routine clinical diagnostics. PMID:28449315

Improvement of Reliability of Diffusion Tensor Metrics in Thigh Skeletal Muscles.

PubMed

Keller, Sarah; Chhabra, Avneesh; Ahmed, Shaheen; Kim, Anne C; Chia, Jonathan M; Yamamura, Jin; Wang, Zhiyue J

2018-05-01

Quantitative diffusion tensor imaging (DTI) of skeletal muscles is challenging due to the bias in DTI metrics, such as fractional anisotropy (FA) and mean diffusivity (MD), related to insufficient signal-to-noise ratio (SNR). This study compares the bias of DTI metrics in skeletal muscles via pixel-based and region-of-interest (ROI)-based analysis. DTI of the thigh muscles was conducted on a 3.0-T system in N = 11 volunteers using a fat-suppressed single-shot spin-echo echo planar imaging (SS SE-EPI) sequence with eight repetitions (number of signal averages (NSA) = 4 or 8 for each repeat). The SNR was calculated for different NSAs and estimated for the composite images combining all data (effective NSA = 48) as standard reference. The bias of MD and FA derived by pixel-based and ROI-based quantification were compared at different NSAs. An "intra-ROI diffusion direction dispersion angle (IRDDDA)" was calculated to assess the uniformity of diffusion within the ROI. Using our standard reference image with NSA = 48, the ROI-based and pixel-based measurements agreed for FA and MD. Larger disagreements were observed for the pixel-based quantification at NSA = 4. MD was less sensitive than FA to the noise level. The IRDDDA decreased with higher NSA. At NSA = 4, ROI-based FA showed a lower average bias (0.9% vs. 37.4%) and narrower 95% limits of agreement compared to the pixel-based method. The ROI-based estimation of FA is less prone to bias than the pixel-based estimations when SNR is low. The IRDDDA can be applied as a quantitative quality measure to assess reliability of ROI-based DTI metrics. Copyright © 2018 Elsevier B.V. All rights reserved.

Automated retinal vessel type classification in color fundus images

NASA Astrophysics Data System (ADS)

Yu, H.; Barriga, S.; Agurto, C.; Nemeth, S.; Bauman, W.; Soliz, P.

2013-02-01

Automated retinal vessel type classification is an essential first step toward machine-based quantitative measurement of various vessel topological parameters and identifying vessel abnormalities and alternations in cardiovascular disease risk analysis. This paper presents a new and accurate automatic artery and vein classification method developed for arteriolar-to-venular width ratio (AVR) and artery and vein tortuosity measurements in regions of interest (ROI) of 1.5 and 2.5 optic disc diameters from the disc center, respectively. This method includes illumination normalization, automatic optic disc detection and retinal vessel segmentation, feature extraction, and a partial least squares (PLS) classification. Normalized multi-color information, color variation, and multi-scale morphological features are extracted on each vessel segment. We trained the algorithm on a set of 51 color fundus images using manually marked arteries and veins. We tested the proposed method in a previously unseen test data set consisting of 42 images. We obtained an area under the ROC curve (AUC) of 93.7% in the ROI of AVR measurement and 91.5% of AUC in the ROI of tortuosity measurement. The proposed AV classification method has the potential to assist automatic cardiovascular disease early detection and risk analysis.

Development of WRF-ROI system by incorporating eigen-decomposition

NASA Astrophysics Data System (ADS)

Kim, S.; Noh, N.; Song, H.; Lim, G.

2011-12-01

This study presents the development of WRF-ROI system, which is the implementation of Retrospective Optimal Interpolation (ROI) to the Weather Research and Forecasting model (WRF). ROI is a new data assimilation algorithm introduced by Song et al. (2009) and Song and Lim (2009). The formulation of ROI is similar with that of Optimal Interpolation (OI), but ROI iteratively assimilates an observation set at a post analysis time into a prior analysis, possibly providing the high quality reanalysis data. ROI method assimilates the data at post analysis time using perturbation method (Errico and Raeder, 1999) without adjoint model. In previous study, ROI method is applied to Lorenz 40-variable model (Lorenz, 1996) to validate the algorithm and to investigate the capability. It is therefore required to apply this ROI method into a more realistic and complicated model framework such as WRF. In this research, the reduced-rank formulation of ROI is used instead of a reduced-resolution method. The computational costs can be reduced due to the eigen-decomposition of background error covariance in the reduced-rank method. When single profile of observations is assimilated in the WRF-ROI system by incorporating eigen-decomposition, the analysis error tends to be reduced if compared with the background error. The difference between forecast errors with assimilation and without assimilation is obviously increased as time passed, which means the improvement of forecast error by assimilation.

T2* Mapping Provides Information That Is Statistically Comparable to an Arthroscopic Evaluation of Acetabular Cartilage.

PubMed

Morgan, Patrick; Nissi, Mikko J; Hughes, John; Mortazavi, Shabnam; Ellerman, Jutta

2017-07-01

Objectives The purpose of this study was to validate T2* mapping as an objective, noninvasive method for the prediction of acetabular cartilage damage. Methods This is the second step in the validation of T2*. In a previous study, we established a quantitative predictive model for identifying and grading acetabular cartilage damage. In this study, the model was applied to a second cohort of 27 consecutive hips to validate the model. A clinical 3.0-T imaging protocol with T2* mapping was used. Acetabular regions of interest (ROI) were identified on magnetic resonance and graded using the previously established model. Each ROI was then graded in a blinded fashion by arthroscopy. Accurate surgical location of ROIs was facilitated with a 2-dimensional map projection of the acetabulum. A total of 459 ROIs were studied. Results When T2* mapping and arthroscopic assessment were compared, 82% of ROIs were within 1 Beck group (of a total 6 possible) and 32% of ROIs were classified identically. Disease prediction based on receiver operating characteristic curve analysis demonstrated a sensitivity of 0.713 and a specificity of 0.804. Model stability evaluation required no significant changes to the predictive model produced in the initial study. Conclusions These results validate that T2* mapping provides statistically comparable information regarding acetabular cartilage when compared to arthroscopy. In contrast to arthroscopy, T2* mapping is quantitative, noninvasive, and can be used in follow-up. Unlike research quantitative magnetic resonance protocols, T2* takes little time and does not require a contrast agent. This may facilitate its use in the clinical sphere.

A combination of spatial and recursive temporal filtering for noise reduction when using region of interest (ROI) fluoroscopy for patient dose reduction in image guided vascular interventions with significant anatomical motion

NASA Astrophysics Data System (ADS)

Setlur Nagesh, S. V.; Khobragade, P.; Ionita, C.; Bednarek, D. R.; Rudin, S.

2015-03-01

Because x-ray based image-guided vascular interventions are minimally invasive they are currently the most preferred method of treating disorders such as stroke, arterial stenosis, and aneurysms; however, the x-ray exposure to the patient during long image-guided interventional procedures could cause harmful effects such as cancer in the long run and even tissue damage in the short term. ROI fluoroscopy reduces patient dose by differentially attenuating the incident x-rays outside the region-of-interest. To reduce the noise in the dose-reduced regions previously recursive temporal filtering was successfully demonstrated for neurovascular interventions. However, in cardiac interventions, anatomical motion is significant and excessive recursive filtering could cause blur. In this work the effects of three noise-reduction schemes, including recursive temporal filtering, spatial mean filtering, and a combination of spatial and recursive temporal filtering, were investigated in a simulated ROI dose-reduced cardiac intervention. First a model to simulate the aortic arch and its movement was built. A coronary stent was used to simulate a bioprosthetic valve used in TAVR procedures and was deployed under dose-reduced ROI fluoroscopy during the simulated heart motion. The images were then retrospectively processed for noise reduction in the periphery, using recursive temporal filtering, spatial filtering and a combination of both. Quantitative metrics for all three noise reduction schemes are calculated and are presented as results. From these it can be concluded that with significant anatomical motion, a combination of spatial and recursive temporal filtering scheme is best suited for reducing the excess quantum noise in the periphery. This new noise-reduction technique in combination with ROI fluoroscopy has the potential for substantial patient-dose savings in cardiac interventions.

Half-Fan-Based Intensity-Weighted Region-of-Interest Imaging for Low-Dose Cone-Beam CT in Image-Guided Radiation Therapy.

PubMed

Yoo, Boyeol; Son, Kihong; Pua, Rizza; Kim, Jinsung; Solodov, Alexander; Cho, Seungryong

2016-10-01

With the increased use of computed tomography (CT) in clinics, dose reduction is the most important feature people seek when considering new CT techniques or applications. We developed an intensity-weighted region-of-interest (IWROI) imaging method in an exact half-fan geometry to reduce the imaging radiation dose to patients in cone-beam CT (CBCT) for image-guided radiation therapy (IGRT). While dose reduction is highly desirable, preserving the high-quality images of the ROI is also important for target localization in IGRT. An intensity-weighting (IW) filter made of copper was mounted in place of a bowtie filter on the X-ray tube unit of an on-board imager (OBI) system such that the filter can substantially reduce radiation exposure to the outer ROI. In addition to mounting the IW filter, the lead-blade collimation of the OBI was adjusted to produce an exact half-fan scanning geometry for a further reduction of the radiation dose. The chord-based rebinned backprojection-filtration (BPF) algorithm in circular CBCT was implemented for image reconstruction, and a humanoid pelvis phantom was used for the IWROI imaging experiment. The IWROI image of the phantom was successfully reconstructed after beam-quality correction, and it was registered to the reference image within an acceptable level of tolerance. Dosimetric measurements revealed that the dose is reduced by approximately 61% in the inner ROI and by 73% in the outer ROI compared to the conventional bowtie filter-based half-fan scan. The IWROI method substantially reduces the imaging radiation dose and provides reconstructed images with an acceptable level of quality for patient setup and target localization. The proposed half-fan-based IWROI imaging technique can add a valuable option to CBCT in IGRT applications.

Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography.

PubMed

Samala, Ravi K; Chan, Heang-Ping; Hadjiiski, Lubomir; Helvie, Mark A; Wei, Jun; Cha, Kenny

2016-12-01

Develop a computer-aided detection (CAD) system for masses in digital breast tomosynthesis (DBT) volume using a deep convolutional neural network (DCNN) with transfer learning from mammograms. A data set containing 2282 digitized film and digital mammograms and 324 DBT volumes were collected with IRB approval. The mass of interest on the images was marked by an experienced breast radiologist as reference standard. The data set was partitioned into a training set (2282 mammograms with 2461 masses and 230 DBT views with 228 masses) and an independent test set (94 DBT views with 89 masses). For DCNN training, the region of interest (ROI) containing the mass (true positive) was extracted from each image. False positive (FP) ROIs were identified at prescreening by their previously developed CAD systems. After data augmentation, a total of 45 072 mammographic ROIs and 37 450 DBT ROIs were obtained. Data normalization and reduction of non-uniformity in the ROIs across heterogeneous data was achieved using a background correction method applied to each ROI. A DCNN with four convolutional layers and three fully connected (FC) layers was first trained on the mammography data. Jittering and dropout techniques were used to reduce overfitting. After training with the mammographic ROIs, all weights in the first three convolutional layers were frozen, and only the last convolution layer and the FC layers were randomly initialized again and trained using the DBT training ROIs. The authors compared the performances of two CAD systems for mass detection in DBT: one used the DCNN-based approach and the other used their previously developed feature-based approach for FP reduction. The prescreening stage was identical in both systems, passing the same set of mass candidates to the FP reduction stage. For the feature-based CAD system, 3D clustering and active contour method was used for segmentation; morphological, gray level, and texture features were extracted and merged with a linear discriminant classifier to score the detected masses. For the DCNN-based CAD system, ROIs from five consecutive slices centered at each candidate were passed through the trained DCNN and a mass likelihood score was generated. The performances of the CAD systems were evaluated using free-response ROC curves and the performance difference was analyzed using a non-parametric method. Before transfer learning, the DCNN trained only on mammograms with an AUC of 0.99 classified DBT masses with an AUC of 0.81 in the DBT training set. After transfer learning with DBT, the AUC improved to 0.90. For breast-based CAD detection in the test set, the sensitivity for the feature-based and the DCNN-based CAD systems was 83% and 91%, respectively, at 1 FP/DBT volume. The difference between the performances for the two systems was statistically significant (p-value < 0.05). The image patterns learned from the mammograms were transferred to the mass detection on DBT slices through the DCNN. This study demonstrated that large data sets collected from mammography are useful for developing new CAD systems for DBT, alleviating the problem and effort of collecting entirely new large data sets for the new modality.

A novel method based on learning automata for automatic lesion detection in breast magnetic resonance imaging.

PubMed

Salehi, Leila; Azmi, Reza

2014-07-01

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. In this way, magnetic resonance imaging (MRI) is emerging as a powerful tool for the detection of breast cancer. Breast MRI presently has two major challenges. First, its specificity is relatively poor, and it detects many false positives (FPs). Second, the method involves acquiring several high-resolution image volumes before, during, and after the injection of a contrast agent. The large volume of data makes the task of interpretation by the radiologist both complex and time-consuming. These challenges have led to the development of the computer-aided detection systems to improve the efficiency and accuracy of the interpretation process. Detection of suspicious regions of interests (ROIs) is a critical preprocessing step in dynamic contrast-enhanced (DCE)-MRI data evaluation. In this regard, this paper introduces a new automatic method to detect the suspicious ROIs for breast DCE-MRI based on region growing. The results indicate that the proposed method is thoroughly able to identify suspicious regions (accuracy of 75.39 ± 3.37 on PIDER breast MRI dataset). Furthermore, the FP per image in this method is averagely 7.92, which shows considerable improvement comparing to other methods like ROI hunter.

Dose reduction in fluoroscopic interventions using a combination of a region of interest (ROI) x-ray attenuator and spatially different, temporally variable temporal filtering

NASA Astrophysics Data System (ADS)

Swetadri Vasan, S. N.; Pope, Liza; Ionita, Ciprian N.; Titus, A. H.; Bednarek, D. R.; Rudin, S.

2013-03-01

A novel dose reduction technique for fluoroscopic interventions involving a combination of a material x-ray region of interest (ROI) attenuator and spatially different, temporally variable ROI temporal recursive filter, was used to guide the catheter to the ROI in three live animal studies, two involving rabbits and one involving a sheep. In the two rabbit studies presented , a catheter was guided to the entrance of the carotid artery. With the added ROI attenuator the image under the high attenuation region is very noisy. By using temporal filtering with a filter weight of 0.6 on previous frames, the noise is reduced. In the sheep study the catheter was guided to the descending aorta of the animal. The sheep offered a relatively higher attenuation to the incident x-rays and thus a higher temporal filter weight of 0.8 on previous frames was used during the procedure to reduce the noise to levels acceptable by the interventionalist. The image sequences from both studies show that significant dose reduction of 5-6 times can be achieved with acceptable image quality outside the ROI by using the above mentioned technique. Even though the temporal filter weighting outside the ROI is higher, the consequent lag does not prevent perception of catheter movement.

Noninvasive imaging of human foveal capillary network using dual-conjugate adaptive optics.

PubMed

Popovic, Zoran; Knutsson, Per; Thaung, Jörgen; Owner-Petersen, Mette; Sjöstrand, Johan

2011-04-22

To demonstrate noninvasive imaging of human foveal capillary networks with a high-resolution, wide-field, dual-conjugate adaptive optics (DCAO) imaging instrument. The foveal capillary networks of five healthy subjects with no previous history of ocular or neurologic disease or surgery were imaged with a novel high-resolution, wide-field DCAO instrument. The foveal avascular zone (FAZ) in each image was defined using a manual procedure. An automated algorithm based on publicly available and custom-written software was used to identify vessels and extract morphologic FAZ and vessel parameters. Capillary densities were calculated in two annular regions of interest (ROIs) outside the FAZ (500 μm and 750 μm outer radius from the foveal center) and in the superior, inferior, temporal, and nasal quadrants within the two ROIs. Mean FAZ area was 0.302 ± 0.100 mm(2), and mean capillary density (length/area) in the inner ROI was 38.0 ± 4.0 mm(-1) and 36.4 ± 4.0 mm(-1) in the outer ROI. The difference in ROI capillary density was not significant. There was no significant difference in quadrant capillary density within the two ROIs or between quadrants irrespective of ROI. The authors have demonstrated a technique for noninvasive imaging and semiautomated detection and analysis of foveal capillaries. In comparison with other studies, their method yielded lower capillary densities than histology but similar results to the current clinical gold standard, fluorescein angiography. The increased field of view of the DCAO instrument opens up new possibilities for high-resolution noninvasive clinical imaging of foveal capillaries.

Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography

PubMed Central

Chan, Heang-Ping; Hadjiiski, Lubomir; Helvie, Mark A.; Wei, Jun; Cha, Kenny

2016-01-01

Purpose: Develop a computer-aided detection (CAD) system for masses in digital breast tomosynthesis (DBT) volume using a deep convolutional neural network (DCNN) with transfer learning from mammograms. Methods: A data set containing 2282 digitized film and digital mammograms and 324 DBT volumes were collected with IRB approval. The mass of interest on the images was marked by an experienced breast radiologist as reference standard. The data set was partitioned into a training set (2282 mammograms with 2461 masses and 230 DBT views with 228 masses) and an independent test set (94 DBT views with 89 masses). For DCNN training, the region of interest (ROI) containing the mass (true positive) was extracted from each image. False positive (FP) ROIs were identified at prescreening by their previously developed CAD systems. After data augmentation, a total of 45 072 mammographic ROIs and 37 450 DBT ROIs were obtained. Data normalization and reduction of non-uniformity in the ROIs across heterogeneous data was achieved using a background correction method applied to each ROI. A DCNN with four convolutional layers and three fully connected (FC) layers was first trained on the mammography data. Jittering and dropout techniques were used to reduce overfitting. After training with the mammographic ROIs, all weights in the first three convolutional layers were frozen, and only the last convolution layer and the FC layers were randomly initialized again and trained using the DBT training ROIs. The authors compared the performances of two CAD systems for mass detection in DBT: one used the DCNN-based approach and the other used their previously developed feature-based approach for FP reduction. The prescreening stage was identical in both systems, passing the same set of mass candidates to the FP reduction stage. For the feature-based CAD system, 3D clustering and active contour method was used for segmentation; morphological, gray level, and texture features were extracted and merged with a linear discriminant classifier to score the detected masses. For the DCNN-based CAD system, ROIs from five consecutive slices centered at each candidate were passed through the trained DCNN and a mass likelihood score was generated. The performances of the CAD systems were evaluated using free-response ROC curves and the performance difference was analyzed using a non-parametric method. Results: Before transfer learning, the DCNN trained only on mammograms with an AUC of 0.99 classified DBT masses with an AUC of 0.81 in the DBT training set. After transfer learning with DBT, the AUC improved to 0.90. For breast-based CAD detection in the test set, the sensitivity for the feature-based and the DCNN-based CAD systems was 83% and 91%, respectively, at 1 FP/DBT volume. The difference between the performances for the two systems was statistically significant (p-value < 0.05). Conclusions: The image patterns learned from the mammograms were transferred to the mass detection on DBT slices through the DCNN. This study demonstrated that large data sets collected from mammography are useful for developing new CAD systems for DBT, alleviating the problem and effort of collecting entirely new large data sets for the new modality. PMID:27908154

Local ROI Reconstruction via Generalized FBP and BPF Algorithms along More Flexible Curves.

PubMed

Yu, Hengyong; Ye, Yangbo; Zhao, Shiying; Wang, Ge

2006-01-01

We study the local region-of-interest (ROI) reconstruction problem, also referred to as the local CT problem. Our scheme includes two steps: (a) the local truncated normal-dose projections are extended to global dataset by combining a few global low-dose projections; (b) the ROI are reconstructed by either the generalized filtered backprojection (FBP) or backprojection-filtration (BPF) algorithms. The simulation results show that both the FBP and BPF algorithms can reconstruct satisfactory results with image quality in the ROI comparable to that of the corresponding global CT reconstruction.

Correlative imaging across microscopy platforms using the fast and accurate relocation of microscopic experimental regions (FARMER) method

NASA Astrophysics Data System (ADS)

Huynh, Toan; Daddysman, Matthew K.; Bao, Ying; Selewa, Alan; Kuznetsov, Andrey; Philipson, Louis H.; Scherer, Norbert F.

2017-05-01

Imaging specific regions of interest (ROIs) of nanomaterials or biological samples with different imaging modalities (e.g., light and electron microscopy) or at subsequent time points (e.g., before and after off-microscope procedures) requires relocating the ROIs. Unfortunately, relocation is typically difficult and very time consuming to achieve. Previously developed techniques involve the fabrication of arrays of features, the procedures for which are complex, and the added features can interfere with imaging the ROIs. We report the Fast and Accurate Relocation of Microscopic Experimental Regions (FARMER) method, which only requires determining the coordinates of 3 (or more) conspicuous reference points (REFs) and employs an algorithm based on geometric operators to relocate ROIs in subsequent imaging sessions. The 3 REFs can be quickly added to various regions of a sample using simple tools (e.g., permanent markers or conductive pens) and do not interfere with the ROIs. The coordinates of the REFs and the ROIs are obtained in the first imaging session (on a particular microscope platform) using an accurate and precise encoded motorized stage. In subsequent imaging sessions, the FARMER algorithm finds the new coordinates of the ROIs (on the same or different platforms), using the coordinates of the manually located REFs and the previously recorded coordinates. FARMER is convenient, fast (3-15 min/session, at least 10-fold faster than manual searches), accurate (4.4 μm average error on a microscope with a 100x objective), and precise (almost all errors are <8 μm), even with deliberate rotating and tilting of the sample well beyond normal repositioning accuracy. We demonstrate this versatility by imaging and re-imaging a diverse set of samples and imaging methods: live mammalian cells at different time points; fixed bacterial cells on two microscopes with different imaging modalities; and nanostructures on optical and electron microscopes. FARMER can be readily adapted to any imaging system with an encoded motorized stage and can facilitate multi-session and multi-platform imaging experiments in biology, materials science, photonics, and nanoscience.

Prediction of venous wound healing with laser speckle imaging.

PubMed

van Vuuren, Timme Maj; Van Zandvoort, Carina; Doganci, Suat; Zwiers, Ineke; tenCate-Hoek, Arina J; Kurstjens, Ralph Lm; Wittens, Cees Ha

2017-12-01

Introduction Laser speckle imaging is used for noninvasive assessment of blood flow of cutaneous wounds. The aim of this study was to assess if laser speckle imaging can be used as a predictor of venous ulcer healing. Methods After generating the flux speckle images, three regions of interest (ROI) were identified to measure the flow. Sensitivity, specificity, negative predictive value, and positive predictive value for ulcer healing were calculated. Results In total, 17 limbs were included. A sensitivity of 92.3%, specificity of 75.0%, PPV of 80.0%, and NPV 75.0% were found in predicting wound healing based on laser speckle images. Mean flux values were lowest in the center (ROI I) and showed an increase at the wound edge (ROI II, p = 0.03). Conclusion Laser speckle imaging shows acceptable sensitivity and specificity rates in predicting venous ulcer healing. The wound edge proved to be the best probability for the prediction of wound healing.

Two new regions of interest to evaluate separately cortical and trabecular BMD in the proximal femur using DXA.

PubMed

Prevrhal, Sven; Meta, Margarita; Genant, Harry K

2004-01-01

To differentiate changes in trabecular and cortical bone density at a skeletal site bearing body weight, the main goal of this retrospective study was to develop and characterize two new regions of interest (ROIs) for DXA at the hip, one mainly focusing on trabecular bone and another mainly focusing on cortical bone. Specific aims were to maximize the precision of the ROIs and to characterize their usefulness for monitoring age-related bone loss and discriminating controls from fracture cases in a cross-sectional study population and to compare them with earlier ROIs designed by our group. The study used populations from two different previous studies conducted in our laboratory, with one comprising cohorts of healthy premenopausal women, healthy postmenopausal women, and postmenopausal osteoporotic women with at least one spinal fracture (Spine Fx Study) and the other one comprising two cohorts of age-matched postmenopausal women, in whom cases had sustained a hip fracture (Hip Fx study). The new ROI for trabecular bone (CIRCROI) tries to improve on the earlier custom-designed Central ROI, which was also targeted at trabecular bone. CIRCROI consists of an approximate largest circle that can fit inside the femoral proximal metaphysis without touching the superior and inferior endocortical walls. The new ROI for cortical bone (CORTROI) at a site bearing body weight is defined as a horizontal rectangular box crossing the femoral shaft below the lesser trochanter. CORTROI BMD cohort means were significantly higher than all other ROIs, and CIRCROI BMD cohort means were lower than standard ROIs with the exception of Ward's ROI. CIRCROI BMD was highly correlated with total femur BMD ( r=0.94) and Central BMD ( r=0.93), whereas CORTROI BMD correlations were lower (highest with total femur BMD ( r=0.86)). Fracture discrimination odds ratios (ORs) of all ROIs were significant for the Hip Fx Study, with CIRCROI BMD having the highest, and CORTROI BMD the lowest, OR (4.83 and 2.49 per SD, respectively, compared with 3.69 for Ward's ROI as the highest OR of standard ROIs). For the Spine Fx Study, only spinal and trochanteric BMD had significant OR. The new trabecular ROI had good short-term precision, comparable to the standard ROIs at the hip, but improving on that of Ward's triangle, the only standard ROI only including the anterior and posterior cortical walls and therefore more predominantly consisting of trabecular bone than other standard ROIs. The precision of the new cortical ROI was lower than standard DXA ROIs, except for Ward's triangle, but provides unique information on purely cortical bone at a skeletal site bearing body weight.

Motion robust remote photoplethysmography in CIELab color space

NASA Astrophysics Data System (ADS)

Yang, Yuting; Liu, Chenbin; Yu, Hui; Shao, Dangdang; Tsow, Francis; Tao, Nongjian

2016-11-01

Remote photoplethysmography (rPPG) is attractive for tracking a subject's physiological parameters without wearing a device. However, rPPG is known to be prone to body movement-induced artifacts, making it unreliable in realistic situations. Here we report a method to minimize the movement-induced artifacts. The method selects an optimal region of interest (ROI) automatically, prunes frames in which the ROI is not clearly captured (e.g., subject moves out of the view), and analyzes rPPG using an algorithm in CIELab color space, rather than the widely used RGB color space. We show that body movement primarily affects image intensity, rather than chromaticity, and separating chromaticity from intensity in CIELab color space thus helps achieve effective reduction of the movement-induced artifacts. We validate the method by performing a pilot study including 17 people with diverse skin tones.

An automatic search of Alzheimer patterns using a nonnegative matrix factorization

NASA Astrophysics Data System (ADS)

Giraldo, Diana L.; García-Arteaga, Juan D.; Romero, Eduardo

2013-11-01

This paper presents a fully automatic method that condenses relevant morphometric information from a database of magnetic resonance images (MR) labeled as either normal (NC) or Alzheimer's disease (AD). The proposed method generates class templates using Nonnegative Matrix Factorization (NMF) which will be used to develop an NC/AD classi cator. It then nds regions of interest (ROI) with discerning inter-class properties. by inspecting the di erence volume of the two class templates. From these templates local probability distribution functions associated to low level features such as intensities, orientation and edges within the found ROI are calculated. A sample brain volume can then be characterized by a similarity measure in the ROI to both the normal and the pathological templates. These characteristics feed a simple binary SVM classi er which, when tested with an experimental group extracted from a public brain MR dataset (OASIS), reveals an equal error rate measure which is better than the state-of-the-art tested on the same dataset (0:9 in the former and 0:8 in the latter).

Multi-scales region segmentation for ROI separation in digital mammograms

NASA Astrophysics Data System (ADS)

Zhang, Dapeng; Zhang, Di; Li, Yue; Wang, Wei

2017-02-01

Mammography is currently the most effective imaging modality used by radiologists for the screening of breast cancer. Segmentation is one of the key steps in the process of developing anatomical models for calculation of safe medical dose of radiation. This paper explores the potential of the statistical region merging segmentation technique for Breast segmentation in digital mammograms. First, the mammograms are pre-processing for regions enhancement, then the enhanced images are segmented using SRM with multi scales, finally these segmentations are combined for region of interest (ROI) separation and edge detection. The proposed algorithm uses multi-scales region segmentation in order to: separate breast region from background region, region edge detection and ROIs separation. The experiments are performed using a data set of mammograms from different patients, demonstrating the validity of the proposed criterion. Results show that, the statistical region merging segmentation algorithm actually can work on the segmentation of medical image and more accurate than another methods. And the outcome shows that the technique has a great potential to become a method of choice for segmentation of mammograms.

Metal-induced streak artifact reduction using iterative reconstruction algorithms in x-ray computed tomography image of the dentoalveolar region.

PubMed

Dong, Jian; Hayakawa, Yoshihiko; Kannenberg, Sven; Kober, Cornelia

2013-02-01

The objective of this study was to reduce metal-induced streak artifact on oral and maxillofacial x-ray computed tomography (CT) images by developing the fast statistical image reconstruction system using iterative reconstruction algorithms. Adjacent CT images often depict similar anatomical structures in thin slices. So, first, images were reconstructed using the same projection data of an artifact-free image. Second, images were processed by the successive iterative restoration method where projection data were generated from reconstructed image in sequence. Besides the maximum likelihood-expectation maximization algorithm, the ordered subset-expectation maximization algorithm (OS-EM) was examined. Also, small region of interest (ROI) setting and reverse processing were applied for improving performance. Both algorithms reduced artifacts instead of slightly decreasing gray levels. The OS-EM and small ROI reduced the processing duration without apparent detriments. Sequential and reverse processing did not show apparent effects. Two alternatives in iterative reconstruction methods were effective for artifact reduction. The OS-EM algorithm and small ROI setting improved the performance. Copyright © 2012 Elsevier Inc. All rights reserved.

ImageParser: a tool for finite element generation from three-dimensional medical images

PubMed Central

Yin, HM; Sun, LZ; Wang, G; Yamada, T; Wang, J; Vannier, MW

2004-01-01

Background The finite element method (FEM) is a powerful mathematical tool to simulate and visualize the mechanical deformation of tissues and organs during medical examinations or interventions. It is yet a challenge to build up an FEM mesh directly from a volumetric image partially because the regions (or structures) of interest (ROIs) may be irregular and fuzzy. Methods A software package, ImageParser, is developed to generate an FEM mesh from 3-D tomographic medical images. This software uses a semi-automatic method to detect ROIs from the context of image including neighboring tissues and organs, completes segmentation of different tissues, and meshes the organ into elements. Results The ImageParser is shown to build up an FEM model for simulating the mechanical responses of the breast based on 3-D CT images. The breast is compressed by two plate paddles under an overall displacement as large as 20% of the initial distance between the paddles. The strain and tangential Young's modulus distributions are specified for the biomechanical analysis of breast tissues. Conclusion The ImageParser can successfully exact the geometry of ROIs from a complex medical image and generate the FEM mesh with customer-defined segmentation information. PMID:15461787

Pulmonary emphysema classification based on an improved texton learning model by sparse representation

NASA Astrophysics Data System (ADS)

Zhang, Min; Zhou, Xiangrong; Goshima, Satoshi; Chen, Huayue; Muramatsu, Chisako; Hara, Takeshi; Yokoyama, Ryujiro; Kanematsu, Masayuki; Fujita, Hiroshi

2013-03-01

In this paper, we present a texture classification method based on texton learned via sparse representation (SR) with new feature histogram maps in the classification of emphysema. First, an overcomplete dictionary of textons is learned via KSVD learning on every class image patches in the training dataset. In this stage, high-pass filter is introduced to exclude patches in smooth area to speed up the dictionary learning process. Second, 3D joint-SR coefficients and intensity histograms of the test images are used for characterizing regions of interest (ROIs) instead of conventional feature histograms constructed from SR coefficients of the test images over the dictionary. Classification is then performed using a classifier with distance as a histogram dissimilarity measure. Four hundreds and seventy annotated ROIs extracted from 14 test subjects, including 6 paraseptal emphysema (PSE) subjects, 5 centrilobular emphysema (CLE) subjects and 3 panlobular emphysema (PLE) subjects, are used to evaluate the effectiveness and robustness of the proposed method. The proposed method is tested on 167 PSE, 240 CLE and 63 PLE ROIs consisting of mild, moderate and severe pulmonary emphysema. The accuracy of the proposed system is around 74%, 88% and 89% for PSE, CLE and PLE, respectively.

The influence of the region of interest width on two-dimensional speckle tracking-based measurements of strain and strain rate.

PubMed

Spriestersbach, Hendrik; Oh-Icí, Darach; Schmitt, Boris; Berger, Felix; Schmitz, Lothar

2015-01-01

There are significant variations in the published normal values of two-dimensional speckle tracking-derived strain and strain rate. These occur even when authors use the same software. To measure strain, the operator creates a region of interest (ROI) to define the myocardium to be analyzed. The purpose of this study was to test the hypothesis that measurements vary significantly with the chosen ROI width. In 20 healthy subjects (11 males, mean age 17.6 ± 6.18 years) an apical four-chamber view (4CH) and parasternal short-axis view (SAX) were analyzed. Initially ROI width was set automatically by the software. Two subsequent measurements were obtained from each cine loop by choosing the ROI width one step narrower and one step wider than the automatic ROI width. The mean differences between the measurements of narrower and automatic ROI and between automatic and wider ROI were -1.8 ± 0.7% and -0.9 ± 0.5% for global longitudinal strain (SL), -2.2 ± 0.6% and -1.7 ± 0.7% for global circumferential strain (SC), -0.10 ± 0.06/sec and -0.07 ± 0.06/sec for global longitudinal strain rate (SrL), and -0.15 ± 0.09/sec and -0.12 ± 0.07/sec for global circumferential strain rate (SrC) (all P < 0.000). This corresponds to a relative difference to the mean of both measurements of -4.4 to -11.0%. Layer-specific myocardial deformation and curvature dependency lead to an inverse correlation between the chosen ROI width and strain and strain rate measurements. Just one step of ROI-width change leads to a significant bias. Precise ROI-width definition is essential but technical factors limit its feasibility. © 2014, Wiley Periodicals, Inc.

Image patch-based method for automated classification and detection of focal liver lesions on CT

NASA Astrophysics Data System (ADS)

Safdari, Mustafa; Pasari, Raghav; Rubin, Daniel; Greenspan, Hayit

2013-03-01

We developed a method for automated classification and detection of liver lesions in CT images based on image patch representation and bag-of-visual-words (BoVW). BoVW analysis has been extensively used in the computer vision domain to analyze scenery images. In the current work we discuss how it can be used for liver lesion classification and detection. The methodology includes building a dictionary for a training set using local descriptors and representing a region in the image using a visual word histogram. Two tasks are described: a classification task, for lesion characterization, and a detection task in which a scan window moves across the image and is determined to be normal liver tissue or a lesion. Data: In the classification task 73 CT images of liver lesions were used, 25 images having cysts, 24 having metastasis and 24 having hemangiomas. A radiologist circumscribed the lesions, creating a region of interest (ROI), in each of the images. He then provided the diagnosis, which was established either by biopsy or clinical follow-up. Thus our data set comprises 73 images and 73 ROIs. In the detection task, a radiologist drew ROIs around each liver lesion and two regions of normal liver, for a total of 159 liver lesion ROIs and 146 normal liver ROIs. The radiologist also demarcated the liver boundary. Results: Classification results of more than 95% were obtained. In the detection task, F1 results obtained is 0.76. Recall is 84%, with precision of 73%. Results show the ability to detect lesions, regardless of shape.

Local ROI Reconstruction via Generalized FBP and BPF Algorithms along More Flexible Curves

PubMed Central

Ye, Yangbo; Zhao, Shiying; Wang, Ge

2006-01-01

We study the local region-of-interest (ROI) reconstruction problem, also referred to as the local CT problem. Our scheme includes two steps: (a) the local truncated normal-dose projections are extended to global dataset by combining a few global low-dose projections; (b) the ROI are reconstructed by either the generalized filtered backprojection (FBP) or backprojection-filtration (BPF) algorithms. The simulation results show that both the FBP and BPF algorithms can reconstruct satisfactory results with image quality in the ROI comparable to that of the corresponding global CT reconstruction. PMID:23165018

Slow-rotation dynamic SPECT with a temporal second derivative constraint.

PubMed

Humphries, T; Celler, A; Trummer, M

2011-08-01

Dynamic tracer behavior in the human body arises as a result of continuous physiological processes. Hence, the change in tracer concentration within a region of interest (ROI) should follow a smooth curve. The authors propose a modification to an existing slow-rotation dynamic SPECT reconstruction algorithm (dSPECT) with the goal of improving the smoothness of time activity curves (TACs) and other properties of the reconstructed image. The new method, denoted d2EM, imposes a constraint on the second derivative (concavity) of the TAC in every voxel of the reconstructed image, allowing it to change sign at most once. Further constraints are enforced to prevent other nonphysical behaviors from arising. The new method is compared with dSPECT using digital phantom simulations and experimental dynamic 99mTc -DTPA renal SPECT data, to assess any improvement in image quality. In both phantom simulations and healthy volunteer experiments, the d2EM method provides smoother TACs than dSPECT, with more consistent shapes in regions with dynamic behavior. Magnitudes of TACs within an ROI still vary noticeably in both dSPECT and d2EM images, but also in images produced using an OSEM approach that reconstructs each time frame individually, based on much more complete projection data. TACs produced by averaging over a region are similar using either method, even for small ROIs. Results for experimental renal data show expected behavior in images produced by both methods, with d2EM providing somewhat smoother mean TACs and more consistent TAC shapes. The d2EM method is successful in improving the smoothness of time activity curves obtained from the reconstruction, as well as improving consistency of TAC shapes within ROIs.

Jansen-MIDAS: A multi-level photomicrograph segmentation software based on isotropic undecimated wavelets.

PubMed

de Siqueira, Alexandre Fioravante; Cabrera, Flávio Camargo; Nakasuga, Wagner Massayuki; Pagamisse, Aylton; Job, Aldo Eloizo

2018-01-01

Image segmentation, the process of separating the elements within a picture, is frequently used for obtaining information from photomicrographs. Segmentation methods should be used with reservations, since incorrect results can mislead when interpreting regions of interest (ROI). This decreases the success rate of extra procedures. Multi-Level Starlet Segmentation (MLSS) and Multi-Level Starlet Optimal Segmentation (MLSOS) were developed to be an alternative for general segmentation tools. These methods gave rise to Jansen-MIDAS, an open-source software. A scientist can use it to obtain several segmentations of hers/his photomicrographs. It is a reliable alternative to process different types of photomicrographs: previous versions of Jansen-MIDAS were used to segment ROI in photomicrographs of two different materials, with an accuracy superior to 89%. © 2017 Wiley Periodicals, Inc.

Mapping thalamocortical functional connectivity in chronic and early stages of psychotic disorders

PubMed Central

Woodward, Neil D.; Heckers, Stephan

2015-01-01

Objective There is considerable evidence that the thalamus is abnormal in psychotic disorders. Resting-state fMRI (RS-fMRI) has revealed an intriguing pattern of thalamic dysconnectivity in psychosis characterized by reduced prefrontal cortex (PFC) connectivity and increased somatomotor-thalamic connectivity. However, critical knowledge gaps remain with respect to the onset, anatomical specificity, and clinical correlates of thalamic dysconnectivity in psychosis. Method RS-fMRI was collected on 105 healthy subjects and 148 individuals with psychosis, including 53 early stage psychosis patients. Using all 253 subjects, the thalamus was parceled into functional regions-of-interest (ROIs) on the basis of connectivity with six a-priori defined cortical ROIs covering most of the cortical mantle. Functional connectivity between each cortical ROI and its corresponding thalamic ROI was quantified and compared across groups. Significant differences in the ROI-to-ROI analysis were followed up with voxel-wise seed-based analyses to further localize thalamic dysconnectivity. Results ROI analysis revealed reduced PFC-thalamic connectivity and increased somatomotor-thalamic connectivity in both chronic and early stages psychosis patients. PFC hypo-connectivity and motor cortex hyper-connectivity correlated in patients suggesting they result from a common pathophysiological mechanism. Seed-based analyses revealed thalamic hypo-connectivity in psychosis localized to dorsolateral PFC, medial PFC, and cerebellar areas of the well-described ‘executive control’ network. Across all subjects, thalamic connectivity with areas of the fronto-parietal network correlated with cognitive functioning, including verbal learning and memory. Conclusions Thalamocortical dysconnectivity is present in both chronic and early stages of psychosis, includes reduced thalamic connectivity with the executive control network, and is related to cognitive impairment. PMID:26248537

Discrimination between patients with mild Alzheimer's disease and healthy subjects based on cerebral blood flow images of the lateral views in xenon-enhanced computed tomography.

PubMed

Sase, Shigeru; Yamamoto, Homaro; Kawashima, Ena; Tan, Xin; Sawa, Yutaka

2018-01-01

Quantitative cerebral blood flow (CBF) measurement is expected to help early detection of functional abnormalities caused by Alzheimer's disease (AD) and enable AD treatment to begin in its early stages. Recently, a technique of layer analysis was reported that allowed CBF to be analyzed from the outer to inner layers of the brain. The aim of this work was to develop methods for discriminating between patients with mild AD and healthy subjects based on CBF images of the lateral views created with the layer analysis technique in xenon-enhanced computed tomography. Xenon-enhanced computed tomography using a wide-volume CT was performed on 17 patients with mild AD aged 75 or older and on 15 healthy age-matched volunteers. For each subject, we created CBF images of the right and left lateral views with a depth of 10-15 mm from the surface of the brain. Ten circular regions of interest (ROI) were placed on each image, and CBF was calculated for each ROI. We determined discriminant ROI that had CBF that could be used to differentiate between the AD and volunteer groups. AD patients' CBF range (mean - SD to mean + SD) and healthy volunteers' CBF range (mean - SD to mean + SD) were obtained for each ROI. Receiver-operator curves were created to identify patients with AD for each of the discriminant ROI and for the AD patients' and healthy volunteers' CBF ranges. We selected an ROI on both the right and left temporal lobes as the discriminant ROI. Areas under the receiver-operator curve were 93.3% using the ROI on the right temporal lobe, 95.3% using the ROI on the left temporal lobe, and 92.4% using the AD patients' and healthy volunteers' CBF ranges. We could effectively discriminate between patients with mild AD and healthy subjects using ROI placed on CBF images of the lateral views in xenon-enhanced computed tomography. © 2017 Japanese Psychogeriatric Society.

Parameter selection with the Hotelling observer in linear iterative image reconstruction for breast tomosynthesis

NASA Astrophysics Data System (ADS)

Rose, Sean D.; Roth, Jacob; Zimmerman, Cole; Reiser, Ingrid; Sidky, Emil Y.; Pan, Xiaochuan

2018-03-01

In this work we investigate an efficient implementation of a region-of-interest (ROI) based Hotelling observer (HO) in the context of parameter optimization for detection of a rod signal at two orientations in linear iterative image reconstruction for DBT. Our preliminary results suggest that ROI-HO performance trends may be efficiently estimated by modeling only the 2D plane perpendicular to the detector and containing the X-ray source trajectory. In addition, the ROI-HO is seen to exhibit orientation dependent trends in detectability as a function of the regularization strength employed in reconstruction. To further investigate the ROI-HO performance in larger 3D system models, we present and validate an iterative methodology for calculating the ROI-HO. Lastly, we present a real data study investigating the correspondence between ROI-HO performance trends and signal conspicuity. Conspicuity of signals in real data reconstructions is seen to track well with trends in ROI-HO detectability. In particular, we observe orientation dependent conspicuity matching the orientation dependent detectability of the ROI-HO.

Quantitative diagnostic method for biceps long head tendinitis by using ultrasound.

PubMed

Huang, Shih-Wei; Wang, Wei-Te

2013-01-01

To investigate the feasibility of grayscale quantitative diagnostic method for biceps tendinitis and determine the cut-off points of a quantitative biceps ultrasound (US) method to diagnose biceps tendinitis. Design. Prospective cross-sectional case controlled study. Outpatient rehabilitation service. A total of 336 shoulder pain patients with suspected biceps tendinitis were recruited in this prospective observational study. The grayscale pixel data of the range of interest (ROI) were obtained for both the transverse and longitudinal views of the biceps US. A total of 136 patients were classified with biceps tendinitis, and 200 patients were classified as not having biceps tendinitis based on the diagnostic criteria. Based on the Youden index, the cut-off points were determined as 26.85 for the transverse view and 21.25 for the longitudinal view of the standard deviation (StdDev) of the ROI values, respectively. When the ROI evaluation of the US surpassed the cut-off point, the sensitivity was 68% and the specificity was 90% in the StdDev of the transverse view, and the sensitivity was 81% and the specificity was 73% in the StdDev of the longitudinal view to diagnose biceps tendinitis. For equivocal cases or inexperienced sonographers, our study provides a more objective method for diagnosing biceps tendinitis in shoulder pain patients.

Defect inspection in hot slab surface: multi-source CCD imaging based fuzzy-rough sets method

NASA Astrophysics Data System (ADS)

Zhao, Liming; Zhang, Yi; Xu, Xiaodong; Xiao, Hong; Huang, Chao

2016-09-01

To provide an accurate surface defects inspection method and make the automation of robust image region of interests(ROI) delineation strategy a reality in production line, a multi-source CCD imaging based fuzzy-rough sets method is proposed for hot slab surface quality assessment. The applicability of the presented method and the devised system are mainly tied to the surface quality inspection for strip, billet and slab surface etcetera. In this work we take into account the complementary advantages in two common machine vision (MV) systems(line array CCD traditional scanning imaging (LS-imaging) and area array CCD laser three-dimensional (3D) scanning imaging (AL-imaging)), and through establishing the model of fuzzy-rough sets in the detection system the seeds for relative fuzzy connectedness(RFC) delineation for ROI can placed adaptively, which introduces the upper and lower approximation sets for RIO definition, and by which the boundary region can be delineated by RFC region competitive classification mechanism. For the first time, a Multi-source CCD imaging based fuzzy-rough sets strategy is attempted for CC-slab surface defects inspection that allows an automatic way of AI algorithms and powerful ROI delineation strategies to be applied to the MV inspection field.

Asymmetry of cortical decline in subtypes of primary progressive aphasia

PubMed Central

Cobia, Derin; Martersteck, Adam; Rademaker, Alfred; Wieneke, Christina; Weintraub, Sandra; Mesulam, M.-Marsel

2014-01-01

Objective: The aim of this study was to provide quantitative measures of changes in cortical atrophy over a 2-year period associated with 3 subtypes of primary progressive aphasia (PPA) using whole-brain vertex-wise and region-of-interest (ROI) neuroimaging methods. The purpose was to quantitate disease progression, establish an empirical basis for clinical expectations, and provide outcome measures for therapeutic trials. Methods: Changes in cortical thickness and volume loss as well as neuropsychological performance were assessed at baseline and 2-year follow-up in 26 patients who fulfilled criteria for logopenic (8 patients), agrammatic (10 patients), and semantic (8 patients) PPA subtypes. Whole-brain vertex-wise and ROI imaging analysis were conducted using the FreeSurfer longitudinal pipeline. Results: Clinical deficits and cortical atrophy patterns showed distinct patterns of change among the subtypes over 2 years. Results confirmed that progression for each of the 3 subtypes showed left greater than right hemisphere asymmetry. An ROI analysis also revealed that progression was greater within, rather than outside, the language network. Conclusions: Preferential neurodegeneration of the left hemisphere language network is a common denominator for all 3 PPA subtypes, even as the disease progresses. Using a focal cortical language network ROI as an outcome measure of disease progression appears to be more sensitive than whole-brain or ventricular volume measures of change and may be helpful for designing future clinical trials in PPA. PMID:25165386

Semiautomated analysis of small-animal PET data.

PubMed

Kesner, Adam L; Dahlbom, Magnus; Huang, Sung-Cheng; Hsueh, Wei-Ann; Pio, Betty S; Czernin, Johannes; Kreissl, Michael; Wu, Hsiao-Ming; Silverman, Daniel H S

2006-07-01

The objective of the work reported here was to develop and test automated methods to calculate biodistribution of PET tracers using small-animal PET images. After developing software that uses visually distinguishable organs and other landmarks on a scan to semiautomatically coregister a digital mouse phantom with a small-animal PET scan, we elastically transformed the phantom to conform to those landmarks in 9 simulated scans and in 18 actual PET scans acquired of 9 mice. Tracer concentrations were automatically calculated in 22 regions of interest (ROIs) reflecting the whole body and 21 individual organs. To assess the accuracy of this approach, we compared the software-measured activities in the ROIs of simulated PET scans with the known activities, and we compared the software-measured activities in the ROIs of real PET scans both with manually established ROI activities in original scan data and with actual radioactivity content in immediately harvested tissues of imaged animals. PET/atlas coregistrations were successfully generated with minimal end-user input, allowing rapid quantification of 22 separate tissue ROIs. The simulated scan analysis found the method to be robust with respect to the overall size and shape of individual animal scans, with average activity values for all organs tested falling within the range of 98% +/- 3% of the organ activity measured in the unstretched phantom scan. Standardized uptake values (SUVs) measured from actual PET scans using this semiautomated method correlated reasonably well with radioactivity content measured in harvested organs (median r = 0.94) and compared favorably with conventional SUV correlations with harvested organ data (median r = 0.825). A semiautomated analytic approach involving coregistration of scan-derived images with atlas-type images can be used in small-animal whole-body radiotracer studies to estimate radioactivity concentrations in organs. This approach is rapid and less labor intensive than are traditional methods, without diminishing overall accuracy. Such techniques have the possibility of saving time, effort, and the number of animals needed for such assessments.

Generalized whole-body Patlak parametric imaging for enhanced quantification in clinical PET.

PubMed

Karakatsanis, Nicolas A; Zhou, Yun; Lodge, Martin A; Casey, Michael E; Wahl, Richard L; Zaidi, Habib; Rahmim, Arman

2015-11-21

We recently developed a dynamic multi-bed PET data acquisition framework to translate the quantitative benefits of Patlak voxel-wise analysis to the domain of routine clinical whole-body (WB) imaging. The standard Patlak (sPatlak) linear graphical analysis assumes irreversible PET tracer uptake, ignoring the effect of FDG dephosphorylation, which has been suggested by a number of PET studies. In this work: (i) a non-linear generalized Patlak (gPatlak) model is utilized, including a net efflux rate constant kloss, and (ii) a hybrid (s/g)Patlak (hPatlak) imaging technique is introduced to enhance contrast to noise ratios (CNRs) of uptake rate Ki images. Representative set of kinetic parameter values and the XCAT phantom were employed to generate realistic 4D simulation PET data, and the proposed methods were additionally evaluated on 11 WB dynamic PET patient studies. Quantitative analysis on the simulated Ki images over 2 groups of regions-of-interest (ROIs), with low (ROI A) or high (ROI B) true kloss relative to Ki, suggested superior accuracy for gPatlak. Bias of sPatlak was found to be 16-18% and 20-40% poorer than gPatlak for ROIs A and B, respectively. By contrast, gPatlak exhibited, on average, 10% higher noise than sPatlak. Meanwhile, the bias and noise levels for hPatlak always ranged between the other two methods. In general, hPatlak was seen to outperform all methods in terms of target-to-background ratio (TBR) and CNR for all ROIs. Validation on patient datasets demonstrated clinical feasibility for all Patlak methods, while TBR and CNR evaluations confirmed our simulation findings, and suggested presence of non-negligible kloss reversibility in clinical data. As such, we recommend gPatlak for highly quantitative imaging tasks, while, for tasks emphasizing lesion detectability (e.g. TBR, CNR) over quantification, or for high levels of noise, hPatlak is instead preferred. Finally, gPatlak and hPatlak CNR was systematically higher compared to routine SUV values.

Generalized whole-body Patlak parametric imaging for enhanced quantification in clinical PET

NASA Astrophysics Data System (ADS)

Karakatsanis, Nicolas A.; Zhou, Yun; Lodge, Martin A.; Casey, Michael E.; Wahl, Richard L.; Zaidi, Habib; Rahmim, Arman

2015-11-01

We recently developed a dynamic multi-bed PET data acquisition framework to translate the quantitative benefits of Patlak voxel-wise analysis to the domain of routine clinical whole-body (WB) imaging. The standard Patlak (sPatlak) linear graphical analysis assumes irreversible PET tracer uptake, ignoring the effect of FDG dephosphorylation, which has been suggested by a number of PET studies. In this work: (i) a non-linear generalized Patlak (gPatlak) model is utilized, including a net efflux rate constant kloss, and (ii) a hybrid (s/g)Patlak (hPatlak) imaging technique is introduced to enhance contrast to noise ratios (CNRs) of uptake rate Ki images. Representative set of kinetic parameter values and the XCAT phantom were employed to generate realistic 4D simulation PET data, and the proposed methods were additionally evaluated on 11 WB dynamic PET patient studies. Quantitative analysis on the simulated Ki images over 2 groups of regions-of-interest (ROIs), with low (ROI A) or high (ROI B) true kloss relative to Ki, suggested superior accuracy for gPatlak. Bias of sPatlak was found to be 16-18% and 20-40% poorer than gPatlak for ROIs A and B, respectively. By contrast, gPatlak exhibited, on average, 10% higher noise than sPatlak. Meanwhile, the bias and noise levels for hPatlak always ranged between the other two methods. In general, hPatlak was seen to outperform all methods in terms of target-to-background ratio (TBR) and CNR for all ROIs. Validation on patient datasets demonstrated clinical feasibility for all Patlak methods, while TBR and CNR evaluations confirmed our simulation findings, and suggested presence of non-negligible kloss reversibility in clinical data. As such, we recommend gPatlak for highly quantitative imaging tasks, while, for tasks emphasizing lesion detectability (e.g. TBR, CNR) over quantification, or for high levels of noise, hPatlak is instead preferred. Finally, gPatlak and hPatlak CNR was systematically higher compared to routine SUV values.

The effect of the sample size and location on contrast ultrasound measurement of perfusion parameters.

PubMed

Leinonen, Merja R; Raekallio, Marja R; Vainio, Outi M; Ruohoniemi, Mirja O; O'Brien, Robert T

2011-01-01

Contrast-enhanced ultrasound can be used to quantify tissue perfusion based on region of interest (ROI) analysis. The effect of the location and size of the ROI on the obtained perfusion parameters has been described in phantom, ex vivo and in vivo studies. We assessed the effects of location and size of the ROI on perfusion parameters in the renal cortex of 10 healthy, anesthetized cats using Definity contrast-enhanced ultrasound to estimate the importance of the ROI on quantification of tissue perfusion with contrast-enhanced ultrasound. Three separate sets of ROIs were placed in the renal cortex, varying in location, size or depth. There was a significant inverse association between increased depth or increased size of the ROI and peak intensity (P < 0.05). There was no statistically significant difference in the peak intensity between the ROIs placed in a row in the near field cortex. There was no significant difference in the ROIs with regard to arrival time, time to peak intensity and wash-in rate. When comparing two different ROIs in a patient with focal lesions, such as suspected neoplasia or infarction, the ROIs should always be placed at same depth and be as similar in size as possible.

A semi-automated region of interest detection method in the scintigraphic glomerular filtration rate determination for patients with abnormal low renal function.

PubMed

Tian, Cancan; Zheng, Xiujuan; Han, Yuan; Sun, Xiaoguang; Chen, Kewei; Huang, Qiu

2013-11-01

This work presents a novel semi-automated renal region-of-interest (ROI) determination method that is user friendly, time saving, and yet provides a robust glomerular filtration rate (GFR) estimation highly consistent with the reference method. We reviewed data from 57 patients who underwent (99m)Tc-diethylenetriaminepentaacetic acid renal scintigraphy and were diagnosed with abnormal renal function. The renal and background ROIs were delineated by the proposed multi-step, semi-automated method, which integrates temporal/morphologic information via visual inspection and computer-aided calculations. The total GFR was estimated using the proposed method (sGFR) performed by 2 junior clinicians (A and B) with 1 and 3 years of experience, respectively (sGFR_a, sGFR_b), and compared with the reference total GFR (rGFR) estimated by a senior clinician with 20 years of experience who manually delineated the kidney and background ROIs. All GFR calculations herein were conducted using the Gates method. Data from 10 patients with unilateral or non-functioning kidneys were excluded from the analysis. For the remaining patients, sGFR correlated well with rGFR (r(s/rGFR_a) = 0.957, P < 0.001 and r(s/rGFR_b) = 0.951, P < 0.001) and sGFR_a correlated well with sGFR_b (r(a/b) = 0.997, P < 0.001). Moreover, the Bland-Altman plots for sGFR_a and sGFR_b confirm the high reproducibility of the proposed method between different operators. Finally, the proposed procedure is almost 3 times faster than the routinely used procedure in clinical practice. The results suggest that this method is easy to use, highly reproducible, and accurate in measuring the GFR of patients with low renal function. The method is being further extended to a fully automated procedure.

Poster — Thur Eve — 09: Evaluation of electrical impedance and computed tomography fusion algorithms using an anthropomorphic phantom

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chugh, Brige Paul; Krishnan, Kalpagam; Liu, Jeff

2014-08-15

Integration of biological conductivity information provided by Electrical Impedance Tomography (EIT) with anatomical information provided by Computed Tomography (CT) imaging could improve the ability to characterize tissues in clinical applications. In this paper, we report results of our study which compared the fusion of EIT with CT using three different image fusion algorithms, namely: weighted averaging, wavelet fusion, and ROI indexing. The ROI indexing method of fusion involves segmenting the regions of interest from the CT image and replacing the pixels with the pixels of the EIT image. The three algorithms were applied to a CT and EIT image ofmore » an anthropomorphic phantom, constructed out of five acrylic contrast targets with varying diameter embedded in a base of gelatin bolus. The imaging performance was assessed using Detectability and Structural Similarity Index Measure (SSIM). Wavelet fusion and ROI-indexing resulted in lower Detectability (by 35% and 47%, respectively) yet higher SSIM (by 66% and 73%, respectively) than weighted averaging. Our results suggest that wavelet fusion and ROI-indexing yielded more consistent and optimal fusion performance than weighted averaging.« less

Bone age assessment by content-based image retrieval and case-based reasoning

NASA Astrophysics Data System (ADS)

Fischer, Benedikt; Welter, Petra; Grouls, Christoph; Günther, Rolf W.; Deserno, Thomas M.

2011-03-01

Skeletal maturity is assessed visually by comparing hand radiographs to a standardized reference image atlas. Most common are the methods by Greulich & Pyle and Tanner & Whitehouse. For computer-aided diagnosis (CAD), local image regions of interest (ROI) such as the epiphysis or the carpal areas are extracted and evaluated. Heuristic approaches trying to automatically extract, measure and classify bones and distances between bones suffer from the high variability of biological material and the differences in bone development resulting from age, gender and ethnic origin. Content-based image retrieval (CBIR) provides a robust solution without delineating and measuring bones. In this work, epiphyseal ROIs (eROIS) of a hand radiograph are compared to previous cases with known age, mimicking a human observer. Leaving-one-out experiments are conducted on 1,102 left hand radiographs and 15,428 metacarpal and phalangeal eROIs from the publicly available USC hand atlas. The similarity of the eROIs is assessed by a combination of cross-correlation, image distortion model, and Tamura texture features, yielding a mean error rate of 0.97 years and a variance of below 0.63 years. Furthermore, we introduce a publicly available online-demonstration system, where queries on the USC dataset as well as on uploaded radiographs are performed for instant CAD. In future, we plan to evaluate physician with CBIR-CAD against physician without CBIR-CAD rather than physician vs. CBIR-CAD.

Constructing and assessing brain templates from Chinese pediatric MRI data using SPM

NASA Astrophysics Data System (ADS)

Yin, Qingjie; Ye, Qing; Yao, Li; Chen, Kewei; Jin, Zhen; Liu, Gang; Wu, Xingchun; Wang, Tingting

2005-04-01

Spatial normalization is a very important step in the processing of magnetic resonance imaging (MRI) data. So the quality of brain templates is crucial for the accuracy of MRI analysis. In this paper, using the classical protocol and the optimized protocol plus nonlinear deformation, we constructed the T1 whole brain templates and apriori brain tissue data from 69 Chinese pediatric MRI data (age 7-16 years). Then we proposed a new assessment method to evaluate our templates. 10 pediatric subjects were chosen to do the assessment as the following steps. First, the cerebellum region, the region of interest (ROI), was located on both the pediatric volume and the template volume by an experienced neuroanatomist. Second, the pediatric whole brain was mapped to the template with affine and nonlinear deformation. Third, the parameter, derived from the second step, was used to only normalize the ROI of the child to the ROI of the template. Last, the overlapping ratio, which described the overlapping rate between the ROI of the template and the normalized ROI of the child, was calculated. The mean of overlapping ratio normalized to the classical template was 0.9687, and the mean normalized to the optimized template was 0.9713. The results show that the two Chinese pediatric brain templates are comparable and their accuracy is adequate to our studies.

Absolute myocardial flow quantification with (82)Rb PET/CT: comparison of different software packages and methods.

PubMed

Tahari, Abdel K; Lee, Andy; Rajaram, Mahadevan; Fukushima, Kenji; Lodge, Martin A; Lee, Benjamin C; Ficaro, Edward P; Nekolla, Stephan; Klein, Ran; deKemp, Robert A; Wahl, Richard L; Bengel, Frank M; Bravo, Paco E

2014-01-01

In clinical cardiac (82)Rb PET, globally impaired coronary flow reserve (CFR) is a relevant marker for predicting short-term cardiovascular events. However, there are limited data on the impact of different software and methods for estimation of myocardial blood flow (MBF) and CFR. Our objective was to compare quantitative results obtained from previously validated software tools. We retrospectively analyzed cardiac (82)Rb PET/CT data from 25 subjects (group 1, 62 ± 11 years) with low-to-intermediate probability of coronary artery disease (CAD) and 26 patients (group 2, 57 ± 10 years; P=0.07) with known CAD. Resting and vasodilator-stress MBF and CFR were derived using three software applications: (1) Corridor4DM (4DM) based on factor analysis (FA) and kinetic modeling, (2) 4DM based on region-of-interest (ROI) and kinetic modeling, (3) MunichHeart (MH), which uses a simplified ROI-based retention model approach, and (4) FlowQuant (FQ) based on ROI and compartmental modeling with constant distribution volume. Resting and stress MBF values (in milliliters per minute per gram) derived using the different methods were significantly different: using 4DM-FA, 4DM-ROI, FQ, and MH resting MBF values were 1.47 ± 0.59, 1.16 ± 0.51, 0.91 ± 0.39, and 0.90 ± 0.44, respectively (P<0.001), and stress MBF values were 3.05 ± 1.66, 2.26 ± 1.01, 1.90 ± 0.82, and 1.83 ± 0.81, respectively (P<0.001). However, there were no statistically significant differences among the CFR values (2.15 ± 1.08, 2.05 ± 0.83, 2.23 ± 0.89, and 2.21 ± 0.90, respectively; P=0.17). Regional MBF and CFR according to vascular territories showed similar results. Linear correlation coefficient for global CFR varied between 0.71 (MH vs. 4DM-ROI) and 0.90 (FQ vs. 4DM-ROI). Using a cut-off value of 2.0 for abnormal CFR, the agreement among the software programs ranged between 76 % (MH vs. FQ) and 90 % (FQ vs. 4DM-ROI). Interobserver agreement was in general excellent with all software packages. Quantitative assessment of resting and stress MBF with (82)Rb PET is dependent on the software and methods used, whereas CFR appears to be more comparable. Follow-up and treatment assessment should be done with the same software and method.

Comparison of region-of-interest-averaged and pixel-averaged analysis of DCE-MRI data based on simulations and pre-clinical experiments

NASA Astrophysics Data System (ADS)

He, Dianning; Zamora, Marta; Oto, Aytekin; Karczmar, Gregory S.; Fan, Xiaobing

2017-09-01

Differences between region-of-interest (ROI) and pixel-by-pixel analysis of dynamic contrast enhanced (DCE) MRI data were investigated in this study with computer simulations and pre-clinical experiments. ROIs were simulated with 10, 50, 100, 200, 400, and 800 different pixels. For each pixel, a contrast agent concentration as a function of time, C(t), was calculated using the Tofts DCE-MRI model with randomly generated physiological parameters (K trans and v e) and the Parker population arterial input function. The average C(t) for each ROI was calculated and then K trans and v e for the ROI was extracted. The simulations were run 100 times for each ROI with new K trans and v e generated. In addition, white Gaussian noise was added to C(t) with 3, 6, and 12 dB signal-to-noise ratios to each C(t). For pre-clinical experiments, Copenhagen rats (n  =  6) with implanted prostate tumors in the hind limb were used in this study. The DCE-MRI data were acquired with a temporal resolution of ~5 s in a 4.7 T animal scanner, before, during, and after a bolus injection (<5 s) of Gd-DTPA for a total imaging duration of ~10 min. K trans and v e were calculated in two ways: (i) by fitting C(t) for each pixel, and then averaging the pixel values over the entire ROI, and (ii) by averaging C(t) over the entire ROI, and then fitting averaged C(t) to extract K trans and v e. The simulation results showed that in heterogeneous ROIs, the pixel-by-pixel averaged K trans was ~25% to ~50% larger (p  <  0.01) than the ROI-averaged K trans. At higher noise levels, the pixel-averaged K trans was greater than the ‘true’ K trans, but the ROI-averaged K trans was lower than the ‘true’ K trans. The ROI-averaged K trans was closer to the true K trans than pixel-averaged K trans for high noise levels. In pre-clinical experiments, the pixel-by-pixel averaged K trans was ~15% larger than the ROI-averaged K trans. Overall, with the Tofts model, the extracted physiological parameters from the pixel-by-pixel averages were larger than the ROI averages. These differences were dependent on the heterogeneity of the ROI.

Template-based automatic extraction of the joint space of foot bones from CT scan

NASA Astrophysics Data System (ADS)

Park, Eunbi; Kim, Taeho; Park, Jinah

2016-03-01

Clean bone segmentation is critical in studying the joint anatomy for measuring the spacing between the bones. However, separation of the coupled bones in CT images is sometimes difficult due to ambiguous gray values coming from the noise and the heterogeneity of bone materials as well as narrowing of the joint space. For fine reconstruction of the individual local boundaries, manual operation is a common practice where the segmentation remains to be a bottleneck. In this paper, we present an automatic method for extracting the joint space by applying graph cut on Markov random field model to the region of interest (ROI) which is identified by a template of 3D bone structures. The template includes encoded articular surface which identifies the tight region of the high-intensity bone boundaries together with the fuzzy joint area of interest. The localized shape information from the template model within the ROI effectively separates the bones nearby. By narrowing the ROI down to the region including two types of tissue, the object extraction problem was reduced to binary segmentation and solved via graph cut. Based on the shape of a joint space marked by the template, the hard constraint was set by the initial seeds which were automatically generated from thresholding and morphological operations. The performance and the robustness of the proposed method are evaluated on 12 volumes of ankle CT data, where each volume includes a set of 4 tarsal bones (calcaneus, talus, navicular and cuboid).

Linear associations between clinically assessed upper motor neuron disease and diffusion tensor imaging metrics in amyotrophic lateral sclerosis.

PubMed

Woo, John H; Wang, Sumei; Melhem, Elias R; Gee, James C; Cucchiara, Andrew; McCluskey, Leo; Elman, Lauren

2014-01-01

To assess the relationship between clinically assessed Upper Motor Neuron (UMN) disease in Amyotrophic Lateral Sclerosis (ALS) and local diffusion alterations measured in the brain corticospinal tract (CST) by a tractography-driven template-space region-of-interest (ROI) analysis of Diffusion Tensor Imaging (DTI). This cross-sectional study included 34 patients with ALS, on whom DTI was performed. Clinical measures were separately obtained including the Penn UMN Score, a summary metric based upon standard clinical methods. After normalizing all DTI data to a population-specific template, tractography was performed to determine a region-of-interest (ROI) outlining the CST, in which average Mean Diffusivity (MD) and Fractional Anisotropy (FA) were estimated. Linear regression analyses were used to investigate associations of DTI metrics (MD, FA) with clinical measures (Penn UMN Score, ALSFRS-R, duration-of-disease), along with age, sex, handedness, and El Escorial category as covariates. For MD, the regression model was significant (p = 0.02), and the only significant predictors were the Penn UMN Score (p = 0.005) and age (p = 0.03). The FA regression model was also significant (p = 0.02); the only significant predictor was the Penn UMN Score (p = 0.003). Measured by the template-space ROI method, both MD and FA were linearly associated with the Penn UMN Score, supporting the hypothesis that DTI alterations reflect UMN pathology as assessed by the clinical examination.

Selecting the most relevant brain regions to discriminate Alzheimer's disease patients from healthy controls using multiple kernel learning: A comparison across functional and structural imaging modalities and atlases.

PubMed

Rondina, Jane Maryam; Ferreira, Luiz Kobuti; de Souza Duran, Fabio Luis; Kubo, Rodrigo; Ono, Carla Rachel; Leite, Claudia Costa; Smid, Jerusa; Nitrini, Ricardo; Buchpiguel, Carlos Alberto; Busatto, Geraldo F

2018-01-01

Machine learning techniques such as support vector machine (SVM) have been applied recently in order to accurately classify individuals with neuropsychiatric disorders such as Alzheimer's disease (AD) based on neuroimaging data. However, the multivariate nature of the SVM approach often precludes the identification of the brain regions that contribute most to classification accuracy. Multiple kernel learning (MKL) is a sparse machine learning method that allows the identification of the most relevant sources for the classification. By parcelating the brain into regions of interest (ROI) it is possible to use each ROI as a source to MKL (ROI-MKL). We applied MKL to multimodal neuroimaging data in order to: 1) compare the diagnostic performance of ROI-MKL and whole-brain SVM in discriminating patients with AD from demographically matched healthy controls and 2) identify the most relevant brain regions to the classification. We used two atlases (AAL and Brodmann's) to parcelate the brain into ROIs and applied ROI-MKL to structural (T1) MRI, 18 F-FDG-PET and regional cerebral blood flow SPECT (rCBF-SPECT) data acquired from the same subjects (20 patients with early AD and 18 controls). In ROI-MKL, each ROI received a weight (ROI-weight) that indicated the region's relevance to the classification. For each ROI, we also calculated whether there was a predominance of voxels indicating decreased or increased regional activity (for 18 F-FDG-PET and rCBF-SPECT) or volume (for T1-MRI) in AD patients. Compared to whole-brain SVM, the ROI-MKL approach resulted in better accuracies (with either atlas) for classification using 18 F-FDG-PET (92.5% accuracy for ROI-MKL versus 84% for whole-brain), but not when using rCBF-SPECT or T1-MRI. Although several cortical and subcortical regions contributed to discrimination, high ROI-weights and predominance of hypometabolism and atrophy were identified specially in medial parietal and temporo-limbic cortical regions. Also, the weight of discrimination due to a pattern of increased voxel-weight values in AD individuals was surprisingly high (ranging from approximately 20% to 40% depending on the imaging modality), located mainly in primary sensorimotor and visual cortices and subcortical nuclei. The MKL-ROI approach highlights the high discriminative weight of a subset of brain regions of known relevance to AD, the selection of which contributes to increased classification accuracy when applied to 18 F-FDG-PET data. Moreover, the MKL-ROI approach demonstrates that brain regions typically spared in mild stages of AD also contribute substantially in the individual discrimination of AD patients from controls.

Motion robust remote photoplethysmography in CIELab color space

PubMed Central

Yang, Yuting; Liu, Chenbin; Yu, Hui; Shao, Dangdang; Tsow, Francis; Tao, Nongjian

2016-01-01

Abstract. Remote photoplethysmography (rPPG) is attractive for tracking a subject’s physiological parameters without wearing a device. However, rPPG is known to be prone to body movement-induced artifacts, making it unreliable in realistic situations. Here we report a method to minimize the movement-induced artifacts. The method selects an optimal region of interest (ROI) automatically, prunes frames in which the ROI is not clearly captured (e.g., subject moves out of the view), and analyzes rPPG using an algorithm in CIELab color space, rather than the widely used RGB color space. We show that body movement primarily affects image intensity, rather than chromaticity, and separating chromaticity from intensity in CIELab color space thus helps achieve effective reduction of the movement-induced artifacts. We validate the method by performing a pilot study including 17 people with diverse skin tones. PMID:27812695

Human Auditory and Adjacent Nonauditory Cerebral Cortices Are Hypermetabolic in Tinnitus as Measured by Functional Near-Infrared Spectroscopy (fNIRS)

PubMed Central

Issa, Mohamad; Bisconti, Silvia; Kovelman, Ioulia; Kileny, Paul

2016-01-01

Tinnitus is the phantom perception of sound in the absence of an acoustic stimulus. To date, the purported neural correlates of tinnitus from animal models have not been adequately characterized with translational technology in the human brain. The aim of the present study was to measure changes in oxy-hemoglobin concentration from regions of interest (ROI; auditory cortex) and non-ROI (adjacent nonauditory cortices) during auditory stimulation and silence in participants with subjective tinnitus appreciated equally in both ears and in nontinnitus controls using functional near-infrared spectroscopy (fNIRS). Control and tinnitus participants with normal/near-normal hearing were tested during a passive auditory task. Hemodynamic activity was monitored over ROI and non-ROI under episodic periods of auditory stimulation with 750 or 8000 Hz tones, broadband noise, and silence. During periods of silence, tinnitus participants maintained increased hemodynamic responses in ROI, while a significant deactivation was seen in controls. Interestingly, non-ROI activity was also increased in the tinnitus group as compared to controls during silence. The present results demonstrate that both auditory and select nonauditory cortices have elevated hemodynamic activity in participants with tinnitus in the absence of an external auditory stimulus, a finding that may reflect basic science neural correlates of tinnitus that ultimately contribute to phantom sound perception. PMID:27042360

Practical implementation of channelized hotelling observers: effect of ROI size

NASA Astrophysics Data System (ADS)

Ferrero, Andrea; Favazza, Christopher P.; Yu, Lifeng; Leng, Shuai; McCollough, Cynthia H.

2017-03-01

Fundamental to the development and application of channelized Hotelling observer (CHO) models is the selection of the region of interest (ROI) to evaluate. For assessment of medical imaging systems, reducing the ROI size can be advantageous. Smaller ROIs enable a greater concentration of interrogable objects in a single phantom image, thereby providing more information from a set of images and reducing the overall image acquisition burden. Additionally, smaller ROIs may promote better assessment of clinical patient images as different patient anatomies present different ROI constraints. To this end, we investigated the minimum ROI size that does not compromise the performance of the CHO model. In this study, we evaluated both simulated images and phantom CT images to identify the minimum ROI size that resulted in an accurate figure of merit (FOM) of the CHO's performance. More specifically, the minimum ROI size was evaluated as a function of the following: number of channels, spatial frequency and number of rotations of the Gabor filters, size and contrast of the object, and magnitude of the image noise. Results demonstrate that a minimum ROI size exists below which the CHO's performance is grossly inaccurate. The minimum ROI size is shown to increase with number of channels and be dictated by truncation of lower frequency filters. We developed a model to estimate the minimum ROI size as a parameterized function of the number of orientations and spatial frequencies of the Gabor filters, providing a guide for investigators to appropriately select parameters for model observer studies.

Practical implementation of Channelized Hotelling Observers: Effect of ROI size.

PubMed

Ferrero, Andrea; Favazza, Christopher P; Yu, Lifeng; Leng, Shuai; McCollough, Cynthia H

2017-03-01

Fundamental to the development and application of channelized Hotelling observer (CHO) models is the selection of the region of interest (ROI) to evaluate. For assessment of medical imaging systems, reducing the ROI size can be advantageous. Smaller ROIs enable a greater concentration of interrogable objects in a single phantom image, thereby providing more information from a set of images and reducing the overall image acquisition burden. Additionally, smaller ROIs may promote better assessment of clinical patient images as different patient anatomies present different ROI constraints. To this end, we investigated the minimum ROI size that does not compromise the performance of the CHO model. In this study, we evaluated both simulated images and phantom CT images to identify the minimum ROI size that resulted in an accurate figure of merit (FOM) of the CHO's performance. More specifically, the minimum ROI size was evaluated as a function of the following: number of channels, spatial frequency and number of rotations of the Gabor filters, size and contrast of the object, and magnitude of the image noise. Results demonstrate that a minimum ROI size exists below which the CHO's performance is grossly inaccurate. The minimum ROI size is shown to increase with number of channels and be dictated by truncation of lower frequency filters. We developed a model to estimate the minimum ROI size as a parameterized function of the number of orientations and spatial frequencies of the Gabor filters, providing a guide for investigators to appropriately select parameters for model observer studies.

Automatic intrinsic cardiac and respiratory gating from cone-beam CT scans of the thorax region

NASA Astrophysics Data System (ADS)

Hahn, Andreas; Sauppe, Sebastian; Lell, Michael; Kachelrieß, Marc

2016-03-01

We present a new algorithm that allows for raw data-based automated cardiac and respiratory intrinsic gating in cone-beam CT scans. It can be summarized in three steps: First, a median filter is applied to an initially reconstructed volume. The forward projection of this volume contains less motion information and is subtracted from the original projections. This results in new raw data that contain only moving and not static anatomy like bones, that would otherwise impede the cardiac or respiratory signal acquisition. All further steps are applied to these modified raw data. Second, the raw data are cropped to a region of interest (ROI). The ROI in the raw data is determined by the forward projection of a binary volume of interest (VOI) that includes the diaphragm for respiratory gating and most of the edge of the heart for cardiac gating. Third, the mean gray value in this ROI is calculated for every projection and the respiratory/cardiac signal is acquired using a bandpass filter. Steps two and three are carried out simultaneously for 64 or 1440 overlapping VOI inside the body for the respiratory or cardiac signal respectively. The signals acquired from each ROI are compared and the most consistent one is chosen as the desired cardiac or respiratory motion signal. Consistency is assessed by the standard deviation of the time between two maxima. The robustness and efficiency of the method is evaluated using simulated and measured patient data by computing the standard deviation of the mean signal difference between the ground truth and the intrinsic signal.

Initial Validation for the Estimation of Resting-State fMRI Effective Connectivity by a Generalization of the Correlation Approach.

PubMed

Xu, Nan; Spreng, R Nathan; Doerschuk, Peter C

2017-01-01

Resting-state functional MRI (rs-fMRI) is widely used to noninvasively study human brain networks. Network functional connectivity is often estimated by calculating the timeseries correlation between blood-oxygen-level dependent (BOLD) signal from different regions of interest (ROIs). However, standard correlation cannot characterize the direction of information flow between regions. In this paper, we introduce and test a new concept, prediction correlation, to estimate effective connectivity in functional brain networks from rs-fMRI. In this approach, the correlation between two BOLD signals is replaced by a correlation between one BOLD signal and a prediction of this signal via a causal system driven by another BOLD signal. Three validations are described: (1) Prediction correlation performed well on simulated data where the ground truth was known, and outperformed four other methods. (2) On simulated data designed to display the "common driver" problem, prediction correlation did not introduce false connections between non-interacting driven ROIs. (3) On experimental data, prediction correlation recovered the previously identified network organization of human brain. Prediction correlation scales well to work with hundreds of ROIs, enabling it to assess whole brain interregional connectivity at the single subject level. These results provide an initial validation that prediction correlation can capture the direction of information flow and estimate the duration of extended temporal delays in information flow between regions of interest ROIs based on BOLD signal. This approach not only maintains the high sensitivity to network connectivity provided by the correlation analysis, but also performs well in the estimation of causal information flow in the brain.

Estimating abdominal adipose tissue with DXA and anthropometry.

PubMed

Hill, Alison M; LaForgia, Joe; Coates, Alison M; Buckley, Jonathan D; Howe, Peter R C

2007-02-01

To identify an anatomically defined region of interest (ROI) from DXA assessment of body composition that when combined with anthropometry can be used to accurately predict intra-abdominal adipose tissue (IAAT) in overweight/obese individuals. Forty-one postmenopausal women (age, 49 to 66 years; BMI, 26 to 37 kg/m(2)) underwent anthropometric and body composition assessments. ROI were defined as quadrilateral boxes extending 5 or 10 cm above the iliac crest and laterally to the edges of the abdominal soft tissue. A single-slice computed tomography (CT) scan was measured at the L3 to L4 intervertebral space, and abdominal skinfolds were taken. Forward step-wise regression revealed the best predictor model of IAAT area measured by CT (r(2) = 0.68, standard error of estimate = 17%) to be: IAAT area (centimeters squared) = 51.844 + DXA 10-cm ROI (grams) (0.031) + abdominal skinfold (millimeters) (1.342). Interobserver reliability for fat mass (r = 0.994; coefficient of variation, 2.60%) and lean mass (r = 0.986, coefficient of variation, 2.67%) in the DXA 10-cm ROI was excellent. This study has identified a DXA ROI that can be reliably measured using prominent anatomical landmarks, in this case, the iliac crest. Using this ROI, combined with an abdominal skinfold measurement, we have derived an equation to predict IAAT in overweight/obese postmenopausal women. This approach offers a simpler, safer, and more cost-effective method than CT for assessing the efficacy of lifestyle interventions aimed at reducing IAAT. However, this warrants further investigation and validation with an independent cohort.

Computer-aided classification of mammographic masses using the deep learning technology: a preliminary study

NASA Astrophysics Data System (ADS)

Qiu, Yuchen; Yan, Shiju; Tan, Maxine; Cheng, Samuel; Liu, Hong; Zheng, Bin

2016-03-01

Although mammography is the only clinically acceptable imaging modality used in the population-based breast cancer screening, its efficacy is quite controversy. One of the major challenges is how to help radiologists more accurately classify between benign and malignant lesions. The purpose of this study is to investigate a new mammographic mass classification scheme based on a deep learning method. In this study, we used an image dataset involving 560 regions of interest (ROIs) extracted from digital mammograms, which includes 280 malignant and 280 benign mass ROIs, respectively. An eight layer deep learning network was applied, which employs three pairs of convolution-max-pooling layers for automatic feature extraction and a multiple layer perception (MLP) classifier for feature categorization. In order to improve robustness of selected features, each convolution layer is connected with a max-pooling layer. A number of 20, 10, and 5 feature maps were utilized for the 1st, 2nd and 3rd convolution layer, respectively. The convolution networks are followed by a MLP classifier, which generates a classification score to predict likelihood of a ROI depicting a malignant mass. Among 560 ROIs, 420 ROIs were used as a training dataset and the remaining 140 ROIs were used as a validation dataset. The result shows that the new deep learning based classifier yielded an area under the receiver operation characteristic curve (AUC) of 0.810+/-0.036. This study demonstrated the potential superiority of using a deep learning based classifier to distinguish malignant and benign breast masses without segmenting the lesions and extracting the pre-defined image features.

Analyzing brain networks with PCA and conditional Granger causality.

PubMed

Zhou, Zhenyu; Chen, Yonghong; Ding, Mingzhou; Wright, Paul; Lu, Zuhong; Liu, Yijun

2009-07-01

Identifying directional influences in anatomical and functional circuits presents one of the greatest challenges for understanding neural computations in the brain. Granger causality mapping (GCM) derived from vector autoregressive models of data has been employed for this purpose, revealing complex temporal and spatial dynamics underlying cognitive processes. However, the traditional GCM methods are computationally expensive, as signals from thousands of voxels within selected regions of interest (ROIs) are individually processed, and being based on pairwise Granger causality, they lack the ability to distinguish direct from indirect connectivity among brain regions. In this work a new algorithm called PCA based conditional GCM is proposed to overcome these problems. The algorithm implements the following two procedures: (i) dimensionality reduction in ROIs of interest with principle component analysis (PCA), and (ii) estimation of the direct causal influences in local brain networks, using conditional Granger causality. Our results show that the proposed method achieves greater accuracy in detecting network connectivity than the commonly used pairwise Granger causality method. Furthermore, the use of PCA components in conjunction with conditional GCM greatly reduces the computational cost relative to the use of individual voxel time series. Copyright 2009 Wiley-Liss, Inc

Effects of global signal regression and subtraction methods on resting-state functional connectivity using arterial spin labeling data.

PubMed

Silva, João Paulo Santos; Mônaco, Luciana da Mata; Paschoal, André Monteiro; Oliveira, Ícaro Agenor Ferreira de; Leoni, Renata Ferranti

2018-05-16

Arterial spin labeling (ASL) is an established magnetic resonance imaging (MRI) technique that is finding broader applications in functional studies of the healthy and diseased brain. To promote improvement in cerebral blood flow (CBF) signal specificity, many algorithms and imaging procedures, such as subtraction methods, were proposed to eliminate or, at least, minimize noise sources. Therefore, this study addressed the main considerations of how CBF functional connectivity (FC) is changed, regarding resting brain network (RBN) identification and correlations between regions of interest (ROI), by different subtraction methods and removal of residual motion artifacts and global signal fluctuations (RMAGSF). Twenty young healthy participants (13 M/7F, mean age = 25 ± 3 years) underwent an MRI protocol with a pseudo-continuous ASL (pCASL) sequence. Perfusion-based images were obtained using simple, sinc and running subtraction. RMAGSF removal was applied to all CBF time series. Independent Component Analysis (ICA) was used for RBN identification, while Pearson' correlation was performed for ROI-based FC analysis. Temporal signal-to-noise ratio (tSNR) was higher in CBF maps obtained by sinc subtraction, although RMAGSF removal had a significant effect on maps obtained with simple and running subtractions. Neither the subtraction method nor the RMAGSF removal directly affected the identification of RBNs. However, the number of correlated and anti-correlated voxels varied for different subtraction and filtering methods. In an ROI-to-ROI level, changes were prominent in FC values and their statistical significance. Our study showed that both RMAGSF filtering and subtraction method might influence resting-state FC results, especially in an ROI level, consequently affecting FC analysis and its interpretation. Taking our results and the whole discussion together, we understand that for an exploratory assessment of the brain, one could avoid removing RMAGSF to not bias FC measures, but could use sinc subtraction to minimize low-frequency contamination. However, CBF signal specificity and frequency range for filtering purposes still need to be assessed in future studies. Copyright © 2018 Elsevier Inc. All rights reserved.

The diminishing dominance of the dominant hemisphere: Language fMRI in focal epilepsy.

PubMed

Tailby, Chris; Abbott, David F; Jackson, Graeme D

2017-01-01

"Which is the dominant hemisphere?" is a question that arises frequently in patients considered for neurosurgery. The concept of the dominant hemisphere implies uniformity of language lateralisation throughout the brain. It is increasingly recognised that this is not the case in the healthy control brain, and it is especially not so in neurological diseases such as epilepsy. In the present work we adapt our published objective lateralisation method (based on the construction of laterality curves) for use with sub-lobar cortical, subcortical and cerebellar regions of interest (ROIs). We apply this method to investigate regional lateralisation of language activation in 12 healthy controls and 18 focal epilepsy patients, using three different block design language fMRI paradigms, each tapping different aspects of language processing. We compared lateralisation within each ROI across tasks, and investigated how the quantity of data collected affected the ability to robustly estimate laterality across ROIs. In controls, lateralisation was stronger, and the variance across individuals smaller, in cortical ROIs, particularly in the Inferior Frontal (Broca) region. Lateralisation within temporal ROIs was dependent on the nature of the language task employed. One of the healthy controls was left lateralised anteriorly and right lateralised posteriorly. Consistent with previous work, departures from normality occurred in ~ 15-50% of focal epilepsy patients across the different ROIs, with atypicality most common in the Lateral Temporal (Wernicke) region. Across tasks and ROIs the absolute magnitude of the laterality estimate increased and its across participant variance decreased as more cycles of task and rest were included, stabilising at ~ 4 cycles (~ 4 min of data collection). Our data highlight the importance of considering language as a complex task where lateralisation varies at the subhemispheric scale. This is especially important for presurgical planning for focal resections where the concept of 'hemispheric dominance' may be misleading. This is a precision medicine approach that enables objective evaluation of language dominance within specific brain regions and can reveal surprising and unexpected anomalies that may be clinically important for individual cases.

A flower image retrieval method based on ROI feature.

PubMed

Hong, An-Xiang; Chen, Gang; Li, Jun-Li; Chi, Zhe-Ru; Zhang, Dan

2004-07-01

Flower image retrieval is a very important step for computer-aided plant species recognition. In this paper, we propose an efficient segmentation method based on color clustering and domain knowledge to extract flower regions from flower images. For flower retrieval, we use the color histogram of a flower region to characterize the color features of flower and two shape-based features sets, Centroid-Contour Distance (CCD) and Angle Code Histogram (ACH), to characterize the shape features of a flower contour. Experimental results showed that our flower region extraction method based on color clustering and domain knowledge can produce accurate flower regions. Flower retrieval results on a database of 885 flower images collected from 14 plant species showed that our Region-of-Interest (ROI) based retrieval approach using both color and shape features can perform better than a method based on the global color histogram proposed by Swain and Ballard (1991) and a method based on domain knowledge-driven segmentation and color names proposed by Das et al.(1999).

A robust pointer segmentation in biomedical images toward building a visual ontology for biomedical article retrieval

NASA Astrophysics Data System (ADS)

You, Daekeun; Simpson, Matthew; Antani, Sameer; Demner-Fushman, Dina; Thoma, George R.

2013-01-01

Pointers (arrows and symbols) are frequently used in biomedical images to highlight specific image regions of interest (ROIs) that are mentioned in figure captions and/or text discussion. Detection of pointers is the first step toward extracting relevant visual features from ROIs and combining them with textual descriptions for a multimodal (text and image) biomedical article retrieval system. Recently we developed a pointer recognition algorithm based on an edge-based pointer segmentation method, and subsequently reported improvements made on our initial approach involving the use of Active Shape Models (ASM) for pointer recognition and region growing-based method for pointer segmentation. These methods contributed to improving the recall of pointer recognition but not much to the precision. The method discussed in this article is our recent effort to improve the precision rate. Evaluation performed on two datasets and compared with other pointer segmentation methods show significantly improved precision and the highest F1 score.

A symmetric multivariate leakage correction for MEG connectomes

PubMed Central

Colclough, G.L.; Brookes, M.J.; Smith, S.M.; Woolrich, M.W.

2015-01-01

Ambiguities in the source reconstruction of magnetoencephalographic (MEG) measurements can cause spurious correlations between estimated source time-courses. In this paper, we propose a symmetric orthogonalisation method to correct for these artificial correlations between a set of multiple regions of interest (ROIs). This process enables the straightforward application of network modelling methods, including partial correlation or multivariate autoregressive modelling, to infer connectomes, or functional networks, from the corrected ROIs. Here, we apply the correction to simulated MEG recordings of simple networks and to a resting-state dataset collected from eight subjects, before computing the partial correlations between power envelopes of the corrected ROItime-courses. We show accurate reconstruction of our simulated networks, and in the analysis of real MEGresting-state connectivity, we find dense bilateral connections within the motor and visual networks, together with longer-range direct fronto-parietal connections. PMID:25862259

Natural scene logo recognition by joint boosting feature selection in salient regions

NASA Astrophysics Data System (ADS)

Fan, Wei; Sun, Jun; Naoi, Satoshi; Minagawa, Akihiro; Hotta, Yoshinobu

2011-01-01

Logos are considered valuable intellectual properties and a key component of the goodwill of a business. In this paper, we propose a natural scene logo recognition method which is segmentation-free and capable of processing images extremely rapidly and achieving high recognition rates. The classifiers for each logo are trained jointly, rather than independently. In this way, common features can be shared across multiple classes for better generalization. To deal with large range of aspect ratio of different logos, a set of salient regions of interest (ROI) are extracted to describe each class. We ensure the selected ROIs to be both individually informative and two-by-two weakly dependant by a Class Conditional Entropy Maximization criteria. Experimental results on a large logo database demonstrate the effectiveness and efficiency of our proposed method.

Relative equilibrium plot improves graphical analysis and allows bias correction of SUVR in quantitative [11C]PiB PET studies

PubMed Central

Zhou, Yun; Sojkova, Jitka; Resnick, Susan M.; Wong, Dean F.

2012-01-01

Both the standardized uptake value ratio (SUVR) and the Logan plot result in biased distribution volume ratios (DVR) in ligand-receptor dynamic PET studies. The objective of this study is to use a recently developed relative equilibrium-based graphical plot (RE plot) method to improve and simplify the two commonly used methods for quantification of [11C]PiB PET. Methods The overestimation of DVR in SUVR was analyzed theoretically using the Logan and the RE plots. A bias-corrected SUVR (bcSUVR) was derived from the RE plot. Seventy-eight [11C]PiB dynamic PET scans (66 from controls and 12 from mildly cognitively impaired participants (MCI) from the Baltimore Longitudinal Study of Aging (BLSA)) were acquired over 90 minutes. Regions of interest (ROIs) were defined on coregistered MRIs. Both the ROI and pixelwise time activity curves (TACs) were used to evaluate the estimates of DVR. DVRs obtained using the Logan plot applied to ROI TACs were used as a reference for comparison of DVR estimates. Results Results from the theoretical analysis were confirmed by human studies. ROI estimates from the RE plot and the bcSUVR were nearly identical to those from the Logan plot with ROI TACs. In contrast, ROI estimates from DVR images in frontal, temporal, parietal, cingulate regions, and the striatum were underestimated by the Logan plot (controls 4 – 12%; MCI 9 – 16%) and overestimated by the SUVR (controls 8 – 16%; MCI 16 – 24%). This bias was higher in the MCI group than in controls (p < 0.01) but was not present when data were analyzed using either the RE plot or the bcSUVR. Conclusion The RE plot improves pixel-wise quantification of [11C]PiB dynamic PET compared to the conventional Logan plot. The bcSUVR results in lower bias and higher consistency of DVR estimates compared to SUVR. The RE plot and the bcSUVR are practical quantitative approaches that improve the analysis of [11C]PiB studies. PMID:22414634

A study on the characteristics of retrospective optimal interpolation using an Observing System Simulation Experiment

NASA Astrophysics Data System (ADS)

Kim, Shin-Woo; Noh, Nam-Kyu; Lim, Gyu-Ho

2013-04-01

This study presents the introduction of retrospective optimal interpolation (ROI) and its application with Weather Research and Forecasting model (WRF). Song et al. (2009) suggested ROI method which is an optimal interpolation (OI) that gradually assimilates observations over the analysis window for variance-minimum estimate of an atmospheric state at the initial time of the analysis window. The assimilation window of ROI algorithm is gradually increased, similar with that of the quasi-static variational assimilation (QSVA; Pires et al., 1996). Unlike QSVA method, however, ROI method assimilates the data at post analysis time using perturbation method (Verlaan and Heemink, 1997) without adjoint model. Song and Lim (2011) improved this method by incorporating eigen-decomposition and covariance inflation. The computational costs for ROI can be reduced due to the eigen-decomposition of background error covariance which can concentrate ROI analyses on the error variances of governing eigenmodes by transforming the control variables into eigenspace. A total energy norm is used for the normalization of each control variables. In this study, ROI method is applied to WRF model with Observing System Simulation Experiment (OSSE) to validate the algorithm and to investigate the capability. Horizontal wind, pressure, potential temperature, and water vapor mixing ratio are used for control variables and observations. Firstly, 1-profile assimilation experiment is performed. Subsequently, OSSE's are performed using the virtual observing system which consists of synop, ship, and sonde data. The difference between forecast errors with assimilation and without assimilation is obviously increased as time passed, which means the improvement of forecast error with the assimilation by ROI. The characteristics and strength/weakness of ROI method are also investigated by conducting the experiments with 3D-Var (3-dimensional variational) method and 4D-Var (4-dimensional variational) method. In the initial time, ROI produces a larger forecast error than that of 4D-Var. However, the difference between the two experimental results is decreased gradually with time, and the ROI shows apparently better result (i.e., smaller forecast error) than that of 4D-Var after 9-hour forecast.

[A quality controllable algorithm for ECG compression based on wavelet transform and ROI coding].

PubMed

Zhao, An; Wu, Baoming

2006-12-01

This paper presents an ECG compression algorithm based on wavelet transform and region of interest (ROI) coding. The algorithm has realized near-lossless coding in ROI and quality controllable lossy coding outside of ROI. After mean removal of the original signal, multi-layer orthogonal discrete wavelet transform is performed. Simultaneously,feature extraction is performed on the original signal to find the position of ROI. The coefficients related to the ROI are important coefficients and kept. Otherwise, the energy loss of the transform domain is calculated according to the goal PRDBE (Percentage Root-mean-square Difference with Baseline Eliminated), and then the threshold of the coefficients outside of ROI is determined according to the loss of energy. The important coefficients, which include the coefficients of ROI and the coefficients that are larger than the threshold outside of ROI, are put into a linear quantifier. The map, which records the positions of the important coefficients in the original wavelet coefficients vector, is compressed with a run-length encoder. Huffman coding has been applied to improve the compression ratio. ECG signals taken from the MIT/BIH arrhythmia database are tested, and satisfactory results in terms of clinical information preserving, quality and compress ratio are obtained.

Optical 3D watermark based digital image watermarking for telemedicine

NASA Astrophysics Data System (ADS)

Li, Xiao Wei; Kim, Seok Tae

2013-12-01

Region of interest (ROI) of a medical image is an area including important diagnostic information and must be stored without any distortion. This algorithm for application of watermarking technique for non-ROI of the medical image preserving ROI. The paper presents a 3D watermark based medical image watermarking scheme. In this paper, a 3D watermark object is first decomposed into 2D elemental image array (EIA) by a lenslet array, and then the 2D elemental image array data is embedded into the host image. The watermark extraction process is an inverse process of embedding. The extracted EIA through the computational integral imaging reconstruction (CIIR) technique, the 3D watermark can be reconstructed. Because the EIA is composed of a number of elemental images possesses their own perspectives of a 3D watermark object. Even though the embedded watermark data badly damaged, the 3D virtual watermark can be successfully reconstructed. Furthermore, using CAT with various rule number parameters, it is possible to get many channels for embedding. So our method can recover the weak point having only one transform plane in traditional watermarking methods. The effectiveness of the proposed watermarking scheme is demonstrated with the aid of experimental results.

Practical implementation of Channelized Hotelling Observers: Effect of ROI size

PubMed Central

Yu, Lifeng; Leng, Shuai; McCollough, Cynthia H.

2017-01-01

Fundamental to the development and application of channelized Hotelling observer (CHO) models is the selection of the region of interest (ROI) to evaluate. For assessment of medical imaging systems, reducing the ROI size can be advantageous. Smaller ROIs enable a greater concentration of interrogable objects in a single phantom image, thereby providing more information from a set of images and reducing the overall image acquisition burden. Additionally, smaller ROIs may promote better assessment of clinical patient images as different patient anatomies present different ROI constraints. To this end, we investigated the minimum ROI size that does not compromise the performance of the CHO model. In this study, we evaluated both simulated images and phantom CT images to identify the minimum ROI size that resulted in an accurate figure of merit (FOM) of the CHO’s performance. More specifically, the minimum ROI size was evaluated as a function of the following: number of channels, spatial frequency and number of rotations of the Gabor filters, size and contrast of the object, and magnitude of the image noise. Results demonstrate that a minimum ROI size exists below which the CHO’s performance is grossly inaccurate. The minimum ROI size is shown to increase with number of channels and be dictated by truncation of lower frequency filters. We developed a model to estimate the minimum ROI size as a parameterized function of the number of orientations and spatial frequencies of the Gabor filters, providing a guide for investigators to appropriately select parameters for model observer studies. PMID:28943699

First-Pass Angiography in Mice Using FDG-PET: A Simple Method of Deriving the Cardiovascular Transit Time Without the Need of Region-of-Interest Drawing.

PubMed

Wu, Hsiao-Ming; Kreissl, Michael C; Schelbert, Heinrich R; Ladno, Waldemar; Prins, Mayumi; Shoghi-Jadid, Kooresh; Chatziioannou, Arion; Phelps, Michael E; Huang, Sung-Cheng

2005-10-01

In this study, we developed a simple and robust semi-automatic method to measure the right ventricle to left ventricle (RV-to-LV) transit time (TT) in mice using 2-[ 18 F]fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET). The accuracy of the method was first evaluated using a 4-D digital dynamic mouse phantom. The RV-to-LV TTs of twenty-nine mouse studies were measured using the new method and compared to those obtained from the conventional ROI-drawing method. The results showed that the new method correctly separated different structures (e.g., RV, lung, and LV) in the PET images and generated corresponding time activity curve (TAC) of each structure. The RV-to-LV TTs obtained from the new method and ROI method were not statistically different (P = 0.20; r = 0.76). We expect that this fast and robust method is applicable to the pathophysiology of cardiovascular diseases using small animal models such as rats and mice.

Video-based respiration monitoring with automatic region of interest detection.

PubMed

Janssen, Rik; Wang, Wenjin; Moço, Andreia; de Haan, Gerard

2016-01-01

Vital signs monitoring is ubiquitous in clinical environments and emerging in home-based healthcare applications. Still, since current monitoring methods require uncomfortable sensors, respiration rate remains the least measured vital sign. In this paper, we propose a video-based respiration monitoring method that automatically detects a respiratory region of interest (RoI) and signal using a camera. Based on the observation that respiration induced chest/abdomen motion is an independent motion system in a video, our basic idea is to exploit the intrinsic properties of respiration to find the respiratory RoI and extract the respiratory signal via motion factorization. We created a benchmark dataset containing 148 video sequences obtained on adults under challenging conditions and also neonates in the neonatal intensive care unit (NICU). The measurements obtained by the proposed video respiration monitoring (VRM) method are not significantly different from the reference methods (guided breathing or contact-based ECG; p-value  =  0.6), and explain more than 99% of the variance of the reference values with low limits of agreement (-2.67 to 2.81 bpm). VRM seems to provide a valid solution to ECG in confined motion scenarios, though precision may be reduced for neonates. More studies are needed to validate VRM under challenging recording conditions, including upper-body motion types.

A study on characteristics of retrospective optimal interpolation with WRF testbed

NASA Astrophysics Data System (ADS)

Kim, S.; Noh, N.; Lim, G.

2012-12-01

This study presents the application of retrospective optimal interpolation (ROI) with Weather Research and Forecasting model (WRF). Song et al. (2009) suggest ROI method which is an optimal interpolation (OI) that gradually assimilates observations over the analysis window for variance-minimum estimate of an atmospheric state at the initial time of the analysis window. Song and Lim (2011) improve the method by incorporating eigen-decomposition and covariance inflation. ROI method assimilates the data at post analysis time using perturbation method (Errico and Raeder, 1999) without adjoint model. In this study, ROI method is applied to WRF model to validate the algorithm and to investigate the capability. The computational costs for ROI can be reduced due to the eigen-decomposition of background error covariance. Using the background error covariance in eigen-space, 1-profile assimilation experiment is performed. The difference between forecast errors with assimilation and without assimilation is obviously increased as time passed, which means the improvement of forecast error by assimilation. The characteristics and strength/weakness of ROI method are investigated by conducting the experiments with other data assimilation method.

A comparison of methods to evaluate gray scale response of tomosynthesis systems using a software breast phantom

NASA Astrophysics Data System (ADS)

Sousa, Maria A. Z.; Bakic, Predrag R.; Schiabel, Homero; Maidment, Andrew D. A.

2017-03-01

Digital breast tomosynthesis (DBT) has been shown to be an effective imaging tool for breast cancer diagnosis as it provides three-dimensional images of the breast with minimal tissue overlap. The quality of the reconstructed image depends on many factors that can be assessed using uniform or realistic phantoms. In this paper, we created four models of phantoms using an anthropomorphic software breast phantom and compared four methods to evaluate the gray scale response in terms of the contrast, noise and detectability of adipose and glandular tissues binarized according to phantom ground truth. For each method, circular regions of interest (ROIs) were selected with various sizes, quantity and positions inside a square area in the phantom. We also estimated the percent density of the simulated breast and the capability of distinguishing both tissues by receiver operating characteristic (ROC) analysis. Results shows a sensitivity of the methods to the ROI size, placement and to the slices considered.

Finger vein verification system based on sparse representation.

PubMed

Xin, Yang; Liu, Zhi; Zhang, Haixia; Zhang, Hong

2012-09-01

Finger vein verification is a promising biometric pattern for personal identification in terms of security and convenience. The recognition performance of this technology heavily relies on the quality of finger vein images and on the recognition algorithm. To achieve efficient recognition performance, a special finger vein imaging device is developed, and a finger vein recognition method based on sparse representation is proposed. The motivation for the proposed method is that finger vein images exhibit a sparse property. In the proposed system, the regions of interest (ROIs) in the finger vein images are segmented and enhanced. Sparse representation and sparsity preserving projection on ROIs are performed to obtain the features. Finally, the features are measured for recognition. An equal error rate of 0.017% was achieved based on the finger vein image database, which contains images that were captured by using the near-IR imaging device that was developed in this study. The experimental results demonstrate that the proposed method is faster and more robust than previous methods.

Sensor-Oriented Path Planning for Multiregion Surveillance with a Single Lightweight UAV SAR

PubMed Central

Li, Jincheng; Chen, Jie; Wang, Pengbo; Li, Chunsheng

2018-01-01

In the surveillance of interested regions by unmanned aerial vehicle (UAV), system performance relies greatly on the motion control strategy of the UAV and the operation characteristics of the onboard sensors. This paper investigates the 2D path planning problem for the lightweight UAV synthetic aperture radar (SAR) system in an environment of multiple regions of interest (ROIs), the sizes of which are comparable to the radar swath width. Taking into account the special requirements of the SAR system on the motion of the platform, we model path planning for UAV SAR as a constrained multiobjective optimization problem (MOP). Based on the fact that the UAV route can be designed in the map image, an image-based path planner is proposed in this paper. First, the neighboring ROIs are merged by the morphological operation. Then, the parts of routes for data collection of the ROIs can be located according to the geometric features of the ROIs and the observation geometry of UAV SAR. Lastly, the route segments for ROIs surveillance are connected by a path planning algorithm named the sampling-based sparse A* search (SSAS) algorithm. Simulation experiments in real scenarios demonstrate that the proposed sensor-oriented path planner can improve the reconnaissance performance of lightweight UAV SAR greatly compared with the conventional zigzag path planner. PMID:29439447

Sensor-Oriented Path Planning for Multiregion Surveillance with a Single Lightweight UAV SAR.

PubMed

Li, Jincheng; Chen, Jie; Wang, Pengbo; Li, Chunsheng

2018-02-11

In the surveillance of interested regions by unmanned aerial vehicle (UAV), system performance relies greatly on the motion control strategy of the UAV and the operation characteristics of the onboard sensors. This paper investigates the 2D path planning problem for the lightweight UAV synthetic aperture radar (SAR) system in an environment of multiple regions of interest (ROIs), the sizes of which are comparable to the radar swath width. Taking into account the special requirements of the SAR system on the motion of the platform, we model path planning for UAV SAR as a constrained multiobjective optimization problem (MOP). Based on the fact that the UAV route can be designed in the map image, an image-based path planner is proposed in this paper. First, the neighboring ROIs are merged by the morphological operation. Then, the parts of routes for data collection of the ROIs can be located according to the geometric features of the ROIs and the observation geometry of UAV SAR. Lastly, the route segments for ROIs surveillance are connected by a path planning algorithm named the sampling-based sparse A* search (SSAS) algorithm. Simulation experiments in real scenarios demonstrate that the proposed sensor-oriented path planner can improve the reconnaissance performance of lightweight UAV SAR greatly compared with the conventional zigzag path planner.

TU-AB-BRA-12: Impact of Image Registration Algorithms On the Prediction of Pathological Response with Radiomic Textures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yip, S; Coroller, T; Niu, N

2015-06-15

Purpose: Tumor regions-of-interest (ROI) can be propagated from the pre-onto the post-treatment PET/CT images using image registration of their CT counterparts, providing an automatic way to compute texture features on longitudinal scans. This exploratory study assessed the impact of image registration algorithms on textures to predict pathological response. Methods: Forty-six esophageal cancer patients (1 tumor/patient) underwent PET/CT scans before and after chemoradiotherapy. Patients were classified into responders and non-responders after the surgery. Physician-defined tumor ROIs on pre-treatment PET were propagated onto the post-treatment PET using rigid and ten deformable registration algorithms. One co-occurrence, two run-length and size zone matrix texturesmore » were computed within all ROIs. The relative difference of each texture at different treatment time-points was used to predict the pathologic responders. Their predictive value was assessed using the area under the receiver-operating-characteristic curve (AUC). Propagated ROIs and texture quantification resulting from different algorithms were compared using overlap volume (OV) and coefficient of variation (CoV), respectively. Results: Tumor volumes were better captured by ROIs propagated by deformable rather than the rigid registration. The OV between rigidly and deformably propagated ROIs were 69%. The deformably propagated ROIs were found to be similar (OV∼80%) except for fast-demons (OV∼60%). Rigidly propagated ROIs with run-length matrix textures failed to significantly differentiate between responders and non-responders (AUC=0.65, p=0.07), while the differentiation was significant with other textures (AUC=0.69–0.72, p<0.03). Among the deformable algorithms, fast-demons was the least predictive (AUC=0.68–0.71, p<0.04). ROIs propagated by all other deformable algorithms with any texture significantly predicted pathologic responders (AUC=0.71–0.78, p<0.01) despite substantial variation in texture quantification (CoV>70%). Conclusion: Propagated ROIs using deformable registration for all textures can lead to accurate prediction of pathologic response, potentially expediting the temporal texture analysis process. However, rigid and fast-demons deformable algorithms are not recommended due to their inferior performance compared to other algorithms. The project was supported in part by a Kaye Scholar Award.« less

Application of a dual-resolution voxelization scheme to compressed-sensing (CS)-based iterative reconstruction in digital tomosynthesis (DTS)

NASA Astrophysics Data System (ADS)

Park, S. Y.; Kim, G. A.; Cho, H. S.; Park, C. K.; Lee, D. Y.; Lim, H. W.; Lee, H. W.; Kim, K. S.; Kang, S. Y.; Park, J. E.; Kim, W. S.; Jeon, D. H.; Je, U. K.; Woo, T. H.; Oh, J. E.

2018-02-01

In recent digital tomosynthesis (DTS), iterative reconstruction methods are often used owing to the potential to provide multiplanar images of superior image quality to conventional filtered-backprojection (FBP)-based methods. However, they require enormous computational cost in the iterative process, which has still been an obstacle to put them to practical use. In this work, we propose a new DTS reconstruction method incorporated with a dual-resolution voxelization scheme in attempt to overcome these difficulties, in which the voxels outside a small region-of-interest (ROI) containing target diagnosis are binned by 2 × 2 × 2 while the voxels inside the ROI remain unbinned. We considered a compressed-sensing (CS)-based iterative algorithm with a dual-constraint strategy for more accurate DTS reconstruction. We implemented the proposed algorithm and performed a systematic simulation and experiment to demonstrate its viability. Our results indicate that the proposed method seems to be effective for reducing computational cost considerably in iterative DTS reconstruction, keeping the image quality inside the ROI not much degraded. A binning size of 2 × 2 × 2 required only about 31.9% computational memory and about 2.6% reconstruction time, compared to those for no binning case. The reconstruction quality was evaluated in terms of the root-mean-square error (RMSE), the contrast-to-noise ratio (CNR), and the universal-quality index (UQI).

A hybrid correlation analysis with application to imaging genetics

NASA Astrophysics Data System (ADS)

Hu, Wenxing; Fang, Jian; Calhoun, Vince D.; Wang, Yu-Ping

2018-03-01

Investigating the association between brain regions and genes continues to be a challenging topic in imaging genetics. Current brain region of interest (ROI)-gene association studies normally reduce data dimension by averaging the value of voxels in each ROI. This averaging may lead to a loss of information due to the existence of functional sub-regions. Pearson correlation is widely used for association analysis. However, it only detects linear correlation whereas nonlinear correlation may exist among ROIs. In this work, we introduced distance correlation to ROI-gene association analysis, which can detect both linear and nonlinear correlations and overcome the limitation of averaging operations by taking advantage of the information at each voxel. Nevertheless, distance correlation usually has a much lower value than Pearson correlation. To address this problem, we proposed a hybrid correlation analysis approach, by applying canonical correlation analysis (CCA) to the distance covariance matrix instead of directly computing distance correlation. Incorporating CCA into distance correlation approach may be more suitable for complex disease study because it can detect highly associated pairs of ROI and gene groups, and may improve the distance correlation level and statistical power. In addition, we developed a novel nonlinear CCA, called distance kernel CCA, which seeks the optimal combination of features with the most significant dependence. This approach was applied to imaging genetic data from the Philadelphia Neurodevelopmental Cohort (PNC). Experiments showed that our hybrid approach produced more consistent results than conventional CCA across resampling and both the correlation and statistical significance were increased compared to distance correlation analysis. Further gene enrichment analysis and region of interest (ROI) analysis confirmed the associations of the identified genes with brain ROIs. Therefore, our approach provides a powerful tool for finding the correlation between brain imaging and genomic data.

Quantitative accuracy of the closed-form least-squares solution for targeted SPECT.

PubMed

Shcherbinin, S; Celler, A

2010-10-07

The aim of this study is to investigate the quantitative accuracy of the closed-form least-squares solution (LSS) for single photon emission computed tomography (SPECT). The main limitation for employing this method in actual clinical reconstructions is the computational cost related to operations with a large-sized system matrix. However, in some clinical situations, the size of the system matrix can be decreased using targeted reconstruction. For example, some oncology SPECT studies are characterized by intense tracer uptakes that are localized in relatively small areas, while the remaining parts of the patient body have only a low activity background. Conventional procedures reconstruct the activity distribution in the whole object, which leads to relatively poor image accuracy/resolution for tumors while computer resources are wasted, trying to rebuild diagnostically useless background. In this study, we apply a concept of targeted reconstruction to SPECT phantom experiments imitating such oncology scans. Our approach includes two major components: (i) disconnection of the entire imaging system of equations and extraction of only those parts that correspond to the targets, i.e., regions of interest (ROI) encompassing active containers/tumors and (ii) generation of the closed-form LSS for each target ROI. We compared these ROI-based LSS with those reconstructed by the conventional MLEM approach. The analysis of the five processed cases from two phantom experiments demonstrated that the LSS approach outperformed MLEM in terms of the noise level inside ROI. On the other hand, MLEM better recovered total activity if the number of iterations was large enough. For the experiment without background activity, the ROI-based LSS led to noticeably better spatial activity distribution inside ROI. However, the distributions pertaining to both approaches were practically identical for the experiment with the concentration ratio 7:1 between the containers and the background.

Quantitative assessment of colorectal cancer tumor vascular parameters by using perfusion CT: influence of tumor region of interest.

PubMed

Goh, Vicky; Halligan, Steve; Gharpuray, Anita; Wellsted, David; Sundin, Josefin; Bartram, Clive I

2008-06-01

To prospectively determine whether position and size of tumor region of interest (ROI) influence estimates of colorectal cancer vascular parameters at computed tomography (CT). After institutional review board approval and informed consent, 25 men and 22 women (mean age, 65.8 years) with colorectal adenocarcinoma underwent 65-second CT perfusion study. Blood volume, blood flow, and permeability-surface area product were determined for 40- or 120-mm(2) circular ROIs placed at the tumor edge and center and around (outlining) visible tumor. ROI analysis was repeated by two observers in different subsets of patients to assess intra- and interobserver variation. Measurements were compared by using analysis of variance; a difference with P = .002 was significant. Blood volume, blood flow, and permeability-surface area product measurements were substantially higher at the edge than at the center for both 40- and 120-mm(2) ROIs. For 40-mm(2) ROI, means of the three measurements were 6.9 mL/100 g (standard deviation [SD], 1.4), 108.7 mL/100 g per minute (SD, 39.2), and 16.9 mL/100 g per minute (SD, 4.2), respectively, at the edge versus 5.1 mL/100 g (SD, 1.5), 56.3 mL/100 g per minute (SD, 33.1), and 13.9 mL/100 g per minute (SD, 4.6), respectively, at the center. For 120-mm(2) ROI, means of the three measurements were 6.6 mL/100 g (SD, 1.3), 96.7 mL/100 g per minute (SD, 42.5), and 16.3 mL/100 g per minute (SD, 5.6), respectively, at the edge versus 5.1 mL/100 g (SD, 1.4), 58.3 mL/100 g per minute (SD, 32.5), and 13.4 mL/100 g per minute (SD, 4.3) at the center (P < .0001). Measurements varied substantially depending on the ROI size; values for the ROI for outlined tumor were intermediate between those at the tumor edge and center. Inter- and intraobserver agreement was poor for both 40- and 120-mm(2) ROIs. Position and size of tumor ROI and observer variation substantially influence ultimate perfusion values. ROI for outlined entire tumor is more reliable for perfusion measurements and more appropriate clinically than use of arbitrarily determined smaller ROIs. (c) RSNA, 2008.

Can state-of-the-art HVS-based objective image quality criteria be used for image reconstruction techniques based on ROI analysis?

NASA Astrophysics Data System (ADS)

Dostal, P.; Krasula, L.; Klima, M.

2012-06-01

Various image processing techniques in multimedia technology are optimized using visual attention feature of the human visual system. Spatial non-uniformity causes that different locations in an image are of different importance in terms of perception of the image. In other words, the perceived image quality depends mainly on the quality of important locations known as regions of interest. The performance of such techniques is measured by subjective evaluation or objective image quality criteria. Many state-of-the-art objective metrics are based on HVS properties; SSIM, MS-SSIM based on image structural information, VIF based on the information that human brain can ideally gain from the reference image or FSIM utilizing the low-level features to assign the different importance to each location in the image. But still none of these objective metrics utilize the analysis of regions of interest. We solve the question if these objective metrics can be used for effective evaluation of images reconstructed by processing techniques based on ROI analysis utilizing high-level features. In this paper authors show that the state-of-the-art objective metrics do not correlate well with subjective evaluation while the demosaicing based on ROI analysis is used for reconstruction. The ROI were computed from "ground truth" visual attention data. The algorithm combining two known demosaicing techniques on the basis of ROI location is proposed to reconstruct the ROI in fine quality while the rest of image is reconstructed with low quality. The color image reconstructed by this ROI approach was compared with selected demosaicing techniques by objective criteria and subjective testing. The qualitative comparison of the objective and subjective results indicates that the state-of-the-art objective metrics are still not suitable for evaluation image processing techniques based on ROI analysis and new criteria is demanded.

The Heterogeneity in Retrieved Relations between the Personality Trait ‘Harm Avoidance’ and Gray Matter Volumes Due to Variations in the VBM and ROI Labeling Processing Settings

PubMed Central

Van Schuerbeek, Peter; Baeken, Chris; De Mey, Johan

2016-01-01

Concerns are raising about the large variability in reported correlations between gray matter morphology and affective personality traits as ‘Harm Avoidance’ (HA). A recent review study (Mincic 2015) stipulated that this variability could come from methodological differences between studies. In order to achieve more robust results by standardizing the data processing procedure, as a first step, we repeatedly analyzed data from healthy females while changing the processing settings (voxel-based morphology (VBM) or region-of-interest (ROI) labeling, smoothing filter width, nuisance parameters included in the regression model, brain atlas and multiple comparisons correction method). The heterogeneity in the obtained results clearly illustrate the dependency of the study outcome to the opted analysis settings. Based on our results and the existing literature, we recommended the use of VBM over ROI labeling for whole brain analyses with a small or intermediate smoothing filter (5-8mm) and a model variable selection step included in the processing procedure. Additionally, it is recommended that ROI labeling should only be used in combination with a clear hypothesis and that authors are encouraged to report their results uncorrected for multiple comparisons as supplementary material to aid review studies. PMID:27096608

Cerebro-cerebellar resting state functional connectivity in children and adolescents with autism spectrum disorder

PubMed Central

Khan, Amanda J.; Nair, Aarti; Keown, Christopher L.; Datko, Michael C.; Lincoln, Alan J.; Müller, Ralph-Axel

2017-01-01

Background The cerebellum plays important roles in both sensorimotor and supramodal cognitive functions. Cellular, volumetric, and functional abnormalities of the cerebellum have been found in autism spectrum disorders (ASD), but no comprehensive investigation of cerebro-cerebellar connectivity in ASD is available. Methods We used resting-state functional connectivity MRI in 56 children and adolescents (28 ASD, 28 typically developing [TD]) aged 8–17 years. Partial and total correlation analyses were performed for unilateral regions of interest (ROIs), distinguished in two broad domains as sensorimotor (premotor/primary motor, somatosensory, superior temporal, occipital) and supramodal (prefrontal, posterior parietal, and inferior and middle temporal). Results There were three main findings: (i) Total correlation analyses showed predominant cerebro-cerebellar functional overconnectivity in the ASD group; (ii) partial correlation analyses that emphasized domain-specificity (sensorimotor vs. supramodal) indicated a pattern of robustly increased connectivity in the ASD group (compared to the TD group) for sensorimotor ROIs, but predominantly reduced connectivity for supramodal ROIs; (iii) this atypical pattern of connectivity was supported by significantly increased non-canonical connections (between sensorimotor cerebral and supramodal cerebellar ROIs, and vice versa) in the ASD group. Conclusions Our findings indicate that sensorimotor intrinsic functional connectivity is atypically increased in ASD, at the expense of connectivity supporting cerebellar participation in supramodal cognition. PMID:25959247

Neural Network Target Identification System for False Alarm Reduction

NASA Technical Reports Server (NTRS)

Ye, David; Edens, Weston; Lu, Thomas T.; Chao, Tien-Hsin

2009-01-01

A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feed forward back propagation neural network (NN) is then trained to classify each feature vector and remove false positives. This paper discusses the test of the system performance and parameter optimizations process which adapts the system to various targets and datasets. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar image dataset.

Fast, shape-directed, landmark-based deep gray matter segmentation for quantification of iron deposition

NASA Astrophysics Data System (ADS)

Ekin, Ahmet; Jasinschi, Radu; van der Grond, Jeroen; van Buchem, Mark A.; van Muiswinkel, Arianne

2006-03-01

This paper introduces image processing methods to automatically detect the 3D volume-of-interest (VOI) and 2D region-of-interest (ROI) for deep gray matter organs (thalamus, globus pallidus, putamen, and caudate nucleus) of patients with suspected iron deposition from MR dual echo images. Prior to the VOI and ROI detection, cerebrospinal fluid (CSF) region is segmented by a clustering algorithm. For the segmentation, we automatically determine the cluster centers with the mean shift algorithm that can quickly identify the modes of a distribution. After the identification of the modes, we employ the K-Harmonic means clustering algorithm to segment the volumetric MR data into CSF and non-CSF. Having the CSF mask and observing that the frontal lobe of the lateral ventricle has more consistent shape accross age and pathological abnormalities, we propose a shape-directed landmark detection algorithm to detect the VOI in a speedy manner. The proposed landmark detection algorithm utilizes a novel shape model of the front lobe of the lateral ventricle for the slices where thalamus, globus pallidus, putamen, and caudate nucleus are expected to appear. After this step, for each slice in the VOI, we use horizontal and vertical projections of the CSF map to detect the approximate locations of the relevant organs to define the ROI. We demonstrate the robustness of the proposed VOI and ROI localization algorithms to pathologies, including severe amounts of iron accumulation as well as white matter lesions, and anatomical variations. The proposed algorithms achieved very high detection accuracy, 100% in the VOI detection , over a large set of a challenging MR dataset.

Initial Validation for the Estimation of Resting-State fMRI Effective Connectivity by a Generalization of the Correlation Approach

PubMed Central

Xu, Nan; Spreng, R. Nathan; Doerschuk, Peter C.

2017-01-01

Resting-state functional MRI (rs-fMRI) is widely used to noninvasively study human brain networks. Network functional connectivity is often estimated by calculating the timeseries correlation between blood-oxygen-level dependent (BOLD) signal from different regions of interest (ROIs). However, standard correlation cannot characterize the direction of information flow between regions. In this paper, we introduce and test a new concept, prediction correlation, to estimate effective connectivity in functional brain networks from rs-fMRI. In this approach, the correlation between two BOLD signals is replaced by a correlation between one BOLD signal and a prediction of this signal via a causal system driven by another BOLD signal. Three validations are described: (1) Prediction correlation performed well on simulated data where the ground truth was known, and outperformed four other methods. (2) On simulated data designed to display the “common driver” problem, prediction correlation did not introduce false connections between non-interacting driven ROIs. (3) On experimental data, prediction correlation recovered the previously identified network organization of human brain. Prediction correlation scales well to work with hundreds of ROIs, enabling it to assess whole brain interregional connectivity at the single subject level. These results provide an initial validation that prediction correlation can capture the direction of information flow and estimate the duration of extended temporal delays in information flow between regions of interest ROIs based on BOLD signal. This approach not only maintains the high sensitivity to network connectivity provided by the correlation analysis, but also performs well in the estimation of causal information flow in the brain. PMID:28559793

Localization of Diagnostically Relevant Regions of Interest in Whole Slide Images: a Comparative Study.

PubMed

Mercan, Ezgi; Aksoy, Selim; Shapiro, Linda G; Weaver, Donald L; Brunyé, Tad T; Elmore, Joann G

2016-08-01

Whole slide digital imaging technology enables researchers to study pathologists' interpretive behavior as they view digital slides and gain new understanding of the diagnostic medical decision-making process. In this study, we propose a simple yet important analysis to extract diagnostically relevant regions of interest (ROIs) from tracking records using only pathologists' actions as they viewed biopsy specimens in the whole slide digital imaging format (zooming, panning, and fixating). We use these extracted regions in a visual bag-of-words model based on color and texture features to predict diagnostically relevant ROIs on whole slide images. Using a logistic regression classifier in a cross-validation setting on 240 digital breast biopsy slides and viewport tracking logs of three expert pathologists, we produce probability maps that show 74 % overlap with the actual regions at which pathologists looked. We compare different bag-of-words models by changing dictionary size, visual word definition (patches vs. superpixels), and training data (automatically extracted ROIs vs. manually marked ROIs). This study is a first step in understanding the scanning behaviors of pathologists and the underlying reasons for diagnostic errors.

Liver in the analysis of body composition by dual-energy X-ray absorptiometry

PubMed Central

Bazzocchi, A; Diano, D; Albisinni, U; Marchesini, G; Battista, G

2014-01-01

Objective: To investigate the predictive value for hepatic steatosis of a new software for the quantification of visceral fat by dual-energy X-ray absorptiometry (DXA) and to design new regions of interest (ROIs). Methods: Adult volunteers were prospectively screened for hepatic steatosis by ultrasonography to obtain a well-balanced population according to the presence/absence of the disease. 90 adult patients without steatosis and 90 with steatosis (mild, 53.3%; moderate, 37.7%; and severe, 10.0%) were recruited. On the same day, all subjects were submitted to blood testing and to anthropometric and whole-body DXA for body composition evaluation. A new software for android visceral fat assessment was employed, and six new “liver-suited” ROIs as well as two modified android ROIs were designed. Their association with steatosis grade was tested by correlation analysis. Results: Fat mass (FM) of the new ROIs showed the highest correlation coefficients with steatosis grade (ρ = 0.610–0.619; p < 0.001), which was also confirmed by multivariate analysis. On the whole population, the new ROIs maintained the highest predictive role for liver steatosis, with areas under the receiver operating characteristic curve up to 0.820 ± 0.032. Inter- and intra-operator agreement for the new ROIs was excellent (k = 0.915–1.000 and k = 0.927–1.000). Conclusion: New ROIs could be designed, standardized and implemented in DXA whole-body scan to provide more specific and predictive values of hepatic lipid content. Advances in knowledge: This is the first study to investigate the predictive value for hepatic steatosis of visceral and regional FM assessed on the hepatic site by DXA in comparison with ultrasonography, anthropometry and surrogate markers derived by previously validated algorithms (fatty liver index). PMID:24919499

Quality Assurance Assessment of Diagnostic and Radiation Therapy–Simulation CT Image Registration for Head and Neck Radiation Therapy: Anatomic Region of Interest–based Comparison of Rigid and Deformable Algorithms

PubMed Central

Mohamed, Abdallah S. R.; Ruangskul, Manee-Naad; Awan, Musaddiq J.; Baron, Charles A.; Kalpathy-Cramer, Jayashree; Castillo, Richard; Castillo, Edward; Guerrero, Thomas M.; Kocak-Uzel, Esengul; Yang, Jinzhong; Court, Laurence E.; Kantor, Michael E.; Gunn, G. Brandon; Colen, Rivka R.; Frank, Steven J.; Garden, Adam S.; Rosenthal, David I.

2015-01-01

Purpose To develop a quality assurance (QA) workflow by using a robust, curated, manually segmented anatomic region-of-interest (ROI) library as a benchmark for quantitative assessment of different image registration techniques used for head and neck radiation therapy–simulation computed tomography (CT) with diagnostic CT coregistration. Materials and Methods Radiation therapy–simulation CT images and diagnostic CT images in 20 patients with head and neck squamous cell carcinoma treated with curative-intent intensity-modulated radiation therapy between August 2011 and May 2012 were retrospectively retrieved with institutional review board approval. Sixty-eight reference anatomic ROIs with gross tumor and nodal targets were then manually contoured on images from each examination. Diagnostic CT images were registered with simulation CT images rigidly and by using four deformable image registration (DIR) algorithms: atlas based, B-spline, demons, and optical flow. The resultant deformed ROIs were compared with manually contoured reference ROIs by using similarity coefficient metrics (ie, Dice similarity coefficient) and surface distance metrics (ie, 95% maximum Hausdorff distance). The nonparametric Steel test with control was used to compare different DIR algorithms with rigid image registration (RIR) by using the post hoc Wilcoxon signed-rank test for stratified metric comparison. Results A total of 2720 anatomic and 50 tumor and nodal ROIs were delineated. All DIR algorithms showed improved performance over RIR for anatomic and target ROI conformance, as shown for most comparison metrics (Steel test, P < .008 after Bonferroni correction). The performance of different algorithms varied substantially with stratification by specific anatomic structures or category and simulation CT section thickness. Conclusion Development of a formal ROI-based QA workflow for registration assessment demonstrated improved performance with DIR techniques over RIR. After QA, DIR implementation should be the standard for head and neck diagnostic CT and simulation CT allineation, especially for target delineation. © RSNA, 2014 Online supplemental material is available for this article. PMID:25380454

Defining the most probable location of the parahippocampal place area using cortex-based alignment and cross-validation.

PubMed

Weiner, Kevin S; Barnett, Michael A; Witthoft, Nathan; Golarai, Golijeh; Stigliani, Anthony; Kay, Kendrick N; Gomez, Jesse; Natu, Vaidehi S; Amunts, Katrin; Zilles, Karl; Grill-Spector, Kalanit

2018-04-15

The parahippocampal place area (PPA) is a widely studied high-level visual region in the human brain involved in place and scene processing. The goal of the present study was to identify the most probable location of place-selective voxels in medial ventral temporal cortex. To achieve this goal, we first used cortex-based alignment (CBA) to create a probabilistic place-selective region of interest (ROI) from one group of 12 participants. We then tested how well this ROI could predict place selectivity in each hemisphere within a new group of 12 participants. Our results reveal that a probabilistic ROI (pROI) generated from one group of 12 participants accurately predicts the location and functional selectivity in individual brains from a new group of 12 participants, despite between subject variability in the exact location of place-selective voxels relative to the folding of parahippocampal cortex. Additionally, the prediction accuracy of our pROI is significantly higher than that achieved by volume-based Talairach alignment. Comparing the location of the pROI of the PPA relative to published data from over 500 participants, including data from the Human Connectome Project, shows a striking convergence of the predicted location of the PPA and the cortical location of voxels exhibiting the highest place selectivity across studies using various methods and stimuli. Specifically, the most predictive anatomical location of voxels exhibiting the highest place selectivity in medial ventral temporal cortex is the junction of the collateral and anterior lingual sulci. Methodologically, we make this pROI freely available (vpnl.stanford.edu/PlaceSelectivity), which provides a means to accurately identify a functional region from anatomical MRI data when fMRI data are not available (for example, in patient populations). Theoretically, we consider different anatomical and functional factors that may contribute to the consistent anatomical location of place selectivity relative to the folding of high-level visual cortex. Copyright © 2017 Elsevier Inc. All rights reserved.

Quality optimized medical image information hiding algorithm that employs edge detection and data coding.

PubMed

Al-Dmour, Hayat; Al-Ani, Ahmed

2016-04-01

The present work has the goal of developing a secure medical imaging information system based on a combined steganography and cryptography technique. It attempts to securely embed patient's confidential information into his/her medical images. The proposed information security scheme conceals coded Electronic Patient Records (EPRs) into medical images in order to protect the EPRs' confidentiality without affecting the image quality and particularly the Region of Interest (ROI), which is essential for diagnosis. The secret EPR data is converted into ciphertext using private symmetric encryption method. Since the Human Visual System (HVS) is less sensitive to alterations in sharp regions compared to uniform regions, a simple edge detection method has been introduced to identify and embed in edge pixels, which will lead to an improved stego image quality. In order to increase the embedding capacity, the algorithm embeds variable number of bits (up to 3) in edge pixels based on the strength of edges. Moreover, to increase the efficiency, two message coding mechanisms have been utilized to enhance the ±1 steganography. The first one, which is based on Hamming code, is simple and fast, while the other which is known as the Syndrome Trellis Code (STC), is more sophisticated as it attempts to find a stego image that is close to the cover image through minimizing the embedding impact. The proposed steganography algorithm embeds the secret data bits into the Region of Non Interest (RONI), where due to its importance; the ROI is preserved from modifications. The experimental results demonstrate that the proposed method can embed large amount of secret data without leaving a noticeable distortion in the output image. The effectiveness of the proposed algorithm is also proven using one of the efficient steganalysis techniques. The proposed medical imaging information system proved to be capable of concealing EPR data and producing imperceptible stego images with minimal embedding distortions compared to other existing methods. In order to refrain from introducing any modifications to the ROI, the proposed system only utilizes the Region of Non Interest (RONI) in embedding the EPR data. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Region of interest methylation analysis: a comparison of MSP with MS-HRM and direct BSP.

PubMed

Akika, Reem; Awada, Zainab; Mogharbil, Nahed; Zgheib, Nathalie K

2017-07-01

The aim of this study was to compare and contrast three DNA methylation methods of a specific region of interest (ROI): methylation-specific PCR (MSP), methylation-sensitive high resolution melting (MS-HRM) and direct bisulfite sequencing (BSP). The methylation of a CpG area in the promoter region of Estrogen receptor alpha (ESR1) was evaluated by these three methods with samples and standards of different methylation percentages. MSP data were neither reproducible nor sensitive, and the assay was not specific due to non-specific binding of primers. MS-HRM was highly reproducible and a step forward into categorizing the methylation status of the samples as percent ranges. Direct BSP was the most informative method regarding methylation percentage of each CpG site. Though not perfect, it was reproducible and sensitive. We recommend the use of either method depending on the research question and target amplicon, and provided that the designed primers and expected amplicons are within recommendations. If the research question targets a limited number of CpG sites and simple yes/no results are enough, MSP may be attempted. For short amplicons that are crowded with CpG sites and of single melting domain, MS-HRM may be the method of choice though it only indicates the overall methylation percentage of the entire amplicon. Although the assay is highly reproducible, being semi-quantitative makes it of lesser interest to study ROI methylation of samples with little methylation differences. Direct BSP is a step forward as it gives information about the methylation percentage at each CpG site.

A semi-automated algorithm for hypothalamus volumetry in 3 Tesla magnetic resonance images.

PubMed

Wolff, Julia; Schindler, Stephanie; Lucas, Christian; Binninger, Anne-Sophie; Weinrich, Luise; Schreiber, Jan; Hegerl, Ulrich; Möller, Harald E; Leitzke, Marco; Geyer, Stefan; Schönknecht, Peter

2018-07-30

The hypothalamus, a small diencephalic gray matter structure, is part of the limbic system. Volumetric changes of this structure occur in psychiatric diseases, therefore there is increasing interest in precise volumetry. Based on our detailed volumetry algorithm for 7 Tesla magnetic resonance imaging (MRI), we developed a method for 3 Tesla MRI, adopting anatomical landmarks and work in triplanar view. We overlaid T1-weighted MR images with gray matter-tissue probability maps to combine anatomical information with tissue class segmentation. Then, we outlined regions of interest (ROIs) that covered potential hypothalamus voxels. Within these ROIs, seed growing technique helped define the hypothalamic volume using gray matter probabilities from the tissue probability maps. This yielded a semi-automated method with short processing times of 20-40 min per hypothalamus. In the MRIs of ten subjects, reliabilities were determined as intraclass correlations (ICC) and volume overlaps in percent. Three raters achieved very good intra-rater reliabilities (ICC 0.82-0.97) and good inter-rater reliabilities (ICC 0.78 and 0.82). Overlaps of intra- and inter-rater runs were very good (≥ 89.7%). We present a fast, semi-automated method for in vivo hypothalamus volumetry in 3 Tesla MRI. Copyright © 2018 Elsevier B.V. All rights reserved.

Efficient random access high resolution region-of-interest (ROI) image retrieval using backward coding of wavelet trees (BCWT)

NASA Astrophysics Data System (ADS)

Corona, Enrique; Nutter, Brian; Mitra, Sunanda; Guo, Jiangling; Karp, Tanja

2008-03-01

Efficient retrieval of high quality Regions-Of-Interest (ROI) from high resolution medical images is essential for reliable interpretation and accurate diagnosis. Random access to high quality ROI from codestreams is becoming an essential feature in many still image compression applications, particularly in viewing diseased areas from large medical images. This feature is easier to implement in block based codecs because of the inherent spatial independency of the code blocks. This independency implies that the decoding order of the blocks is unimportant as long as the position for each is properly identified. In contrast, wavelet-tree based codecs naturally use some interdependency that exploits the decaying spectrum model of the wavelet coefficients. Thus one must keep track of the decoding order from level to level with such codecs. We have developed an innovative multi-rate image subband coding scheme using "Backward Coding of Wavelet Trees (BCWT)" which is fast, memory efficient, and resolution scalable. It offers far less complexity than many other existing codecs including both, wavelet-tree, and block based algorithms. The ROI feature in BCWT is implemented through a transcoder stage that generates a new BCWT codestream containing only the information associated with the user-defined ROI. This paper presents an efficient technique that locates a particular ROI within the BCWT coded domain, and decodes it back to the spatial domain. This technique allows better access and proper identification of pathologies in high resolution images since only a small fraction of the codestream is required to be transmitted and analyzed.

A preliminary study of DTI Fingerprinting on stroke analysis.

PubMed

Ma, Heather T; Ye, Chenfei; Wu, Jun; Yang, Pengfei; Chen, Xuhui; Yang, Zhengyi; Ma, Jingbo

2014-01-01

DTI (Diffusion Tensor Imaging) is a well-known MRI (Magnetic Resonance Imaging) technique which provides useful structural information about human brain. However, the quantitative measurement to physiological variation of subtypes of ischemic stroke is not available. An automatically quantitative method for DTI analysis will enhance the DTI application in clinics. In this study, we proposed a DTI Fingerprinting technology to quantitatively analyze white matter tissue, which was applied in stroke classification. The TBSS (Tract Based Spatial Statistics) method was employed to generate mask automatically. To evaluate the clustering performance of the automatic method, lesion ROI (Region of Interest) is manually drawn on the DWI images as a reference. The results from the DTI Fingerprinting were compared with those obtained from the reference ROIs. It indicates that the DTI Fingerprinting could identify different states of ischemic stroke and has promising potential to provide a more comprehensive measure of the DTI data. Further development should be carried out to improve DTI Fingerprinting technology in clinics.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Besemer, A; Marsh, I; Bednarz, B

Purpose: The calculation of 3D internal dose calculations in targeted radionuclide therapy requires the acquisition and temporal coregistration of a serial PET/CT or SPECT/CT images. This work investigates the dosimetric impact of different temporal coregistration methods commonly used for 3D internal dosimetry. Methods: PET/CT images of four mice were acquired at 1, 24, 48, 72, 96, 144 hrs post-injection of {sup 124}I-CLR1404. The therapeutic {sup 131}I-CLR1404 absorbed dose rate (ADR) was calculated at each time point using a Geant4-based MC dosimetry platform using three temporal image coregistration Methods: (1) no coregistration (NC), whole body sequential CT-CT affine coregistration (WBAC), andmore » individual sequential ROI-ROI affine coregistration (IRAC). For NC, only the ROI mean ADR was integrated to obtain ROI mean doses. For WBAC, the CT at each time point was coregistered to a single reference CT. The CT transformations were applied to the corresponding ADR images and the dose was calculated on a voxel-basis within the whole CT volume. For IRAC, each individual ROI was isolated and sequentially coregistered to a single reference ROI. The ROI transformations were applied to the corresponding ADR images and the dose was calculated on a voxel-basis within the ROI volumes. Results: The percent differences in the ROI mean doses were as large as 109%, 88%, and 32%, comparing the WBAC vs. IRAC, NC vs. IRAC, and NC vs. WBAC methods, respectively. The CoV in the mean dose between the all three methods ranged from 2–36%. The pronounced curvature of the spinal cord was not adequately coregistered using WBAC which resulted in large difference between the WBAC and IRAC. Conclusion: The method used for temporal image coregistration can result in large differences in 3D internal dosimetry calculations. Care must be taken to choose the most appropriate method depending on the imaging conditions, clinical site, and specific application. This work is partially funded by NIH Grant R21 CA198392-01.« less

Relative equilibrium plot improves graphical analysis and allows bias correction of standardized uptake value ratio in quantitative 11C-PiB PET studies.

PubMed

Zhou, Yun; Sojkova, Jitka; Resnick, Susan M; Wong, Dean F

2012-04-01

Both the standardized uptake value ratio (SUVR) and the Logan plot result in biased distribution volume ratios (DVRs) in ligand-receptor dynamic PET studies. The objective of this study was to use a recently developed relative equilibrium-based graphical (RE) plot method to improve and simplify the 2 commonly used methods for quantification of (11)C-Pittsburgh compound B ((11)C-PiB) PET. The overestimation of DVR in SUVR was analyzed theoretically using the Logan and the RE plots. A bias-corrected SUVR (bcSUVR) was derived from the RE plot. Seventy-eight (11)C-PiB dynamic PET scans (66 from controls and 12 from participants with mild cognitive impaired [MCI] from the Baltimore Longitudinal Study of Aging) were acquired over 90 min. Regions of interest (ROIs) were defined on coregistered MR images. Both the ROI and the pixelwise time-activity curves were used to evaluate the estimates of DVR. DVRs obtained using the Logan plot applied to ROI time-activity curves were used as a reference for comparison of DVR estimates. Results from the theoretic analysis were confirmed by human studies. ROI estimates from the RE plot and the bcSUVR were nearly identical to those from the Logan plot with ROI time-activity curves. In contrast, ROI estimates from DVR images in frontal, temporal, parietal, and cingulate regions and the striatum were underestimated by the Logan plot (controls, 4%-12%; MCI, 9%-16%) and overestimated by the SUVR (controls, 8%-16%; MCI, 16%-24%). This bias was higher in the MCI group than in controls (P < 0.01) but was not present when data were analyzed using either the RE plot or the bcSUVR. The RE plot improves pixelwise quantification of (11)C-PiB dynamic PET, compared with the conventional Logan plot. The bcSUVR results in lower bias and higher consistency of DVR estimates than of SUVR. The RE plot and the bcSUVR are practical quantitative approaches that improve the analysis of (11)C-PiB studies.

A Multiatlas Segmentation Using Graph Cuts with Applications to Liver Segmentation in CT Scans

PubMed Central

2014-01-01

An atlas-based segmentation approach is presented that combines low-level operations, an affine probabilistic atlas, and a multiatlas-based segmentation. The proposed combination provides highly accurate segmentation due to registrations and atlas selections based on the regions of interest (ROIs) and coarse segmentations. Our approach shares the following common elements between the probabilistic atlas and multiatlas segmentation: (a) the spatial normalisation and (b) the segmentation method, which is based on minimising a discrete energy function using graph cuts. The method is evaluated for the segmentation of the liver in computed tomography (CT) images. Low-level operations define a ROI around the liver from an abdominal CT. We generate a probabilistic atlas using an affine registration based on geometry moments from manually labelled data. Next, a coarse segmentation of the liver is obtained from the probabilistic atlas with low computational effort. Then, a multiatlas segmentation approach improves the accuracy of the segmentation. Both the atlas selections and the nonrigid registrations of the multiatlas approach use a binary mask defined by coarse segmentation. We experimentally demonstrate that this approach performs better than atlas selections and nonrigid registrations in the entire ROI. The segmentation results are comparable to those obtained by human experts and to other recently published results. PMID:25276219

Fully automatic region of interest selection in glomerular filtration rate estimation from 99mTc-DTPA renogram.

PubMed

Lin, Kun-Ju; Huang, Jia-Yann; Chen, Yung-Sheng

2011-12-01

Glomerular filtration rate (GFR) is a common accepted standard estimation of renal function. Gamma camera-based methods for estimating renal uptake of (99m)Tc-diethylenetriaminepentaacetic acid (DTPA) without blood or urine sampling have been widely used. Of these, the method introduced by Gates has been the most common method. Currently, most of gamma cameras are equipped with a commercial program for GFR determination, a semi-quantitative analysis by manually drawing region of interest (ROI) over each kidney. Then, the GFR value can be computed from the scintigraphic determination of (99m)Tc-DTPA uptake within the kidney automatically. Delineating the kidney area is difficult when applying a fixed threshold value. Moreover, hand-drawn ROIs are tedious, time consuming, and dependent highly on operator skill. Thus, we developed a fully automatic renal ROI estimation system based on the temporal changes in intensity counts, intensity-pair distribution image contrast enhancement method, adaptive thresholding, and morphological operations that can locate the kidney area and obtain the GFR value from a (99m)Tc-DTPA renogram. To evaluate the performance of the proposed approach, 30 clinical dynamic renograms were introduced. The fully automatic approach failed in one patient with very poor renal function. Four patients had a unilateral kidney, and the others had bilateral kidneys. The automatic contours from the remaining 54 kidneys were compared with the contours of manual drawing. The 54 kidneys were included for area error and boundary error analyses. There was high correlation between two physicians' manual contours and the contours obtained by our approach. For area error analysis, the mean true positive area overlap is 91%, the mean false negative is 13.4%, and the mean false positive is 9.3%. The boundary error is 1.6 pixels. The GFR calculated using this automatic computer-aided approach is reproducible and may be applied to help nuclear medicine physicians in clinical practice.

Towards continuous monitoring of pulse rate in neonatal intensive care unit with a webcam.

PubMed

Mestha, Lalit K; Kyal, Survi; Xu, Beilei; Lewis, Leslie Edward; Kumar, Vijay

2014-01-01

We describe a novel method to monitor pulse rate (PR) on a continuous basis of patients in a neonatal intensive care unit (NICU) using videos taken from a high definition (HD) webcam. We describe algorithms that determine PR from videoplethysmographic (VPG) signals extracted from multiple regions of interest (ROI) simultaneously available within the field of view of the camera where cardiac signal is registered. We detect motion from video images and compensate for motion artifacts from each ROI. Preliminary clinical results are presented on 8 neonates each with 30 minutes of uninterrupted video. Comparisons to hospital equipment indicate that the proposed technology can meet medical industry standards and give improved patient comfort and ease of use for practitioners when instrumented with proper hardware.

Introduction of the ASGARD Code

NASA Technical Reports Server (NTRS)

Bethge, Christian; Winebarger, Amy; Tiwari, Sanjiv; Fayock, Brian

2017-01-01

ASGARD stands for 'Automated Selection and Grouping of events in AIA Regional Data'. The code is a refinement of the event detection method in Ugarte-Urra & Warren (2014). It is intended to automatically detect and group brightenings ('events') in the AIA EUV channels, to record event parameters, and to find related events over multiple channels. Ultimately, the goal is to automatically determine heating and cooling timescales in the corona and to significantly increase statistics in this respect. The code is written in IDL and requires the SolarSoft library. It is parallelized and can run with multiple CPUs. Input files are regions of interest (ROIs) in time series of AIA images from the JSOC cutout service (http://jsoc.stanford.edu/ajax/exportdata.html). The ROIs need to be tracked, co-registered, and limited in time (typically 12 hours).

An ROI multi-resolution compression method for 3D-HEVC

NASA Astrophysics Data System (ADS)

Ti, Chunli; Guan, Yudong; Xu, Guodong; Teng, Yidan; Miao, Xinyuan

2017-09-01

3D High Efficiency Video Coding (3D-HEVC) provides a significant potential on increasing the compression ratio of multi-view RGB-D videos. However, the bit rate still rises dramatically with the improvement of the video resolution, which will bring challenges to the transmission network, especially the mobile network. This paper propose an ROI multi-resolution compression method for 3D-HEVC to better preserve the information in ROI on condition of limited bandwidth. This is realized primarily through ROI extraction and compression multi-resolution preprocessed video as alternative data according to the network conditions. At first, the semantic contours are detected by the modified structured forests to restrain the color textures inside objects. The ROI is then determined utilizing the contour neighborhood along with the face region and foreground area of the scene. Secondly, the RGB-D videos are divided into slices and compressed via 3D-HEVC under different resolutions for selection by the audiences and applications. Afterwards, the reconstructed low-resolution videos from 3D-HEVC encoder are directly up-sampled via Laplace transformation and used to replace the non-ROI areas of the high-resolution videos. Finally, the ROI multi-resolution compressed slices are obtained by compressing the ROI preprocessed videos with 3D-HEVC. The temporal and special details of non-ROI are reduced in the low-resolution videos, so the ROI will be better preserved by the encoder automatically. Experiments indicate that the proposed method can keep the key high-frequency information with subjective significance while the bit rate is reduced.

BPF-type region-of-interest reconstruction for parallel translational computed tomography.

PubMed

Wu, Weiwen; Yu, Hengyong; Wang, Shaoyu; Liu, Fenglin

2017-01-01

The objective of this study is to present and test a new ultra-low-cost linear scan based tomography architecture. Similar to linear tomosynthesis, the source and detector are translated in opposite directions and the data acquisition system targets on a region-of-interest (ROI) to acquire data for image reconstruction. This kind of tomographic architecture was named parallel translational computed tomography (PTCT). In previous studies, filtered backprojection (FBP)-type algorithms were developed to reconstruct images from PTCT. However, the reconstructed ROI images from truncated projections have severe truncation artefact. In order to overcome this limitation, we in this study proposed two backprojection filtering (BPF)-type algorithms named MP-BPF and MZ-BPF to reconstruct ROI images from truncated PTCT data. A weight function is constructed to deal with data redundancy for multi-linear translations modes. Extensive numerical simulations are performed to evaluate the proposed MP-BPF and MZ-BPF algorithms for PTCT in fan-beam geometry. Qualitative and quantitative results demonstrate that the proposed BPF-type algorithms cannot only more accurately reconstruct ROI images from truncated projections but also generate high-quality images for the entire image support in some circumstances.

SU-F-T-92: Clinical Benefit for Breast and Chest Wall Setup in Using a Breast Board

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, S; Miyamoto, C; Serratore, D

Purpose: To validate benefit of using a breast board (BB) by analyzing the geometry and dosimetry changes of the regions of interest (ROIs) between CT scans with and without BB. Methods: Seven patients, two chest walls (CW) and five breasts, use BB at CT simulation and no BB at diagnostic CT were included. By using deformable image registration software (Velocity AI), diagnostic CT and planning CT were rigidly co-registered according to the thoracic cage at the target. The heart and the target were then deformedly matched and the contours of the planned ROIs were transferred to the diagnostic CT. Whichmore » were brought back to the planning CT data set though the initial rigid co-registration in order to keep the deformed ROIs redefined in the diagnostic CT. Anatomic shifts and volume changes of a ROI beyond the rigid translation were recorded and dosimetry changes to ROIs were compared with recalculated DVHs. Results: Patient setup without the BB had small but systematic heart shifts superiorly by ∼5 mm. Torso rotations in two cases moved the heart in opposite directions by ∼10 mm. The breast target volume, shape, and locations were significantly changed with arm extension over the head but not in cases with the arm extended laterally. Breast setup without BB could increase the mean dose to the heart and the maximal dose to the anterior ventricle wall by 1.1 and 6.7 Gy, respectively. Conclusion: A method for evaluation of breast setup technique is introduced and applied for patients. Results of systematic heart displacement without using the BB and the potential increase of heart doses encourage us to further investigate the current trend of not using a BB for easy setup and CT scans. Using a BB would likely increase patient sag during prolonged IMRT and real-time patient position monitoring is clinically desired.« less

Scanning linear estimation: improvements over region of interest (ROI) methods

NASA Astrophysics Data System (ADS)

Kupinski, Meredith K.; Clarkson, Eric W.; Barrett, Harrison H.

2013-03-01

In tomographic medical imaging, a signal activity is typically estimated by summing voxels from a reconstructed image. We introduce an alternative estimation scheme that operates on the raw projection data and offers a substantial improvement, as measured by the ensemble mean-square error (EMSE), when compared to using voxel values from a maximum-likelihood expectation-maximization (MLEM) reconstruction. The scanning-linear (SL) estimator operates on the raw projection data and is derived as a special case of maximum-likelihood estimation with a series of approximations to make the calculation tractable. The approximated likelihood accounts for background randomness, measurement noise and variability in the parameters to be estimated. When signal size and location are known, the SL estimate of signal activity is unbiased, i.e. the average estimate equals the true value. By contrast, unpredictable bias arising from the null functions of the imaging system affect standard algorithms that operate on reconstructed data. The SL method is demonstrated for two different tasks: (1) simultaneously estimating a signal’s size, location and activity; (2) for a fixed signal size and location, estimating activity. Noisy projection data are realistically simulated using measured calibration data from the multi-module multi-resolution small-animal SPECT imaging system. For both tasks, the same set of images is reconstructed using the MLEM algorithm (80 iterations), and the average and maximum values within the region of interest (ROI) are calculated for comparison. This comparison shows dramatic improvements in EMSE for the SL estimates. To show that the bias in ROI estimates affects not only absolute values but also relative differences, such as those used to monitor the response to therapy, the activity estimation task is repeated for three different signal sizes.

Optical coherence tomography to evaluate variance in the extent of carious lesions in depth.

PubMed

Park, Kyung-Jin; Schneider, Hartmut; Ziebolz, Dirk; Krause, Felix; Haak, Rainer

2018-05-03

Evaluation of variance in the extent of carious lesions in depth at smooth surfaces within the same ICDAS code group using optical coherence tomography (OCT) in vitro and in vivo. (1) Verification/validation of OCT to assess non-cavitated caries: 13 human molars with ICDAS code 2 at smooth surfaces were imaged using OCT and light microscopy. Regions of interest (ROI) were categorized according to the depth of carious lesions. Agreement between histology and OCT was determined by unweighted Cohen's Kappa and Wilcoxon test. (2) Assessment of 133 smooth surfaces using ICDAS and OCT in vitro, 49 surfaces in vivo. ROI were categorized according to the caries extent (ICDAS: codes 0-4, OCT: scoring based on lesion depth). A frequency distribution of the OCT scores for each ICDAS code was determined. (1) Histology and OCT agreed moderately (κ = 0.54, p ≤ 0.001) with no significant difference between both methods (p = 0.25). The lesions (76.9% (10 of 13)) _were equally scored. (2) In vitro, OCT revealed caries in 42% of ROI clinically assessed as sound. OCT detected dentin-caries in 40% of ROIs visually assessed as enamel-caries. In vivo, large differences between ICDAS and OCT were observed. Carious lesions of ICDAS codes 1 and 2 vary largely in their extent in depth.

Asymmetry of cortical decline in subtypes of primary progressive aphasia.

PubMed

Rogalski, Emily; Cobia, Derin; Martersteck, Adam; Rademaker, Alfred; Wieneke, Christina; Weintraub, Sandra; Mesulam, M-Marsel

2014-09-23

The aim of this study was to provide quantitative measures of changes in cortical atrophy over a 2-year period associated with 3 subtypes of primary progressive aphasia (PPA) using whole-brain vertex-wise and region-of-interest (ROI) neuroimaging methods. The purpose was to quantitate disease progression, establish an empirical basis for clinical expectations, and provide outcome measures for therapeutic trials. Changes in cortical thickness and volume loss as well as neuropsychological performance were assessed at baseline and 2-year follow-up in 26 patients who fulfilled criteria for logopenic (8 patients), agrammatic (10 patients), and semantic (8 patients) PPA subtypes. Whole-brain vertex-wise and ROI imaging analysis were conducted using the FreeSurfer longitudinal pipeline. Clinical deficits and cortical atrophy patterns showed distinct patterns of change among the subtypes over 2 years. Results confirmed that progression for each of the 3 subtypes showed left greater than right hemisphere asymmetry. An ROI analysis also revealed that progression was greater within, rather than outside, the language network. Preferential neurodegeneration of the left hemisphere language network is a common denominator for all 3 PPA subtypes, even as the disease progresses. Using a focal cortical language network ROI as an outcome measure of disease progression appears to be more sensitive than whole-brain or ventricular volume measures of change and may be helpful for designing future clinical trials in PPA. © 2014 American Academy of Neurology.

Methionine Uptake and Required Radiation Dose to Control Glioblastoma

DOE Office of Scientific and Technical Information (OSTI.GOV)

Iuchi, Toshihiko, E-mail: tiuchi@chiba-cc.jp; Hatano, Kazuo; Uchino, Yoshio

Purpose: The purpose of this study was to retrospectively assess the feasibility of radiation therapy planning for glioblastoma multiforme (GBM) based on the use of methionine (MET) positron emission tomography (PET), and the correlation among MET uptake, radiation dose, and tumor control. Methods and Materials: Twenty-two patients with GBM who underwent MET-PET prior to radiation therapy were enrolled. MET uptake in 30 regions of interest (ROIs) from 22 GBMs, biologically effective doses (BEDs) for the ROIs and their ratios (MET uptake:BED) were compared in terms of whether the ROIs were controlled for >12 months. Results: MET uptake was significantly correlated withmore » tumor control (odds ratio [OR], 10.0; P=.005); however, there was a higher level of correlation between MET uptake:BED ratio and tumor control (OR, 40.0; P<.0001). These data indicated that the required BEDs for controlling the ROIs could be predicted in terms of MET uptake; BED could be calculated as [34.0 × MET uptake] Gy from the optimal threshold of the MET uptake:BED ratio for tumor control. Conclusions: Target delineation based on MET-PET was demonstrated to be feasible for radiation therapy treatment planning. MET-PET could not only provide precise visualization of infiltrating tumor cells but also predict the required radiation doses to control target regions.« less

Automated selection of brain regions for real-time fMRI brain-computer interfaces

NASA Astrophysics Data System (ADS)

Lührs, Michael; Sorger, Bettina; Goebel, Rainer; Esposito, Fabrizio

2017-02-01

Objective. Brain-computer interfaces (BCIs) implemented with real-time functional magnetic resonance imaging (rt-fMRI) use fMRI time-courses from predefined regions of interest (ROIs). To reach best performances, localizer experiments and on-site expert supervision are required for ROI definition. To automate this step, we developed two unsupervised computational techniques based on the general linear model (GLM) and independent component analysis (ICA) of rt-fMRI data, and compared their performances on a communication BCI. Approach. 3 T fMRI data of six volunteers were re-analyzed in simulated real-time. During a localizer run, participants performed three mental tasks following visual cues. During two communication runs, a letter-spelling display guided the subjects to freely encode letters by performing one of the mental tasks with a specific timing. GLM- and ICA-based procedures were used to decode each letter, respectively using compact ROIs and whole-brain distributed spatio-temporal patterns of fMRI activity, automatically defined from subject-specific or group-level maps. Main results. Letter-decoding performances were comparable to supervised methods. In combination with a similarity-based criterion, GLM- and ICA-based approaches successfully decoded more than 80% (average) of the letters. Subject-specific maps yielded optimal performances. Significance. Automated solutions for ROI selection may help accelerating the translation of rt-fMRI BCIs from research to clinical applications.

Sustained reduction of nicotine craving with real-time neurofeedback: exploring the role of severity of dependence.

PubMed

Canterberry, Melanie; Hanlon, Colleen A; Hartwell, Karen J; Li, Xingbao; Owens, Max; LeMatty, Todd; Prisciandaro, James J; Borckardt, Jeffrey; Saladin, Michael E; Brady, Kathleen T; George, Mark S

2013-12-01

Neurofeedback delivered via real-time functional magnetic resonance imaging (rtfMRI) is a promising therapeutic technique being explored to facilitate self-regulation of craving in nicotine-dependent cigarette smokers. The current study examined the role of nicotine-dependence severity and the efficacy of multiple visits of neurofeedback from a single region of interest (ROI) in the anterior cingulate cortex (ACC) on craving reduction. Nine nicotine-dependent cigarette smokers participated in three rtfMRI visits that examined cue-induced craving and brain activation. Severity of nicotine dependence was assessed with the Fagerström Test for Nicotine Dependence. When viewing smoking-related images with instructions to "crave," patient-tailored ROIs were generated in the vicinity of the ACC. Activity levels from the ROI were fed back while participants viewed smoking cues with the instruction to reduce craving. Neurofeedback from a single ROI in the ACC led to consistent decreases in self-reported craving and activation in the ACC across the three visits. Dependence severity predicted response to neurofeedback at Visit 3. This study builds upon previous rtfMRI studies on the regulation of nicotine craving in demonstrating that feedback from the ACC can reduce activation to smoking cues across three separate visits. Individuals with lower nicotine-dependence severity were more successful in reducing ACC activation over time. These data highlight the need to consider dependence severity in developing more individualized neurofeedback methods.

Detection of soft tissue densities from digital breast tomosynthesis: comparison of conventional and deep learning approaches

NASA Astrophysics Data System (ADS)

Fotin, Sergei V.; Yin, Yin; Haldankar, Hrishikesh; Hoffmeister, Jeffrey W.; Periaswamy, Senthil

2016-03-01

Computer-aided detection (CAD) has been used in screening mammography for many years and is likely to be utilized for digital breast tomosynthesis (DBT). Higher detection performance is desirable as it may have an impact on radiologist's decisions and clinical outcomes. Recently the algorithms based on deep convolutional architectures have been shown to achieve state of the art performance in object classification and detection. Similarly, we trained a deep convolutional neural network directly on patches sampled from two-dimensional mammography and reconstructed DBT volumes and compared its performance to a conventional CAD algorithm that is based on computation and classification of hand-engineered features. The detection performance was evaluated on the independent test set of 344 DBT reconstructions (GE SenoClaire 3D, iterative reconstruction algorithm) containing 328 suspicious and 115 malignant soft tissue densities including masses and architectural distortions. Detection sensitivity was measured on a region of interest (ROI) basis at the rate of five detection marks per volume. Moving from conventional to deep learning approach resulted in increase of ROI sensitivity from 0:832 +/- 0:040 to 0:893 +/- 0:033 for suspicious ROIs; and from 0:852 +/- 0:065 to 0:930 +/- 0:046 for malignant ROIs. These results indicate the high utility of deep feature learning in the analysis of DBT data and high potential of the method for broader medical image analysis tasks.

Visual analytics of brain networks.

PubMed

Li, Kaiming; Guo, Lei; Faraco, Carlos; Zhu, Dajiang; Chen, Hanbo; Yuan, Yixuan; Lv, Jinglei; Deng, Fan; Jiang, Xi; Zhang, Tuo; Hu, Xintao; Zhang, Degang; Miller, L Stephen; Liu, Tianming

2012-05-15

Identification of regions of interest (ROIs) is a fundamental issue in brain network construction and analysis. Recent studies demonstrate that multimodal neuroimaging approaches and joint analysis strategies are crucial for accurate, reliable and individualized identification of brain ROIs. In this paper, we present a novel approach of visual analytics and its open-source software for ROI definition and brain network construction. By combining neuroscience knowledge and computational intelligence capabilities, visual analytics can generate accurate, reliable and individualized ROIs for brain networks via joint modeling of multimodal neuroimaging data and an intuitive and real-time visual analytics interface. Furthermore, it can be used as a functional ROI optimization and prediction solution when fMRI data is unavailable or inadequate. We have applied this approach to an operation span working memory fMRI/DTI dataset, a schizophrenia DTI/resting state fMRI (R-fMRI) dataset, and a mild cognitive impairment DTI/R-fMRI dataset, in order to demonstrate the effectiveness of visual analytics. Our experimental results are encouraging. Copyright © 2012 Elsevier Inc. All rights reserved.

Topology optimization based design of unilateral NMR for generating a remote homogeneous field.

PubMed

Wang, Qi; Gao, Renjing; Liu, Shutian

2017-06-01

This paper presents a topology optimization based design method for the design of unilateral nuclear magnetic resonance (NMR), with which a remote homogeneous field can be obtained. The topology optimization is actualized by seeking out the optimal layout of ferromagnetic materials within a given design domain. The design objective is defined as generating a sensitive magnetic field with optimal homogeneity and maximal field strength within a required region of interest (ROI). The sensitivity of the objective function with respect to the design variables is derived and the method for solving the optimization problem is presented. A design example is provided to illustrate the utility of the design method, specifically the ability to improve the quality of the magnetic field over the required ROI by determining the optimal structural topology for the ferromagnetic poles. Both in simulations and experiments, the sensitive region of the magnetic field achieves about 2 times larger than that of the reference design, validating validates the feasibility of the design method. Copyright © 2017. Published by Elsevier Inc.

The heritability of the functional connectome is robust to common nonlinear registration methods

NASA Astrophysics Data System (ADS)

Hafzalla, George W.; Prasad, Gautam; Baboyan, Vatche G.; Faskowitz, Joshua; Jahanshad, Neda; McMahon, Katie L.; de Zubicaray, Greig I.; Wright, Margaret J.; Braskie, Meredith N.; Thompson, Paul M.

2016-03-01

Nonlinear registration algorithms are routinely used in brain imaging, to align data for inter-subject and group comparisons, and for voxelwise statistical analyses. To understand how the choice of registration method affects maps of functional brain connectivity in a sample of 611 twins, we evaluated three popular nonlinear registration methods: Advanced Normalization Tools (ANTs), Automatic Registration Toolbox (ART), and FMRIB's Nonlinear Image Registration Tool (FNIRT). Using both structural and functional MRI, we used each of the three methods to align the MNI152 brain template, and 80 regions of interest (ROIs), to each subject's T1-weighted (T1w) anatomical image. We then transformed each subject's ROIs onto the associated resting state functional MRI (rs-fMRI) scans and computed a connectivity network or functional connectome for each subject. Given the different degrees of genetic similarity between pairs of monozygotic (MZ) and same-sex dizygotic (DZ) twins, we used structural equation modeling to estimate the additive genetic influences on the elements of the function networks, or their heritability. The functional connectome and derived statistics were relatively robust to nonlinear registration effects.

Development and testing of texture discriminators for the analysis of trabecular bone in proximal femur radiographs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huber, M. B.; Carballido-Gamio, J.; Fritscher, K.

2009-11-15

Purpose: Texture analysis of femur radiographs may serve as a potential low cost technique to predict osteoporotic fracture risk and has received considerable attention in the past years. A further application of this technique may be the measurement of the quality of specific bone compartments to provide useful information for treatment of bone fractures. Two challenges of texture analysis are the selection of the best suitable texture measure and reproducible placement of regions of interest (ROIs). The goal of this in vitro study was to automatically place ROIs in radiographs of proximal femur specimens and to calculate correlations between variousmore » different texture analysis methods and the femurs' anchorage strength. Methods: Radiographs were obtained from 14 femoral specimens and bone mineral density (BMD) was measured in the femoral neck. Biomechanical testing was performed to assess the anchorage strength in terms of failure load, breakaway torque, and number of cycles. Images were segmented using a framework that is based on the usage of level sets and statistical in-shape models. Five ROIs were automatically placed in the head, upper and lower neck, trochanteric, and shaft compartment in an atlas subject. All other subjects were registered rigidly, affinely, and nonlinearly, and the resulting transformation was used to map the five ROIs onto the individual femora. Results: In each ROI, texture features were extracted using gray level co-occurence matrices (GLCM), third-order GLCM, morphological gradients (MGs), Minkowski dimensions (MDs), Minkowski functionals (MFs), Gaussian Markov random fields, and scaling index method (SIM). Coefficients of determination for each texture feature with parameters of anchorage strength were computed. In a stepwise multiregression analysis, the most predictive parameters were identified in different models. Texture features were highly correlated with anchorage strength estimated by the failure load of up to R{sup 2}=0.61 (MF and MG features, p<0.01) and were partially independent of BMD. The correlations were dependent on the choice of the ROI and the texture measure. The best predictive multiregression model for failure load R{sub adj}{sup 2}=0.86 (p<0.001) included a set of recently developed texture methods (MF and SIM) but excluded bone mineral density and commonly used texture measures. Conclusions: The results suggest that texture information contained in trabecular bone structure visualized on radiographs may predict whether an implant anchorage can be used and may determine the local bone quality from preoperative radiographs.« less

Multi-Stage System for Automatic Target Recognition

NASA Technical Reports Server (NTRS)

Chao, Tien-Hsin; Lu, Thomas T.; Ye, David; Edens, Weston; Johnson, Oliver

2010-01-01

A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feedforward back-propagation neural network (NN) is then trained to classify each feature vector and to remove false positives. The system parameter optimizations process has been developed to adapt to various targets and datasets. The objective was to design an efficient computer vision system that can learn to detect multiple targets in large images with unknown backgrounds. Because the target size is small relative to the image size in this problem, there are many regions of the image that could potentially contain the target. A cursory analysis of every region can be computationally efficient, but may yield too many false positives. On the other hand, a detailed analysis of every region can yield better results, but may be computationally inefficient. The multi-stage ATR system was designed to achieve an optimal balance between accuracy and computational efficiency by incorporating both models. The detection stage first identifies potential ROIs where the target may be present by performing a fast Fourier domain OT-MACH filter-based correlation. Because threshold for this stage is chosen with the goal of detecting all true positives, a number of false positives are also detected as ROIs. The verification stage then transforms the regions of interest into feature space, and eliminates false positives using an artificial neural network classifier. The multi-stage system allows tuning the detection sensitivity and the identification specificity individually in each stage. It is easier to achieve optimized ATR operation based on its specific goal. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar and video image datasets.

Database of normal human cerebral blood flow measured by SPECT: II. Quantification of I-123-IMP studies with ARG method and effects of partial volume correction.

PubMed

Inoue, Kentaro; Ito, Hiroshi; Shidahara, Miho; Goto, Ryoi; Kinomura, Shigeo; Sato, Kazunori; Taki, Yasuyuki; Okada, Ken; Kaneta, Tomohiro; Fukuda, Hiroshi

2006-02-01

The limited spatial resolution of SPECT causes a partial volume effect (PVE) and can lead to the significant underestimation of regional tracer concentration in the small structures surrounded by a low tracer concentration, such as the cortical gray matter of an atrophied brain. The aim of the present study was to determine, using 123I-IMP and SPECT, normal CBF of elderly subjects with and without PVE correction (PVC), and to determine regional differences in the effect of PVC and their association with the regional tissue fraction of the brain. Quantitative CBF SPECT using 123I-IMP was performed in 33 healthy elderly subjects (18 males, 15 females, 54-74 years old) using the autoradiographic method. We corrected CBF for PVE using segmented MR images, and analyzed quantitative CBF and regional differences in the effect of PVC using tissue fractions of gray matter (GM) and white matter (WM) in regions of interest (ROIs) placed on the cortical and subcortical GM regions and deep WM regions. The mean CBF in GM-ROIs were 31.7 +/- 6.6 and 41.0 +/- 8.1 ml/100 g/min for males and females, and in WM-ROIs, 18.2 +/- 0.7 and 22.9 +/- 0.8 ml/100 g/min for males and females, respectively. The mean CBF in GM-ROIs after PVC were 50.9 +/- 12.8 and 65.8 +/- 16.1 ml/100 g/min for males and females, respectively. There were statistically significant differences in the effect of PVC among ROIs, but not between genders. The effect of PVC was small in the cerebellum and parahippocampal gyrus, and it was large in the superior frontal gyrus, superior parietal lobule and precentral gyrus. Quantitative CBF in GM recovered significantly, but did not reach values as high as those obtained by invasive methods or in the H2(15)O PET study that used PVC. There were significant regional differences in the effect of PVC, which were considered to result from regional differences in GM tissue fraction, which is more reduced in the frontoparietal regions in the atrophied brain of the elderly.

Assessing age-related gray matter decline with voxel-based morphometry depends significantly on segmentation and normalization procedures

PubMed Central

Callaert, Dorothée V.; Ribbens, Annemie; Maes, Frederik; Swinnen, Stephan P.; Wenderoth, Nicole

2014-01-01

Healthy ageing coincides with a progressive decline of brain gray matter (GM) ultimately affecting the entire brain. For a long time, manual delineation-based volumetry within predefined regions of interest (ROI) has been the gold standard for assessing such degeneration. Voxel-Based Morphometry (VBM) offers an automated alternative approach that, however, relies critically on the segmentation and spatial normalization of a large collection of images from different subjects. This can be achieved via different algorithms, with SPM5/SPM8, DARTEL of SPM8 and FSL tools (FAST, FNIRT) being three of the most frequently used. We complemented these voxel based measurements with a ROI based approach, whereby the ROIs are defined by transforms of an atlas (containing different tissue probability maps as well as predefined anatomic labels) to the individual subject images in order to obtain volumetric information at the level of the whole brain or within separate ROIs. Comparing GM decline between 21 young subjects (mean age 23) and 18 elderly (mean age 66) revealed that volumetric measurements differed significantly between methods. The unified segmentation/normalization of SPM5/SPM8 revealed the largest age-related differences and DARTEL the smallest, with FSL being more similar to the DARTEL approach. Method specific differences were substantial after segmentation and most pronounced for the cortical structures in close vicinity to major sulci and fissures. Our findings suggest that algorithms that provide only limited degrees of freedom for local deformations (such as the unified segmentation and normalization of SPM5/SPM8) tend to overestimate between-group differences in VBM results when compared to methods providing more flexible warping. This difference seems to be most pronounced if the anatomy of one of the groups deviates from custom templates, a finding that is of particular importance when results are compared across studies using different VBM methods. PMID:25002845

Analysis of breast thermograms for ROI extraction and description using mathematical morphology

NASA Astrophysics Data System (ADS)

Zermeño-Loreto, O. A.; Toxqui-Quitl, C.; Orozco Guillén, E. E.; Padilla-Vivanco, A.

2017-09-01

The detection of a temperature increase or hot spots in breast thermograms can be related with high metabolic activity of disease cells. Image processing algorithms to seek mainly temperature increases above 3°C which have a high probability of being a malignancy are proposed. Also a derivative operator is used to highlights breast regions of interest (ROI). In order to determinate a medical alert, a feature descriptor of the ROI is constructed using its maximum temperature, maximum increase of temperature, sector/quadrant position in the breast, and area. The proposed algorithms are tested in a home database and a public database for mastology research.

Comparison of manual and automatic techniques for substriatal segmentation in 11C-raclopride high-resolution PET studies.

PubMed

Johansson, Jarkko; Alakurtti, Kati; Joutsa, Juho; Tohka, Jussi; Ruotsalainen, Ulla; Rinne, Juha O

2016-10-01

The striatum is the primary target in regional C-raclopride-PET studies, and despite its small volume, it contains several functional and anatomical subregions. The outcome of the quantitative dopamine receptor study using C-raclopride-PET depends heavily on the quality of the region-of-interest (ROI) definition of these subregions. The aim of this study was to evaluate subregional analysis techniques because new approaches have emerged, but have not yet been compared directly. In this paper, we compared manual ROI delineation with several automatic methods. The automatic methods used either direct clustering of the PET image or individualization of chosen brain atlases on the basis of MRI or PET image normalization. State-of-the-art normalization methods and atlases were applied, including those provided in the FreeSurfer, Statistical Parametric Mapping8, and FSL software packages. Evaluation of the automatic methods was based on voxel-wise congruity with the manual delineations and the test-retest variability and reliability of the outcome measures using data from seven healthy male participants who were scanned twice with C-raclopride-PET on the same day. The results show that both manual and automatic methods can be used to define striatal subregions. Although most of the methods performed well with respect to the test-retest variability and reliability of binding potential, the smallest average test-retest variability and SEM were obtained using a connectivity-based atlas and PET normalization (test-retest variability=4.5%, SEM=0.17). The current state-of-the-art automatic ROI methods can be considered good alternatives for subjective and laborious manual segmentation in C-raclopride-PET studies.

WE-AB-BRA-08: Results of a Multi-Institutional Study for the Evaluation of Deformable Image Registration Algorithms for Structure Delineation Via Computational Phantoms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Loi, G; Fusella, M; Fiandra, C

2015-06-15

Purpose: To investigate the accuracy of various algorithms for deformable image registration (DIR), to propagate regions of interest (ROIs) in computational phantoms based on patient images using different commercial systems. This work is part of an Italian multi-institutional study to test on common datasets the accuracy, reproducibility and safety of DIR applications in Adaptive Radiotherapy. Methods: Eleven institutions with three available commercial solutions provided data to assess the agreement of DIR-propagated ROIs with automatically drown ROIs considered as ground-truth for the comparison. The DIR algorithms were tested on real patient data from three different anatomical districts: head and neck, thoraxmore » and pelvis. For every dataset two specific Deformation Vector Fields (DVFs) provided by ImSimQA software were applied to the reference data set. Three different commercial software were used in this study: RayStation, Velocity and Mirada. The DIR-mapped ROIs were then compared with the reference ROIs using the Jaccard Conformity Index (JCI). Results: More than 600 DIR-mapped ROIs were analyzed. Putting together all JCI data of all institutions for the first DVF, the mean JCI was 0.87 ± 0.7 (1 SD) while for the second DVF JCI was 0.8 ± 0.13 (1 SD). Several considerations on different structures are available from collected data: the standard deviation among different institutions on specific structure raise as the larger is the applied DVF. The higher value is 10% for bladder. Conclusion: Although the complexity of deformation of human body is very difficult to model, this work illustrates some clinical scenarios with well-known DVFs provided by specific software. CI parameter gives the inter-user variability and may put in evidence the need of improving the working protocol in order to reduce the inter-institution JCI variability.« less

Bladder cancer treatment response assessment using deep learning in CT with transfer learning

NASA Astrophysics Data System (ADS)

Cha, Kenny H.; Hadjiiski, Lubomir M.; Chan, Heang-Ping; Samala, Ravi K.; Cohan, Richard H.; Caoili, Elaine M.; Paramagul, Chintana; Alva, Ajjai; Weizer, Alon Z.

2017-03-01

We are developing a CAD system for bladder cancer treatment response assessment in CT. We compared the performance of the deep-learning convolution neural network (DL-CNN) using different network sizes, and with and without transfer learning using natural scene images or regions of interest (ROIs) inside and outside the bladder. The DL-CNN was trained to identify responders (T0 disease) and non-responders to chemotherapy. ROIs were extracted from segmented lesions in pre- and post-treatment scans of a patient and paired to generate hybrid pre-post-treatment paired ROIs. The 87 lesions from 82 patients generated 104 temporal lesion pairs and 6,700 pre-post-treatment paired ROIs. Two-fold cross-validation and receiver operating characteristic analysis were performed and the area under the curve (AUC) was calculated for the DL-CNN estimates. The AUCs for prediction of T0 disease after treatment were 0.77+/-0.08 and 0.75+/-0.08, respectively, for the two partitions using DL-CNN without transfer learning and a small network, and were 0.74+/-0.07 and 0.74+/-0.08 with a large network. The AUCs were 0.73+/-0.08 and 0.62+/-0.08 with transfer learning using a small network pre-trained with bladder ROIs. The AUC values were 0.77+/-0.08 and 0.73+/-0.07 using the large network pre-trained with the same bladder ROIs. With transfer learning using the large network pretrained with the Canadian Institute for Advanced Research (CIFAR-10) data set, the AUCs were 0.72+/-0.06 and 0.64+/-0.09, respectively, for the two partitions. None of the differences in the methods reached statistical significance. Our study demonstrated the feasibility of using DL-CNN for the estimation of treatment response in CT. Transfer learning did not improve the treatment response estimation. The DL-CNN performed better when transfer learning with bladder images was used instead of natural scene images.

SU-E-T-497: Semi-Automated in Vivo Radiochromic Film Dosimetry Using a Novel Image Processing Algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Reyhan, M; Yue, N

Purpose: To validate an automated image processing algorithm designed to detect the center of radiochromic film used for in vivo film dosimetry against the current gold standard of manual selection. Methods: An image processing algorithm was developed to automatically select the region of interest (ROI) in *.tiff images that contain multiple pieces of radiochromic film (0.5x1.3cm{sup 2}). After a user has linked a calibration file to the processing algorithm and selected a *.tiff file for processing, an ROI is automatically detected for all films by a combination of thresholding and erosion, which removes edges and any additional markings for orientation.more » Calibration is applied to the mean pixel values from the ROIs and a *.tiff image is output displaying the original image with an overlay of the ROIs and the measured doses. Validation of the algorithm was determined by comparing in vivo dose determined using the current gold standard (manually drawn ROIs) versus automated ROIs for n=420 scanned films. Bland-Altman analysis, paired t-test, and linear regression were performed to demonstrate agreement between the processes. Results: The measured doses ranged from 0.2-886.6cGy. Bland-Altman analysis of the two techniques (automatic minus manual) revealed a bias of -0.28cGy and a 95% confidence interval of (5.5cGy,-6.1cGy). These values demonstrate excellent agreement between the two techniques. Paired t-test results showed no statistical differences between the two techniques, p=0.98. Linear regression with a forced zero intercept demonstrated that Automatic=0.997*Manual, with a Pearson correlation coefficient of 0.999. The minimal differences between the two techniques may be explained by the fact that the hand drawn ROIs were not identical to the automatically selected ones. The average processing time was 6.7seconds in Matlab on an IntelCore2Duo processor. Conclusion: An automated image processing algorithm has been developed and validated, which will help minimize user interaction and processing time of radiochromic film used for in vivo dosimetry.« less

Quantitative Lesion-to-Fat Elasticity Ratio Measured by Shear-Wave Elastography for Breast Mass: Which Area Should Be Selected as the Fat Reference?

PubMed Central

Youk, Ji Hyun; Son, Eun Ju; Gweon, Hye Mi; Han, Kyung Hwa; Kim, Jeong-Ah

2015-01-01

Objectives To investigate whether the diagnostic performance of lesion-to-fat elasticity ratio (Eratio) was affected by the location of the reference fat. Methods For 257 breast masses in 250 women who underwent shear-wave elastography before biopsy or surgery, multiple Eratios were measured with a fixed region-of-interest (ROI) in the mass along with multiple ROIs over the surrounding fat in different locations. Logistic regression analysis was used to determine that Eratio was independently associated with malignancy adjusted for the location of fat ROI (depth, laterality, and distance from lesion or skin). Mean (Emean) and maximum (Emax) elasticity values of fat were divided into four groups according to their interquartile ranges. Diagnostic performance of each group was evaluated using the area under the ROC curve (AUC). False diagnoses of Eratio were reviewed for ROIs on areas showing artifactual high or low stiffness and analyzed by logistic regression analysis to determine variables (associated palpable abnormality, lesion size, the vertical distance from fat ROI to skin, and elasticity values of lesion or fat) independently associated with false results. Results Eratio was independently associated with malignancy adjusted for the location of fat ROI (P<0.0001). Among four groups of fat elasticity values, the AUC showed no significant difference (<25th percentile, 25th percentile~median, median~75th percentile, and ≥75th percentile; 0.973, 0.982, 0.967, and 0.954 for Emean; 0.977, 0.967, 0.966, and 0.957 for Emax). Fat elasticity values were independently associated with false results of Eratio with the cut-off of 3.18 from ROC curve (P<0.0001). ROIs were set on fat showing artifactual high stiffness in 90% of 10 false negatives and on lesion showing vertical striped artifact or fat showing artifactual low stiffness in 77.5% of 71 false positives. Conclusion Eratio shows good diagnostic performance regardless of the location of reference fat, except when it is placed in areas of artifacts. PMID:26368920

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moore, B; Yin, F; Cai, J

Purpose: To determine the variation in tumor contrast between different MRI sequences and between patients for the purpose of MRI-based treatment planning. Methods: Multiple MRI scans of 11 patients with cancer(s) in the liver were included in this IRB-approved study. Imaging sequences consisted of T1W MRI, Contrast-Enhanced T1W MRI, T2W MRI, and T2*/T1W MRI. MRI images were acquired on a 1.5T GE Signa scanner with a four-channel torso coil. We calculated the tumor-to-tissue contrast to noise ratio (CNR) for each MR sequence by contouring the tumor and a region of interest (ROI) in a homogeneous region of the liver usingmore » the Eclipse treatment planning software. CNR was calculated (I-Tum-I-ROI)/SD-ROI, where I-Tum and I-ROI are the mean values of the tumor and the ROI respectively, and SD-ROI is the standard deviation of the ROI. The same tumor and ROI structures were used in all measurements for different MR sequences. Inter-patient Coefficient of variation (CV), and inter-sequence CV was determined. In addition, mean and standard deviation of CNR were calculated and compared between different MR sequences. Results: Our preliminary results showed large inter-patient CV (range: 37.7% to 88%) and inter-sequence CV (range 5.3% to 104.9%) of liver tumor CNR, indicating great variations in tumor CNR between MR sequences and between patients. Tumor CNR was found to be largest in CE-T1W (8.5±7.5), followed by T2W (4.2±2.4), T1W (3.4±2.2), and T2*/T1W (1.7±0.6) MR scans. The inter-patient CV of tumor CNR was also the largest in CE-T1W (88%), followed by T1W (64.3%), T1W (56.2%), and T2*/T1W (37.7) MR scans. Conclusion: Large inter-sequence and inter-patient variations were observed in liver tumor CNR. CE-T1W MR images on average provided the best tumor CNR. Efforts are needed to optimize tumor contrast and its consistency for MRI-based treatment planning of cancer in the liver. This project is supported by NIH grant: 1R21CA165384.« less

Gender differences in functional connectivities between insular subdivisions and selective pain-related brain structures.

PubMed

Dai, Yu-Jie; Zhang, Xin; Yang, Yang; Nan, Hai-Yan; Yu, Ying; Sun, Qian; Yan, Lin-Feng; Hu, Bo; Zhang, Jin; Qiu, Zi-Yu; Gao, Yi; Cui, Guang-Bin; Chen, Bi-Liang; Wang, Wen

2018-03-14

The incidence of pain disorders in women is higher than in men, making gender differences in pain a research focus. The human insular cortex is an important brain hub structure for pain processing and is divided into several subdivisions, serving different functions in pain perception. Here we aimed to examine the gender differences of the functional connectivities (FCs) between the twelve insular subdivisions and selected pain-related brain structures in healthy adults. Twenty-six healthy males and 11 age-matched healthy females were recruited in this cross-sectional study. FCs between the 12 insular subdivisions (as 12 regions of interest (ROIs)) and the whole brain (ROI-whole brain level) or 64 selected pain-related brain regions (64 ROIs, ROI-ROI level) were measured between the males and females. Significant gender differences in the FCs of the insular subdivisions were revealed: (1) The FCs between the dorsal dysgranular insula (dId) and other brain regions were significantly increased in males using two different techniques (ROI-whole brain and ROI-ROI analyses); (2) Based on the ROI-whole brain analysis, the FC increases in 4 FC-pairs were observed in males, including the left dId - the right median cingulate and paracingulate/ right posterior cingulate gyrus/ right precuneus, the left dId - the right median cingulate and paracingulate, the left dId - the left angular as well as the left dId - the left middle frontal gyrus; (3) According to the ROI-ROI analysis, increased FC between the left dId and the right rostral anterior cingulate cortex was investigated in males. In summary, the gender differences in the FCs of the insular subdivisions with pain-related brain regions were revealed in the current study, offering neuroimaging evidence for gender differences in pain processing. ClinicalTrials.gov, NCT02820974 . Registered 28 June 2016.

An artifacts removal post-processing for epiphyseal region-of-interest (EROI) localization in automated bone age assessment (BAA)

PubMed Central

2011-01-01

Background Segmentation is the most crucial part in the computer-aided bone age assessment. A well-known type of segmentation performed in the system is adaptive segmentation. While providing better result than global thresholding method, the adaptive segmentation produces a lot of unwanted noise that could affect the latter process of epiphysis extraction. Methods A proposed method with anisotropic diffusion as pre-processing and a novel Bounded Area Elimination (BAE) post-processing algorithm to improve the algorithm of ossification site localization technique are designed with the intent of improving the adaptive segmentation result and the region-of interest (ROI) localization accuracy. Results The results are then evaluated by quantitative analysis and qualitative analysis using texture feature evaluation. The result indicates that the image homogeneity after anisotropic diffusion has improved averagely on each age group for 17.59%. Results of experiments showed that the smoothness has been improved averagely 35% after BAE algorithm and the improvement of ROI localization has improved for averagely 8.19%. The MSSIM has improved averagely 10.49% after performing the BAE algorithm on the adaptive segmented hand radiograph. Conclusions The result indicated that hand radiographs which have undergone anisotropic diffusion have greatly reduced the noise in the segmented image and the result as well indicated that the BAE algorithm proposed is capable of removing the artifacts generated in adaptive segmentation. PMID:21952080

Optimization of the reconstruction parameters in [123I]FP-CIT SPECT

NASA Astrophysics Data System (ADS)

Niñerola-Baizán, Aida; Gallego, Judith; Cot, Albert; Aguiar, Pablo; Lomeña, Francisco; Pavía, Javier; Ros, Domènec

2018-04-01

The aim of this work was to obtain a set of parameters to be applied in [123I]FP-CIT SPECT reconstruction in order to minimize the error between standardized and true values of the specific uptake ratio (SUR) in dopaminergic neurotransmission SPECT studies. To this end, Monte Carlo simulation was used to generate a database of 1380 projection data-sets from 23 subjects, including normal cases and a variety of pathologies. Studies were reconstructed using filtered back projection (FBP) with attenuation correction and ordered subset expectation maximization (OSEM) with correction for different degradations (attenuation, scatter and PSF). Reconstruction parameters to be optimized were the cut-off frequency of a 2D Butterworth pre-filter in FBP, and the number of iterations and the full width at Half maximum of a 3D Gaussian post-filter in OSEM. Reconstructed images were quantified using regions of interest (ROIs) derived from Magnetic Resonance scans and from the Automated Anatomical Labeling map. Results were standardized by applying a simple linear regression line obtained from the entire patient dataset. Our findings show that we can obtain a set of optimal parameters for each reconstruction strategy. The accuracy of the standardized SUR increases when the reconstruction method includes more corrections. The use of generic ROIs instead of subject-specific ROIs adds significant inaccuracies. Thus, after reconstruction with OSEM and correction for all degradations, subject-specific ROIs led to errors between standardized and true SUR values in the range [‑0.5, +0.5] in 87% and 92% of the cases for caudate and putamen, respectively. These percentages dropped to 75% and 88% when the generic ROIs were used.

A New Approach to Develop Computer-aided Diagnosis Scheme of Breast Mass Classification Using Deep Learning Technology

PubMed Central

Qiu, Yuchen; Yan, Shiju; Gundreddy, Rohith Reddy; Wang, Yunzhi; Cheng, Samuel; Liu, Hong; Zheng, Bin

2017-01-01

PURPOSE To develop and test a deep learning based computer-aided diagnosis (CAD) scheme of mammograms for classifying between malignant and benign masses. METHODS An image dataset involving 560 regions of interest (ROIs) extracted from digital mammograms was used. After down-sampling each ROI from 512×512 to 64×64 pixel size, we applied an 8 layer deep learning network that involves 3 pairs of convolution-max-pooling layers for automatic feature extraction and a multiple layer perceptron (MLP) classifier for feature categorization to process ROIs. The 3 pairs of convolution layers contain 20, 10, and 5 feature maps, respectively. Each convolution layer is connected with a max-pooling layer to improve the feature robustness. The output of the sixth layer is fully connected with a MLP classifier, which is composed of one hidden layer and one logistic regression layer. The network then generates a classification score to predict the likelihood of ROI depicting a malignant mass. A four-fold cross validation method was applied to train and test this deep learning network. RESULTS The results revealed that this CAD scheme yields an area under the receiver operation characteristic curve (AUC) of 0.696±0.044, 0.802±0.037, 0.836±0.036, and 0.822±0.035 for fold 1 to 4 testing datasets, respectively. The overall AUC of the entire dataset is 0.790±0.019. CONCLUSIONS This study demonstrates the feasibility of applying a deep learning based CAD scheme to classify between malignant and benign breast masses without a lesion segmentation, image feature computation and selection process. PMID:28436410

Defect detection in slab surface: a novel dual Charge-coupled Device imaging-based fuzzy connectedness strategy.

PubMed

Zhao, Liming; Ouyang, Qi; Chen, Dengfu; Udupa, Jayaram K; Wang, Huiqian; Zeng, Yuebin

2014-11-01

To provide an accurate surface defects inspection system and make the automation of robust image segmentation method a reality in routine production line, a general approach is presented for continuous casting slab (CC-slab) surface defects extraction and delineation. The applicability of the system is not tied to CC-slab exclusively. We combined the line array CCD (Charge-coupled Device) traditional scanning imaging (LS-imaging) and area array CCD laser three-dimensional (3D) scanning imaging (AL-imaging) strategies in designing the system. Its aim is to suppress the respective imaging system's limitations. In the system, the images acquired from the two CCD sensors are carefully aligned in space and in time by maximum mutual information-based full-fledged registration schema. Subsequently, the image information is fused from these two subsystems such as the unbroken 2D information in LS-imaging and 3D depressed information in AL-imaging. Finally, on the basis of the established dual scanning imaging system the region of interest (ROI) localization by seed specification was designed, and the delineation for ROI by iterative relative fuzzy connectedness (IRFC) algorithm was utilized to get a precise inspection result. Our method takes into account the complementary advantages in the two common machine vision (MV) systems and it performs competitively with the state-of-the-art as seen from the comparison of experimental results. For the first time, a joint imaging scanning strategy is proposed for CC-slab surface defect inspection that allows a feasible way of powerful ROI delineation strategies to be applied to the MV inspection field. Multi-ROI delineation by using IRFC in this research field may further improve the results.

Improving the Nulling Beamformer Using Subspace Suppression.

PubMed

Rana, Kunjan D; Hämäläinen, Matti S; Vaina, Lucia M

2018-01-01

Magnetoencephalography (MEG) captures the magnetic fields generated by neuronal current sources with sensors outside the head. In MEG analysis these current sources are estimated from the measured data to identify the locations and time courses of neural activity. Since there is no unique solution to this so-called inverse problem, multiple source estimation techniques have been developed. The nulling beamformer (NB), a modified form of the linearly constrained minimum variance (LCMV) beamformer, is specifically used in the process of inferring interregional interactions and is designed to eliminate shared signal contributions, or cross-talk, between regions of interest (ROIs) that would otherwise interfere with the connectivity analyses. The nulling beamformer applies the truncated singular value decomposition (TSVD) to remove small signal contributions from a ROI to the sensor signals. However, ROIs with strong crosstalk will have high separating power in the weaker components, which may be removed by the TSVD operation. To address this issue we propose a new method, the nulling beamformer with subspace suppression (NBSS). This method, controlled by a tuning parameter, reweights the singular values of the gain matrix mapping from source to sensor space such that components with high overlap are reduced. By doing so, we are able to measure signals between nearby source locations with limited cross-talk interference, allowing for reliable cortical connectivity analysis between them. In two simulations, we demonstrated that NBSS reduces cross-talk while retaining ROIs' signal power, and has higher separating power than both the minimum norm estimate (MNE) and the nulling beamformer without subspace suppression. We also showed that NBSS successfully localized the auditory M100 event-related field in primary auditory cortex, measured from a subject undergoing an auditory localizer task, and suppressed cross-talk in a nearby region in the superior temporal sulcus.

Assessing vertebral fracture risk on volumetric quantitative computed tomography by geometric characterization of trabecular bone structure

NASA Astrophysics Data System (ADS)

Checefsky, Walter A.; Abidin, Anas Z.; Nagarajan, Mahesh B.; Bauer, Jan S.; Baum, Thomas; Wismüller, Axel

2016-03-01

The current clinical standard for measuring Bone Mineral Density (BMD) is dual X-ray absorptiometry, however more recently BMD derived from volumetric quantitative computed tomography has been shown to demonstrate a high association with spinal fracture susceptibility. In this study, we propose a method of fracture risk assessment using structural properties of trabecular bone in spinal vertebrae. Experimental data was acquired via axial multi-detector CT (MDCT) from 12 spinal vertebrae specimens using a whole-body 256-row CT scanner with a dedicated calibration phantom. Common image processing methods were used to annotate the trabecular compartment in the vertebral slices creating a circular region of interest (ROI) that excluded cortical bone for each slice. The pixels inside the ROI were converted to values indicative of BMD. High dimensional geometrical features were derived using the scaling index method (SIM) at different radii and scaling factors (SF). The mean BMD values within the ROI were then extracted and used in conjunction with a support vector machine to predict the failure load of the specimens. Prediction performance was measured using the root-mean-square error (RMSE) metric and determined that SIM combined with mean BMD features (RMSE = 0.82 +/- 0.37) outperformed MDCT-measured mean BMD (RMSE = 1.11 +/- 0.33) (p < 10-4). These results demonstrate that biomechanical strength prediction in vertebrae can be significantly improved through the use of SIM-derived texture features from trabecular bone.

The Relative Value of Skills, Knowledge, and Teaching Methods in Explaining Master of Business Administration (MBA) Program Return on Investment

ERIC Educational Resources Information Center

van Auken, Stuart; Wells, Ludmilla Gricenko; Chrysler, Earl

2005-01-01

In this article, the authors provide insight into alumni perceptions of Master of Business Administration (MBA) program return on investment (ROI). They sought to assess the relative value of skills, knowledge, and teaching methods in explaining ROI. By developing insight into the drivers of ROI, the real utility of MBA program ingredients can be…

A hands-free region-of-interest selection interface for solo surgery with a wide-angle endoscope: preclinical proof of concept.

PubMed

Jung, Kyunghwa; Choi, Hyunseok; Hong, Hanpyo; Adikrishna, Arnold; Jeon, In-Ho; Hong, Jaesung

2017-02-01

A hands-free region-of-interest (ROI) selection interface is proposed for solo surgery using a wide-angle endoscope. A wide-angle endoscope provides images with a larger field of view than a conventional endoscope. With an appropriate selection interface for a ROI, surgeons can also obtain a detailed local view as if they moved a conventional endoscope in a specific position and direction. To manipulate the endoscope without releasing the surgical instrument in hand, a mini-camera is attached to the instrument, and the images taken by the attached camera are analyzed. When a surgeon moves the instrument, the instrument orientation is calculated by an image processing. Surgeons can select the ROI with this instrument movement after switching from 'task mode' to 'selection mode.' The accelerated KAZE algorithm is used to track the features of the camera images once the instrument is moved. Both the wide-angle and detailed local views are displayed simultaneously, and a surgeon can move the local view area by moving the mini-camera attached to the surgical instrument. Local view selection for a solo surgery was performed without releasing the instrument. The accuracy of camera pose estimation was not significantly different between camera resolutions, but it was significantly different between background camera images with different numbers of features (P < 0.01). The success rate of ROI selection diminished as the number of separated regions increased. However, separated regions up to 12 with a region size of 160 × 160 pixels were selected with no failure. Surgical tasks on a phantom model and a cadaver were attempted to verify the feasibility in a clinical environment. Hands-free endoscope manipulation without releasing the instruments in hand was achieved. The proposed method requires only a small, low-cost camera and an image processing. The technique enables surgeons to perform solo surgeries without a camera assistant.

Network modelling methods for FMRI.

PubMed

Smith, Stephen M; Miller, Karla L; Salimi-Khorshidi, Gholamreza; Webster, Matthew; Beckmann, Christian F; Nichols, Thomas E; Ramsey, Joseph D; Woolrich, Mark W

2011-01-15

There is great interest in estimating brain "networks" from FMRI data. This is often attempted by identifying a set of functional "nodes" (e.g., spatial ROIs or ICA maps) and then conducting a connectivity analysis between the nodes, based on the FMRI timeseries associated with the nodes. Analysis methods range from very simple measures that consider just two nodes at a time (e.g., correlation between two nodes' timeseries) to sophisticated approaches that consider all nodes simultaneously and estimate one global network model (e.g., Bayes net models). Many different methods are being used in the literature, but almost none has been carefully validated or compared for use on FMRI timeseries data. In this work we generate rich, realistic simulated FMRI data for a wide range of underlying networks, experimental protocols and problematic confounds in the data, in order to compare different connectivity estimation approaches. Our results show that in general correlation-based approaches can be quite successful, methods based on higher-order statistics are less sensitive, and lag-based approaches perform very poorly. More specifically: there are several methods that can give high sensitivity to network connection detection on good quality FMRI data, in particular, partial correlation, regularised inverse covariance estimation and several Bayes net methods; however, accurate estimation of connection directionality is more difficult to achieve, though Patel's τ can be reasonably successful. With respect to the various confounds added to the data, the most striking result was that the use of functionally inaccurate ROIs (when defining the network nodes and extracting their associated timeseries) is extremely damaging to network estimation; hence, results derived from inappropriate ROI definition (such as via structural atlases) should be regarded with great caution. Copyright © 2010 Elsevier Inc. All rights reserved.

A vessel length-based method to compute coronary fractional flow reserve from optical coherence tomography images.

PubMed

Lee, Kyung Eun; Lee, Seo Ho; Shin, Eun-Seok; Shim, Eun Bo

2017-06-26

Hemodynamic simulation for quantifying fractional flow reserve (FFR) is often performed in a patient-specific geometry of coronary arteries reconstructed from the images from various imaging modalities. Because optical coherence tomography (OCT) images can provide more precise vascular lumen geometry, regardless of stenotic severity, hemodynamic simulation based on OCT images may be effective. The aim of this study is to perform OCT-FFR simulations by coupling a 3D CFD model from geometrically correct OCT images with a LPM based on vessel lengths extracted from CAG data with clinical validations for the present method. To simulate coronary hemodynamics, we developed a fast and accurate method that combined a computational fluid dynamics (CFD) model of an OCT-based region of interest (ROI) with a lumped parameter model (LPM) of the coronary microvasculature and veins. Here, the LPM was based on vessel lengths extracted from coronary X-ray angiography (CAG) images. Based on a vessel length-based approach, we describe a theoretical formulation for the total resistance of the LPM from a three-dimensional (3D) CFD model of the ROI. To show the utility of this method, we present calculated examples of FFR from OCT images. To validate the OCT-based FFR calculation (OCT-FFR) clinically, we compared the computed OCT-FFR values for 17 vessels of 13 patients with clinically measured FFR (M-FFR) values. A novel formulation for the total resistance of LPM is introduced to accurately simulate a 3D CFD model of the ROI. The simulated FFR values compared well with clinically measured ones, showing the accuracy of the method. Moreover, the present method is fast in terms of computational time, enabling clinicians to provide solutions handled within the hospital.

Monitoring of bone regeneration process by means of texture analysis

NASA Astrophysics Data System (ADS)

Kokkinou, E.; Boniatis, I.; Costaridou, L.; Saridis, A.; Panagiotopoulos, E.; Panayiotakis, G.

2009-09-01

An image analysis method is proposed for the monitoring of the regeneration of the tibial bone. For this purpose, 130 digitized radiographs of 13 patients, who had undergone tibial lengthening by the Ilizarov method, were studied. For each patient, 10 radiographs, taken at an equal number of postoperative successive time moments, were available. Employing available software, 3 Regions Of Interest (ROIs), corresponding to the: (a) upper, (b) central, and (c) lower aspect of the gap, where bone regeneration was expected to occur, were determined on each radiograph. Employing custom developed algorithms: (i) a number of textural features were generated from each of the ROIs, and (ii) a texture-feature based regression model was designed for the quantitative monitoring of the bone regeneration process. Statistically significant differences (p < 0.05) were derived for the initial and the final textural features values, generated from the first and the last postoperatively obtained radiographs, respectively. A quadratic polynomial regression equation fitted data adequately (r2 = 0.9, p < 0.001). The suggested method may contribute to the monitoring of the tibial bone regeneration process.

Defining regions of interest using cross-frequency coupling in extratemporal lobe epilepsy patients

NASA Astrophysics Data System (ADS)

Guirgis, Mirna; Chinvarun, Yotin; del Campo, Martin; Carlen, Peter L.; Bardakjian, Berj L.

2015-04-01

Objective. Clinicians identify seizure onset zones (SOZs) for resection in an attempt to localize the epileptogenic zone (EZ), which is the cortical tissue that is indispensible for seizure generation. An automated system is proposed to objectively localize this EZ by identifying regions of interest (ROIs). Methods. Intracranial electroencephalogram recordings were obtained from seven patients presenting with extratemporal lobe epilepsy and the interaction between neuronal rhythms in the form of phase-amplitude coupling was investigated. Modulation of the amplitude of high frequency oscillations (HFOs) by the phase of low frequency oscillations was measured by computing the modulation index (MI). Delta- (0.5-4 Hz) and theta- (4-8 Hz) modulation of HFOs (30-450 Hz) were examined across the channels of a 64-electrode subdural grid. Surrogate analysis was performed and false discovery rates were computed to determine the significance of the modulation observed. Mean MI values were subjected to eigenvalue decomposition (EVD) and channels defining the ROIs were selected based on the components of the eigenvector corresponding to the largest eigenvalue. ROIs were compared to the SOZs identified by two independent neurologists. Global coherence values were also computed. Main results. MI was found to capture the seizure in time for six of seven patients and identified ROIs in all seven. Patients were found to have a poorer post-surgical outcome when the number of EVD-selected channels that were not resected increased. Moreover, in patients who experienced a seizure-free outcome (i.e., Engel Class I) all EVD-selected channels were found to be within the resected tissue or immediately adjacent to it. In these Engel Class I patients, delta-modulated HFOs were found to identify more of the channels in the resected tissue compared to theta-modulated HFOs. However, for the Engel Class IV patient, the delta-modulated HFOs did not identify any of the channels in the resected tissue suggesting that the resected tissue was not appropriate, which was also suggested by the Engel Class IV outcome. A sensitivity of 75.4% and a false positive rate of 15.6% were achieved using delta-modulated HFOs in an Engel Class I patient. Significance. LFO-modulated HFOs can be used to identify ROIs in extratemporal lobe patients. Moreover, delta-modulated HFOs may provide more accurate localization of the EZ. These ROIs may result in better surgical outcomes when used to compliment the SOZs identified by clinicians for resection.

CBF measured by Xe-CT: Approach to analysis and normal values

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yonas, H.; Darby, J.M.; Marks, E.C.

1991-09-01

Normal reference values and a practical approach to CBF analysis are needed for routine clinical analysis and interpretation of xenon-enhanced computed tomography (CT) CBF studies. The authors measured CBF in 67 normal individuals with the GE 9800 CT scanner adapted for CBF imaging with stable Xe. CBF values for vascular territories were systematically analyzed using the clustering of contiguous 2-cm circular regions of interest (ROIs) placed within the cortical mantle and basal ganglia. Mixed cortical flows averaged 51 {plus minus} 10ml.100g-1.min-1. High and low flow compartments, sampled by placing 5-mm circular ROIs in regions containing the highest and lowest flowmore » values in each hemisphere, averaged 84 {plus minus} 14 and 20 {plus minus} 5 ml.100 g-1.min-1, respectively. Mixed cortical flow values as well as values within the high flow compartment demonstrated significant decline with age; however, there were no significant age-related changes in the low flow compartment. The clustering of systematically placed cortical and subcortical ROIs has provided a normative data base for Xe-CT CBF and a flexible and uncomplicated method for the analysis of CBF maps generated by Xe-enhanced CT.« less

Data analysis in emission tomography using emission-count posteriors

NASA Astrophysics Data System (ADS)

Sitek, Arkadiusz

2012-11-01

A novel approach to the analysis of emission tomography data using the posterior probability of the number of emissions per voxel (emission count) conditioned on acquired tomographic data is explored. The posterior is derived from the prior and the Poisson likelihood of the emission-count data by marginalizing voxel activities. Based on emission-count posteriors, examples of Bayesian analysis including estimation and classification tasks in emission tomography are provided. The application of the method to computer simulations of 2D tomography is demonstrated. In particular, the minimum-mean-square-error point estimator of the emission count is demonstrated. The process of finding this estimator can be considered as a tomographic image reconstruction technique since the estimates of the number of emissions per voxel divided by voxel sensitivities and acquisition time are the estimates of the voxel activities. As an example of a classification task, a hypothesis stating that some region of interest (ROI) emitted at least or at most r-times the number of events in some other ROI is tested. The ROIs are specified by the user. The analysis described in this work provides new quantitative statistical measures that can be used in decision making in diagnostic imaging using emission tomography.

Autonomous Image Processing Algorithms Locate Region-of-Interests: The Mars Rover Application

NASA Technical Reports Server (NTRS)

Privitera, Claudio; Azzariti, Michela; Stark, Lawrence W.

1998-01-01

In this report, we demonstrate that bottom-up IPA's, image-processing algorithms, can perform a new visual task to select and locate Regions-Of-Interests (ROIs). This task has been defined on the basis of a theory of top-down human vision, the scanpath theory. Further, using measures, Sp and Ss, the similarity of location and ordering, respectively, developed over the years in studying human perception and the active looking role of eye movements, we could quantify the efficient and efficacious manner that IPAs can imitate human vision in located ROIS. The means to quantitatively evaluate IPA performance has been an important part of our study. In fact, these measures were essential in choosing from the initial wide variety of IPAS, that particular one that best serves for a type of picture and for a required task. It should be emphasized that the selection of efficient IPAs has depended upon their correlation with actual human chosen ROIs for the same type of picture and for the same required task accomplishment.

Kinetic Modeling of PET Data Without Blood Sampling

NASA Astrophysics Data System (ADS)

Bentourkia, M.

2005-06-01

In positron emission tomography (PET) imaging, application of kinetic modeling always requires an input curve (IC) together with the PET data. The IC can be obtained by means of external blood sampling or, in the case of cardiac studies, by means of a region-of-interest (ROI) drawn on the blood pool. It is, however, very unsuitable to withdraw and to analyze blood samples, and in small animals, these operations become difficult, while ICs determined from ROIs are generally contaminated by emissions from neighboring sites, or they are underestimated because of partial volume effect. In this paper, we report a new method to extract kinetic parameters from dynamic PET studies without a priori knowledge of the IC. The method is applied in human brain data measured with fluorodeoxyglucose (FDG) human-brain and in cardiac-rat perfusion studies with /sup 13/N-ammonia and /sup 11/C-acetate. The tissue blood volume (TBV), usually fitted together with the rate constants, is extracted simultaneously with the tissue time activity curves for cardiac studies, while for brain gray matter, TBV is known to be about 4% to 7%. The shape of IC is obtained by means of factor analysis from an ROI drawn around a cardiac tissue or a brain artery. The results show a good correlation (p<0.05) between the cerebral metabolic rate of glucose, myocardial blood flow, and oxygen consumption obtained with the new method in comparison to the usual method. In conclusion, it is possible to apply kinetic modeling without any blood sampling, which significantly simplifies PET acquisition and data analysis.

Laser line scan underwater imaging by complementary metal-oxide-semiconductor camera

NASA Astrophysics Data System (ADS)

He, Zhiyi; Luo, Meixing; Song, Xiyu; Wang, Dundong; He, Ning

2017-12-01

This work employs the complementary metal-oxide-semiconductor (CMOS) camera to acquire images in a scanning manner for laser line scan (LLS) underwater imaging to alleviate backscatter impact of seawater. Two operating features of the CMOS camera, namely the region of interest (ROI) and rolling shutter, can be utilized to perform image scan without the difficulty of translating the receiver above the target as the traditional LLS imaging systems have. By the dynamically reconfigurable ROI of an industrial CMOS camera, we evenly divided the image into five subareas along the pixel rows and then scanned them by changing the ROI region automatically under the synchronous illumination by the fun beams of the lasers. Another scanning method was explored by the rolling shutter operation of the CMOS camera. The fun beam lasers were turned on/off to illuminate the narrow zones on the target in a good correspondence to the exposure lines during the rolling procedure of the camera's electronic shutter. The frame synchronization between the image scan and the laser beam sweep may be achieved by either the strobe lighting output pulse or the external triggering pulse of the industrial camera. Comparison between the scanning and nonscanning images shows that contrast of the underwater image can be improved by our LLS imaging techniques, with higher stability and feasibility than the mechanically controlled scanning method.

A Novel Azimuth Super-Resolution Method by Synthesizing Azimuth Bandwidth of Multiple Tracks of Airborne Stripmap SAR Data

PubMed Central

Wang, Yan; Li, Jingwen; Sun, Bing; Yang, Jian

2016-01-01

Azimuth resolution of airborne stripmap synthetic aperture radar (SAR) is restricted by the azimuth antenna size. Conventionally, a higher azimuth resolution should be achieved by employing alternate modes that steer the beam in azimuth to enlarge the synthetic antenna aperture. However, if a data set of a certain region, consisting of multiple tracks of airborne stripmap SAR data, is available, the azimuth resolution of specific small region of interest (ROI) can be conveniently improved by a novel azimuth super-resolution method as introduced by this paper. The proposed azimuth super-resolution method synthesize the azimuth bandwidth of the data selected from multiple discontinuous tracks and contributes to a magnifier-like function with which the ROI can be further zoomed in with a higher azimuth resolution than that of the original stripmap images. Detailed derivation of the azimuth super-resolution method, including the steps of two-dimensional dechirping, residual video phase (RVP) removal, data stitching and data correction, is provided. The restrictions of the proposed method are also discussed. Lastly, the presented approach is evaluated via both the single- and multi-target computer simulations. PMID:27304959

DOE Office of Scientific and Technical Information (OSTI.GOV)

Guinn, I.; Buuck, M.; Cuesta, C.

The MAJORANA Collaboration will seek neutrinoless double beta decay (0νββ) in {sup 76}Ge using isotopically enriched p-type point contact (PPC) high purity Germanium (HPGe) detectors. A tonne-scale array of HPGe detectors would require background levels below 1 count/ROI-tonne-year in the 4 keV region of interest (ROI) around the 2039 keV Q-value of the decay. In order to demonstrate the feasibility of such an experiment, the MAJORANA DEMONSTRATOR, a 40 kg HPGe detector array, is being constructed with a background goal of < 3 count/ROI-tonne-year, which is expected to scale down to < 1 count/ROI-tonne-year for a tonne-scale experiment. The signalmore » readout electronics, which must be placed in close proximity to the detectors, present a challenge toward reaching this background goal. This talk will discuss the materials and design used to construct signal readout electronics with low enough backgrounds for the MAJORANA DEMONSTRATOR.« less

Functional near-infrared spectroscopy (fNIRS) brain imaging of multi-sensory integration during computerized dynamic posturography in middle-aged and older adults.

PubMed

Lin, Chia-Cheng; Barker, Jeffrey W; Sparto, Patrick J; Furman, Joseph M; Huppert, Theodore J

2017-04-01

Studies suggest that aging affects the sensory re-weighting process, but the neuroimaging evidence is minimal. Functional Near-Infrared Spectroscopy (fNIRS) is a novel neuroimaging tool that can detect brain activities during dynamic movement condition. In this study, fNIRS was used to investigate the hemodynamic changes in the frontal-lateral, temporal-parietal, and occipital regions of interest (ROIs) during four sensory integration conditions that manipulated visual and somatosensory feedback in 15 middle-aged and 15 older adults. The results showed that the temporal-parietal ROI was activated more when somatosensory and visual information were absent in both groups, which indicated the sole use of vestibular input for maintaining balance. While both older adults and middle-aged adults had greater activity in most brain ROIs during changes in the sensory conditions, the older adults had greater increases in the occipital ROI and frontal-lateral ROIs. These findings suggest a cortical component to sensory re-weighting that is more distributed and requires greater attention in older adults.

Computer-aided diagnostic system for diffuse liver diseases with ultrasonography by neural networks

NASA Astrophysics Data System (ADS)

Ogawa, K.; Fukushima, M.; Kubota, K.; Hisa, N.

1998-12-01

The aim of the study is to establish a computer-aided diagnostic system for diffuse liver diseases such as chronic active hepatitis (CAH) and liver cirrhosis (LC). The authors introduced an artificial neural network in the classification of these diseases. In this system the neural network was trained by feature parameters extracted from B-mode ultrasonic images of normal liver (NL), CAH and LC. For input data the authors used six parameters calculated by a region of interest (ROI) and a parameter calculated by five ROIs in each image. They were variance of pixel values, coefficient of variation, annular Fourier power spectrum, longitudinal Fourier power spectrum which were calculated for the ROI, and variation of the means of the five ROIs. In addition, the authors used two more parameters calculated from a co-occurrence matrix of pixel values in the ROI. The results showed that the neural network classifier was 83.8% in sensitivity for LC, 90.0% in sensitivity for CAH and 93.6% in specificity, and the system was considered to be helpful for clinical and educational use.

Fully convolutional networks with double-label for esophageal cancer image segmentation by self-transfer learning

NASA Astrophysics Data System (ADS)

Xue, Di-Xiu; Zhang, Rong; Zhao, Yuan-Yuan; Xu, Jian-Ming; Wang, Ya-Lei

2017-07-01

Cancer recognition is the prerequisite to determine appropriate treatment. This paper focuses on the semantic segmentation task of microvascular morphological types on narrowband images to aid clinical examination of esophageal cancer. The most challenge for semantic segmentation is incomplete-labeling. Our key insight is to build fully convolutional networks (FCNs) with double-label to make pixel-wise predictions. The roi-label indicating ROIs (region of interest) is introduced as extra constraint to guild feature learning. Trained end-to-end, the FCN model with two target jointly optimizes both segmentation of sem-label (semantic label) and segmentation of roi-label within the framework of self-transfer learning based on multi-task learning theory. The learning representation ability of shared convolutional networks for sem-label is improved with support of roi-label via achieving a better understanding of information outside the ROIs. Our best FCN model gives satisfactory segmentation result with mean IU up to 77.8% (pixel accuracy > 90%). The results show that the proposed approach is able to assist clinical diagnosis to a certain extent.

Infrared thermography in the evaluation of meibomian gland dysfunction.

PubMed

Su, Tai-Yuan; Ho, Wei-Ting; Chiang, Shu-Chiung; Lu, Chien-Yi; Chiang, Huihua Kenny; Chang, Shu-Wen

2017-07-01

To evaluate meibomian gland dysfunction (MGD) by infrared thermography. An observational study was conducted at the Department of Ophthalmology, Far Eastern Memorial Hospital, New Taipei City, Taiwan. Participants included 89 MGD patients (30 in Grade 1, 49 in Grade 2, and 10 in Grade 3) and 65 controls. The close-eye thermographic images of the eyelid were obtained noninvasively by infrared thermography. Temperatures at 8 regions of interest (ROIs) of the eyelid margin and a reference temperature at the center of the upper eyelid were measured. The temperature ratio was defined as the temperature of ROI divided by the reference temperature. Eyelid margin temperature measured by infrared thermography increased from temporal side (ROI 1) to the nasal side (ROI 8) of the eye in both MGD patients and control groups. The temperature ratios were significantly higher in MGD participants than in controls, especially at ROI 8. The eyelid margin temperature measured by infrared thermography was higher in MGD participants. Further development of this infrared thermography system may become a rapid and non-invasive tool for MGD screening. Copyright © 2016. Published by Elsevier B.V.

Automated detection of periventricular veins on 7 T brain MRI

NASA Astrophysics Data System (ADS)

Kuijf, Hugo J.; Bouvy, Willem H.; Zwanenburg, Jaco J. M.; Viergever, Max A.; Biessels, Geert Jan; Vincken, Koen L.

2015-03-01

Cerebral small vessel disease is common in elderly persons and a leading cause of cognitive decline, dementia, and acute stroke. With the introduction of ultra-high field strength 7.0T MRI, it is possible to visualize small vessels in the brain. In this work, a proof-of-principle study is conducted to assess the feasibility of automatically detecting periventricular veins. Periventricular veins are organized in a fan-pattern and drain venous blood from the brain towards the caudate vein of Schlesinger, which is situated along the lateral ventricles. Just outside this vein, a region-of- interest (ROI) through which all periventricular veins must cross is defined. Within this ROI, a combination of the vesselness filter, tubular tracking, and hysteresis thresholding is applied to locate periventricular veins. All detected locations were evaluated by an expert human observer. The results showed a positive predictive value of 88% and a sensitivity of 95% for detecting periventricular veins. The proposed method shows good results in detecting periventricular veins in the brain on 7.0T MR images. Compared to previous works, that only use a 1D or 2D ROI and limited image processing, our work presents a more comprehensive definition of the ROI, advanced image processing techniques to detect periventricular veins, and a quantitative analysis of the performance. The results of this proof-of-principle study are promising and will be used to assess periventricular veins on 7.0T brain MRI.

Anatomy guided automated SPECT renal seed point estimation

NASA Astrophysics Data System (ADS)

Dwivedi, Shekhar; Kumar, Sailendra

2010-04-01

Quantification of SPECT(Single Photon Emission Computed Tomography) images can be more accurate if correct segmentation of region of interest (ROI) is achieved. Segmenting ROI from SPECT images is challenging due to poor image resolution. SPECT is utilized to study the kidney function, though the challenge involved is to accurately locate the kidneys and bladder for analysis. This paper presents an automated method for generating seed point location of both kidneys using anatomical location of kidneys and bladder. The motivation for this work is based on the premise that the anatomical location of the bladder relative to the kidneys will not differ much. A model is generated based on manual segmentation of the bladder and both the kidneys on 10 patient datasets (including sum and max images). Centroid is estimated for manually segmented bladder and kidneys. Relatively easier bladder segmentation is followed by feeding bladder centroid coordinates into the model to generate seed point for kidneys. Percentage error observed in centroid coordinates of organs from ground truth to estimated values from our approach are acceptable. Percentage error of approximately 1%, 6% and 2% is observed in X coordinates and approximately 2%, 5% and 8% is observed in Y coordinates of bladder, left kidney and right kidney respectively. Using a regression model and the location of the bladder, the ROI generation for kidneys is facilitated. The model based seed point estimation will enhance the robustness of kidney ROI estimation for noisy cases.

TU-D-209-02: A Backscatter Point Spread Function for Entrance Skin Dose Determination

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vijayan, S; Xiong, Z; Shankar, A

Purpose: To determine the distribution of backscattered radiation to the skin resulting from a non-uniform distribution of primary radiation through convolution with a backscatter point spread function (PSF). Methods: A backscatter PSF is determined using Monte Carlo simulation of a 1 mm primary beam incident on a 30 × 30 cm × 20 cm thick PMMA phantom using EGSnrc software. A primary profile is similarly obtained without the phantom and the difference from the total provides the backscatter profile. This scatter PSF characterizes the backscatter spread for a “point” primary interaction and can be convolved with the entrance primary dosemore » distribution to obtain the total entrance skin dose. The backscatter PSF was integrated into the skin dose tracking system (DTS), a graphical utility for displaying the color-coded skin dose distribution on a 3D graphic of the patient during interventional fluoroscopic procedures. The backscatter convolution method was validated for the non-uniform beam resulting from the use of an ROI attenuator. The ROI attenuator is a copper sheet with about 20% primary transmission (0.7 mm thick) containing a circular aperture; this attenuator is placed in the beam to reduce dose in the periphery while maintaining full dose in the region of interest. The DTS calculated primary plus backscatter distribution is compared to that measured with GafChromic film and that calculated using EGSnrc Monte-Carlo software. Results: The PSF convolution method used in the DTS software was able to account for the spread of backscatter from the ROI region to the region under the attenuator. The skin dose distribution determined using DTS with the ROI attenuator was in good agreement with the distributions measured with Gafchromic film and determined by Monte Carlo simulation Conclusion: The PSF convolution technique provides an accurate alternative for entrance skin dose determination with non-uniform primary x-ray beams. Partial support from NIH Grant R01-EB002873 and Toshiba Medical Systems Corp.« less

Genetic and environmental influences on the size of specific brain regions in midlife: the VETSA MRI study.

PubMed

Kremen, William S; Prom-Wormley, Elizabeth; Panizzon, Matthew S; Eyler, Lisa T; Fischl, Bruce; Neale, Michael C; Franz, Carol E; Lyons, Michael J; Pacheco, Jennifer; Perry, Michele E; Stevens, Allison; Schmitt, J Eric; Grant, Michael D; Seidman, Larry J; Thermenos, Heidi W; Tsuang, Ming T; Eisen, Seth A; Dale, Anders M; Fennema-Notestine, Christine

2010-01-15

The impact of genetic and environmental factors on human brain structure is of great importance for understanding normative cognitive and brain aging as well as neuropsychiatric disorders. However, most studies of genetic and environmental influences on human brain structure have either focused on global measures or have had samples that were too small for reliable estimates. Using the classical twin design, we assessed genetic, shared environmental, and individual-specific environmental influences on individual differences in the size of 96 brain regions of interest (ROIs). Participants were 474 middle-aged male twins (202 pairs; 70 unpaired) in the Vietnam Era Twin Study of Aging (VETSA). They were 51-59 years old, and were similar to U.S. men in their age range in terms of sociodemographic and health characteristics. We measured thickness of cortical ROIs and volume of other ROIs. On average, genetic influences accounted for approximately 70% of the variance in the volume of global, subcortical, and ventricular ROIs and approximately 45% of the variance in the thickness of cortical ROIs. There was greater variability in the heritability of cortical ROIs (0.00-0.75) as compared with subcortical and ventricular ROIs (0.48-0.85). The results did not indicate lateralized heritability differences or greater genetic influences on the size of regions underlying higher cognitive functions. The findings provide key information for imaging genetic studies and other studies of brain phenotypes and endophenotypes. Longitudinal analysis will be needed to determine whether the degree of genetic and environmental influences changes for different ROIs from midlife to later life.

Intercomparison of methods for image quality characterization. II. Noise power spectrum

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dobbins, James T. III; Samei, Ehsan; Ranger, Nicole T.

Second in a two-part series comparing measurement techniques for the assessment of basic image quality metrics in digital radiography, in this paper we focus on the measurement of the image noise power spectrum (NPS). Three methods were considered: (1) a method published by Dobbins et al. [Med. Phys. 22, 1581-1593 (1995)] (2) a method published by Samei et al. [Med. Phys. 30, 608-622 (2003)], and (3) a new method sanctioned by the International Electrotechnical Commission (IEC 62220-1, 2003), developed as part of an international standard for the measurement of detective quantum efficiency. In addition to an overall comparison of themore » estimated NPS between the three techniques, the following factors were also evaluated for their effect on the measured NPS: horizontal versus vertical directional dependence, the use of beam-limiting apertures, beam spectrum, and computational methods of NPS analysis, including the region-of-interest (ROI) size and the method of ROI normalization. Of these factors, none was found to demonstrate a substantial impact on the amplitude of the NPS estimates ({<=}3.1% relative difference in NPS averaged over frequency, for each factor considered separately). Overall, the three methods agreed to within 1.6%{+-}0.8% when averaged over frequencies >0.15 mm{sup -1}.« less

Repeatability of Brain Volume Measurements Made with the Atlas-based Method from T1-weighted Images Acquired Using a 0.4 Tesla Low Field MR Scanner.

PubMed

Goto, Masami; Suzuki, Makoto; Mizukami, Shinya; Abe, Osamu; Aoki, Shigeki; Miyati, Tosiaki; Fukuda, Michinari; Gomi, Tsutomu; Takeda, Tohoru

2016-10-11

An understanding of the repeatability of measured results is important for both the atlas-based and voxel-based morphometry (VBM) methods of magnetic resonance (MR) brain volumetry. However, many recent studies that have investigated the repeatability of brain volume measurements have been performed using static magnetic fields of 1-4 tesla, and no study has used a low-strength static magnetic field. The aim of this study was to investigate the repeatability of measured volumes using the atlas-based method and a low-strength static magnetic field (0.4 tesla). Ten healthy volunteers participated in this study. Using a 0.4 tesla magnetic resonance imaging (MRI) scanner and a quadrature head coil, three-dimensional T 1 -weighted images (3D-T 1 WIs) were obtained from each subject, twice on the same day. VBM8 software was used to construct segmented normalized images [gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) images]. The regions-of-interest (ROIs) of GM, WM, CSF, hippocampus (HC), orbital gyrus (OG), and cerebellum posterior lobe (CPL) were generated using WFU PickAtlas. The percentage change was defined as[100 × (measured volume with first segmented image - mean volume in each subject)/(mean volume in each subject)]The average percentage change was calculated as the percentage change in the 6 ROIs of the 10 subjects. The mean of the average percentage changes for each ROI was as follows: GM, 0.556%; WM, 0.324%; CSF, 0.573%; HC, 0.645%; OG, 1.74%; and CPL, 0.471%. The average percentage change was higher for the orbital gyrus than for the other ROIs. We consider that repeatability of the atlas-based method is similar between 0.4 and 1.5 tesla MR scanners. To our knowledge, this is the first report to show that the level of repeatability with a 0.4 tesla MR scanner is adequate for the estimation of brain volume change by the atlas-based method.

Automated selection of trabecular bone regions in knee radiographs.

PubMed

Podsiadlo, P; Wolski, M; Stachowiak, G W

2008-05-01

Osteoarthritic (OA) changes in knee joints can be assessed by analyzing the structure of trabecular bone (TB) in the tibia. This analysis is performed on TB regions selected manually by a human operator on x-ray images. Manual selection is time-consuming, tedious, and expensive. Even if a radiologist expert or highly trained person is available to select regions, high inter- and intraobserver variabilities are still possible. A fully automated image segmentation method was, therefore, developed to select the bone regions for numerical analyses of changes in bone structures. The newly developed method consists of image preprocessing, delineation of cortical bone plates (active shape model), and location of regions of interest (ROI). The method was trained on an independent set of 40 x-ray images. Automatically selected regions were compared to the "gold standard" that contains ROIs selected manually by a radiologist expert on 132 x-ray images. All images were acquired from subjects locked in a standardized standing position using a radiography rig. The size of each ROI is 12.8 x 12.8 mm. The automated method results showed a good agreement with the gold standard [similarity index (SI) = 0.83 (medial) and 0.81 (lateral) and the offset =[-1.78, 1.27]x[-0.65,0.26] mm (medial) and [-2.15, 1.59]x[-0.58, 0.52] mm (lateral)]. Bland and Altman plots were constructed for fractal signatures, and changes of fractal dimensions (FD) to region offsets calculated between the gold standard and automatically selected regions were calculated. The plots showed a random scatter and the 95% confidence intervals were (-0.006, 0.008) and (-0.001, 0.011). The changes of FDs to region offsets were less than 0.035. Previous studies showed that differences in FDs between non-OA and OA bone regions were greater than 0.05. ROIs were also selected by a second radiologist and then evaluated. Results indicated that the newly developed method could replace a human operator and produces bone regions with an accuracy that is sufficient for fractal analyses of bone texture.

An application of Chan-Vese method used to determine the ROI area in CT lung screening

NASA Astrophysics Data System (ADS)

Prokop, Paweł; Surtel, Wojciech

2016-09-01

The article presents two approaches of determining the ROI area in CT lung screening. First approach is based on a classic method of framing the image in order to determine the ROI by using a MaZda tool. Second approach is based on segmentation of CT images of the lungs and reducing the redundant information from the image. Of the two approaches of an Active Contour, it was decided to choose the Chan-Vese method. In order to determine the effectiveness of the approach, it was performed an analysis of received ROI texture and extraction of textural features. In order to determine the effectiveness of the method, it was performed an analysis of the received ROI textures and extraction of the texture features, by using a Mazda tool. The results were compared and presented in the form of the radar graphs. The second approach proved to be effective and appropriate and consequently it is used for further analysis of CT images, in the computer-aided diagnosis of sarcoidosis.

Markerless attenuation correction for carotid MRI surface receiver coils in combined PET/MR imaging

NASA Astrophysics Data System (ADS)

Eldib, Mootaz; Bini, Jason; Robson, Philip M.; Calcagno, Claudia; Faul, David D.; Tsoumpas, Charalampos; Fayad, Zahi A.

2015-06-01

The purpose of the study was to evaluate the effect of attenuation of MR coils on quantitative carotid PET/MR exams. Additionally, an automated attenuation correction method for flexible carotid MR coils was developed and evaluated. The attenuation of the carotid coil was measured by imaging a uniform water phantom injected with 37 MBq of 18F-FDG in a combined PET/MR scanner for 24 min with and without the coil. In the same session, an ultra-short echo time (UTE) image of the coil on top of the phantom was acquired. Using a combination of rigid and non-rigid registration, a CT-based attenuation map was registered to the UTE image of the coil for attenuation and scatter correction. After phantom validation, the effect of the carotid coil attenuation and the attenuation correction method were evaluated in five subjects. Phantom studies indicated that the overall loss of PET counts due to the coil was 6.3% with local region-of-interest (ROI) errors reaching up to 18.8%. Our registration method to correct for attenuation from the coil decreased the global error and local error (ROI) to 0.8% and 3.8%, respectively. The proposed registration method accurately captured the location and shape of the coil with a maximum spatial error of 2.6 mm. Quantitative analysis in human studies correlated with the phantom findings, but was dependent on the size of the ROI used in the analysis. MR coils result in significant error in PET quantification and thus attenuation correction is needed. The proposed strategy provides an operator-free method for attenuation and scatter correction for a flexible MRI carotid surface coil for routine clinical use.

White matter changes in comatose survivors of anoxic ischemic encephalopathy and traumatic brain injury: comparative diffusion-tensor imaging study.

PubMed

van der Eerden, Anke W; Khalilzadeh, Omid; Perlbarg, Vincent; Dinkel, Julien; Sanchez, Paola; Vos, Pieter E; Luyt, Charles-Edouard; Stevens, Robert D; Menjot de Champfleur, Nicolas; Delmaire, Christine; Tollard, Eleonore; Gupta, Rajiv; Dormont, Didier; Laureys, Steven; Benali, Habib; Vanhaudenhuyse, Audrey; Galanaud, Damien; Puybasset, Louis

2014-02-01

To analyze white matter pathologic abnormalities by using diffusion-tensor (DT) imaging in a multicenter prospective cohort of comatose patients following cardiac arrest or traumatic brain injury (TBI). Institutional review board approval and informed consent from proxies and control subjects were obtained. DT imaging was performed 5-57 days after insult in 49 cardiac arrest and 40 TBI patients. To control for DT imaging-processing variability, patients' values were normalized to those of 111 control subjects. Automated segmentation software calculated normalized axial diffusivity (λ1) and radial diffusivity (λ⊥) in 19 predefined white matter regions of interest (ROIs). DT imaging variables were compared by using general linear modeling, and side-to-side Pearson correlation coefficients were calculated. P values were corrected for multiple testing (Bonferroni). In central white matter, λ1 differed from that in control subjects in six of seven TBI ROIs and five of seven cardiac arrest ROIs (all P < .01). The λ⊥ differed from that in control subjects in all ROIs in both patient groups (P < .01). In hemispheres, λ1 was decreased compared with that in control subjects in three of 12 TBI ROIs (P < .05) and nine of 12 cardiac arrest ROIs (P < .01). The λ⊥ was increased in all TBI ROIs (P < .01) and in seven of 12 cardiac arrest ROIs (P < .05). Cerebral hemisphere λ1 was lower in cardiac arrest than in TBI in six of 12 ROIs (P < .01), while λ⊥ was higher in TBI than in cardiac arrest in eight of 12 ROIs (P < .01). Diffusivity values were symmetrically distributed in cardiac arrest (P < .001 for side-to-side correlation) but not in TBI patients. DT imaging findings are consistent with the known predominance of cerebral hemisphere axonal injury in cardiac arrest and chiefly central myelin injury in TBI. This consistency supports the validity of DT imaging for differentiating axon and myelin damage in vivo in humans. © RSNA, 2013

The fast iris image clarity evaluation based on Tenengrad and ROI selection

NASA Astrophysics Data System (ADS)

Gao, Shuqin; Han, Min; Cheng, Xu

2018-04-01

In iris recognition system, the clarity of iris image is an important factor that influences recognition effect. In the process of recognition, the blurred image may possibly be rejected by the automatic iris recognition system, which will lead to the failure of identification. Therefore it is necessary to evaluate the iris image definition before recognition. Considered the existing evaluation methods on iris image definition, we proposed a fast algorithm to evaluate the definition of iris image in this paper. In our algorithm, firstly ROI (Region of Interest) is extracted based on the reference point which is determined by using the feature of the light spots within the pupil, then Tenengrad operator is used to evaluate the iris image's definition. Experiment results show that, the iris image definition algorithm proposed in this paper could accurately distinguish the iris images of different clarity, and the algorithm has the merit of low computational complexity and more effectiveness.

A Novel Segmentation Approach Combining Region- and Edge-Based Information for Ultrasound Images

PubMed Central

Luo, Yaozhong; Liu, Longzhong; Li, Xuelong

2017-01-01

Ultrasound imaging has become one of the most popular medical imaging modalities with numerous diagnostic applications. However, ultrasound (US) image segmentation, which is the essential process for further analysis, is a challenging task due to the poor image quality. In this paper, we propose a new segmentation scheme to combine both region- and edge-based information into the robust graph-based (RGB) segmentation method. The only interaction required is to select two diagonal points to determine a region of interest (ROI) on the original image. The ROI image is smoothed by a bilateral filter and then contrast-enhanced by histogram equalization. Then, the enhanced image is filtered by pyramid mean shift to improve homogeneity. With the optimization of particle swarm optimization (PSO) algorithm, the RGB segmentation method is performed to segment the filtered image. The segmentation results of our method have been compared with the corresponding results obtained by three existing approaches, and four metrics have been used to measure the segmentation performance. The experimental results show that the method achieves the best overall performance and gets the lowest ARE (10.77%), the second highest TPVF (85.34%), and the second lowest FPVF (4.48%). PMID:28536703

[Methodological aspects related to the determination of the relative renal function using 99mTC MAG3].

PubMed

Ladrón De Guevara Hernández, D; Ham, H; Franken, P; Piepsz, A; Lobo Sotomayor, G

2002-01-01

The aim of the study was to evaluate three different methods for calculating the split renal function in patients with only one functioning kidney, keeping in mind that the split function should be zero on the side of the non-functioning kidney. We retrospectively selected 28 99mTc MAG3 renograms performed in children, 12 with unilateral nephrectomy, 4 with unilateral agenesis and 12 with a non-functioning kidney. A renal and perirenal region of interest (ROI) were delineated around the functioning kidney. The ROIs around the empty kidney were drawn symmetrically to the contralateral side. The split renal function was calculated using three different methods, the integral method, the slope method and the Patlak-Rutland algorithm. For the whole group of 28 kidneys as well as for the three categories of patients, the three methods provided a split function on the side of the non-functioning kidney close to the zero value, regardless of whether the empty kidney was the left or the right one. We recommend the use of the integral method for the whole range of split renal function with 99mTc MAG3. No significant improvement was obtained by means of the more sophisticated Patlak-Rutland method.

Improved explanation of human intelligence using cortical features with second order moments and regression.

PubMed

Park, Hyunjin; Yang, Jin-ju; Seo, Jongbum; Choi, Yu-yong; Lee, Kun-ho; Lee, Jong-min

2014-04-01

Cortical features derived from magnetic resonance imaging (MRI) provide important information to account for human intelligence. Cortical thickness, surface area, sulcal depth, and mean curvature were considered to explain human intelligence. One region of interest (ROI) of a cortical structure consisting of thousands of vertices contained thousands of measurements, and typically, one mean value (first order moment), was used to represent a chosen ROI, which led to a potentially significant loss of information. We proposed a technological improvement to account for human intelligence in which a second moment (variance) in addition to the mean value was adopted to represent a chosen ROI, so that the loss of information would be less severe. Two computed moments for the chosen ROIs were analyzed with partial least squares regression (PLSR). Cortical features for 78 adults were measured and analyzed in conjunction with the full-scale intelligence quotient (FSIQ). Our results showed that 45% of the variance of the FSIQ could be explained using the combination of four cortical features using two moments per chosen ROI. Our results showed improvement over using a mean value for each ROI, which explained 37% of the variance of FSIQ using the same set of cortical measurements. Our results suggest that using additional second order moments is potentially better than using mean values of chosen ROIs for regression analysis to account for human intelligence. Copyright © 2014 Elsevier Ltd. All rights reserved.

Modulation of functionally localized right insular cortex activity using real-time fMRI-based neurofeedback.

PubMed

Berman, Brian D; Horovitz, Silvina G; Hallett, Mark

2013-01-01

The capacity for subjects to learn to volitionally control localized brain activity using neurofeedback is actively being investigated. We aimed to investigate the ability of healthy volunteers to quickly learn to use visual feedback during real-time functional MRI (rtfMRI) to modulate brain activity within their anterior right insular cortex (RIC) localized during a blink suppression task, an approach of possible interest in the use of rtfMRI to reduce urges. The RIC region of interest (RIC-ROI) was functionally localized using a blink suppression task, and blood-oxygen level dependent (BOLD) signal changes within RIC-ROI used to create a constantly updating display fed back to the subject in the scanner. Subjects were instructed to use emotional imagery to try and increase activity within RIC-ROI during four feedback training runs (FB1-FB4). A "control" run (CNTRL) before training and a "transfer" run (XSFR) after training were performed without feedback to assess for baseline abilities and learning effects. Fourteen participants completed all neurofeedback training runs. At the group-level, increased BOLD activity was seen in the anterior RIC during all the FB runs, but a significant increase in the functionally defined RIC-ROI was only attained during FB2. In atlas-defined insular cortex ROIs, significant increases were seen bilaterally during the CNTRL, FB1, FB2, and FB4 runs. Increased activity within the insular cortices did not show lateralization. Training did, however, result in a significant increase in functional connectivity between the RIC-ROI and the medial frontal gyrus when comparing FB4 to FB1. Since neurofeedback training did not lead to an increase in BOLD signal across all feedback runs, we suggest that learning to control one's brain activity in this fashion may require longer or repeated rtfMRI training sessions.

Nonlocal atlas-guided multi-channel forest learning for human brain labeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ma, Guangkai; Gao, Yaozong; Wu, Guorong

Purpose: It is important for many quantitative brain studies to label meaningful anatomical regions in MR brain images. However, due to high complexity of brain structures and ambiguous boundaries between different anatomical regions, the anatomical labeling of MR brain images is still quite a challenging task. In many existing label fusion methods, appearance information is widely used. However, since local anatomy in the human brain is often complex, the appearance information alone is limited in characterizing each image point, especially for identifying the same anatomical structure across different subjects. Recent progress in computer vision suggests that the context features canmore » be very useful in identifying an object from a complex scene. In light of this, the authors propose a novel learning-based label fusion method by using both low-level appearance features (computed from the target image) and high-level context features (computed from warped atlases or tentative labeling maps of the target image). Methods: In particular, the authors employ a multi-channel random forest to learn the nonlinear relationship between these hybrid features and target labels (i.e., corresponding to certain anatomical structures). Specifically, at each of the iterations, the random forest will output tentative labeling maps of the target image, from which the authors compute spatial label context features and then use in combination with original appearance features of the target image to refine the labeling. Moreover, to accommodate the high inter-subject variations, the authors further extend their learning-based label fusion to a multi-atlas scenario, i.e., they train a random forest for each atlas and then obtain the final labeling result according to the consensus of results from all atlases. Results: The authors have comprehensively evaluated their method on both public LONI-LBPA40 and IXI datasets. To quantitatively evaluate the labeling accuracy, the authors use the dice similarity coefficient to measure the overlap degree. Their method achieves average overlaps of 82.56% on 54 regions of interest (ROIs) and 79.78% on 80 ROIs, respectively, which significantly outperform the baseline method (random forests), with the average overlaps of 72.48% on 54 ROIs and 72.09% on 80 ROIs, respectively. Conclusions: The proposed methods have achieved the highest labeling accuracy, compared to several state-of-the-art methods in the literature.« less

Nonlocal atlas-guided multi-channel forest learning for human brain labeling

PubMed Central

Ma, Guangkai; Gao, Yaozong; Wu, Guorong; Wu, Ligang; Shen, Dinggang

2016-01-01

Purpose: It is important for many quantitative brain studies to label meaningful anatomical regions in MR brain images. However, due to high complexity of brain structures and ambiguous boundaries between different anatomical regions, the anatomical labeling of MR brain images is still quite a challenging task. In many existing label fusion methods, appearance information is widely used. However, since local anatomy in the human brain is often complex, the appearance information alone is limited in characterizing each image point, especially for identifying the same anatomical structure across different subjects. Recent progress in computer vision suggests that the context features can be very useful in identifying an object from a complex scene. In light of this, the authors propose a novel learning-based label fusion method by using both low-level appearance features (computed from the target image) and high-level context features (computed from warped atlases or tentative labeling maps of the target image). Methods: In particular, the authors employ a multi-channel random forest to learn the nonlinear relationship between these hybrid features and target labels (i.e., corresponding to certain anatomical structures). Specifically, at each of the iterations, the random forest will output tentative labeling maps of the target image, from which the authors compute spatial label context features and then use in combination with original appearance features of the target image to refine the labeling. Moreover, to accommodate the high inter-subject variations, the authors further extend their learning-based label fusion to a multi-atlas scenario, i.e., they train a random forest for each atlas and then obtain the final labeling result according to the consensus of results from all atlases. Results: The authors have comprehensively evaluated their method on both public LONI_LBPA40 and IXI datasets. To quantitatively evaluate the labeling accuracy, the authors use the dice similarity coefficient to measure the overlap degree. Their method achieves average overlaps of 82.56% on 54 regions of interest (ROIs) and 79.78% on 80 ROIs, respectively, which significantly outperform the baseline method (random forests), with the average overlaps of 72.48% on 54 ROIs and 72.09% on 80 ROIs, respectively. Conclusions: The proposed methods have achieved the highest labeling accuracy, compared to several state-of-the-art methods in the literature. PMID:26843260

An interactive toolkit to extract phenological time series data from digital repeat photography

NASA Astrophysics Data System (ADS)

Seyednasrollah, B.; Milliman, T. E.; Hufkens, K.; Kosmala, M.; Richardson, A. D.

2017-12-01

Near-surface remote sensing and in situ photography are powerful tools to study how climate change and climate variability influence vegetation phenology and the associated seasonal rhythms of green-up and senescence. The rapidly-growing PhenoCam network has been using in situ digital repeat photography to study phenology in almost 500 locations around the world, with an emphasis on North America. However, extracting time series data from multiple years of half-hourly imagery - while each set of images may contain several regions of interest (ROI's), corresponding to different species or vegetation types - is not always straightforward. Large volumes of data require substantial processing time, and changes (either intentional or accidental) in camera field of view requires adjustment of ROI masks. Here, we introduce and present "DrawROI" as an interactive web-based application for imagery from PhenoCam. DrawROI can also be used offline, as a fully independent toolkit that significantly facilitates extraction of phenological data from any stack of digital repeat photography images. DrawROI provides a responsive environment for phenological scientists to interactively a) delineate ROIs, b) handle field of view (FOV) shifts, and c) extract and export time series data characterizing image color (i.e. red, green and blue channel digital numbers for the defined ROI). The application utilizes artificial intelligence and advanced machine learning techniques and gives user the opportunity to redraw new ROIs every time an FOV shift occurs. DrawROI also offers a quality control flag to indicate noisy data and images with low quality due to presence of foggy weather or snow conditions. The web-based application significantly accelerates the process of creating new ROIs and modifying pre-existing ROI in the PhenoCam database. The offline toolkit is presented as an open source R-package that can be used with similar datasets with time-lapse photography to obtain more data for studying phenology for a large community of ecologists. We will illustrate the use of the toolkit using imagery from a selection of sites within the National Ecological Observatory Network (NEON).

A new method for tracking organ motion on diagnostic ultrasound images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kubota, Yoshiki, E-mail: y-kubota@gunma-u.ac.jp; Matsumura, Akihiko, E-mail: matchan.akihiko@gunma-u.ac.jp; Fukahori, Mai, E-mail: fukahori@nirs.go.jp

2014-09-15

Purpose: Respiratory-gated irradiation is effective in reducing the margins of a target in the case of abdominal organs, such as the liver, that change their position as a result of respiratory motion. However, existing technologies are incapable of directly measuring organ motion in real-time during radiation beam delivery. Hence, the authors proposed a novel quantitative organ motion tracking method involving the use of diagnostic ultrasound images; it is noninvasive and does not entail radiation exposure. In the present study, the authors have prospectively evaluated this proposed method. Methods: The method involved real-time processing of clinical ultrasound imaging data rather thanmore » organ monitoring; it comprised a three-dimensional ultrasound device, a respiratory sensing system, and two PCs for data storage and analysis. The study was designed to evaluate the effectiveness of the proposed method by tracking the gallbladder in one subject and a liver vein in another subject. To track a moving target organ, the method involved the control of a region of interest (ROI) that delineated the target. A tracking algorithm was used to control the ROI, and a large number of feature points and an error correction algorithm were used to achieve long-term tracking of the target. Tracking accuracy was assessed in terms of how well the ROI matched the center of the target. Results: The effectiveness of using a large number of feature points and the error correction algorithm in the proposed method was verified by comparing it with two simple tracking methods. The ROI could capture the center of the target for about 5 min in a cross-sectional image with changing position. Indeed, using the proposed method, it was possible to accurately track a target with a center deviation of 1.54 ± 0.9 mm. The computing time for one frame image using our proposed method was 8 ms. It is expected that it would be possible to track any soft-tissue organ or tumor with large deformations and changing cross-sectional position using this method. Conclusions: The proposed method achieved real-time processing and continuous tracking of the target organ for about 5 min. It is expected that our method will enable more accurate radiation treatment than is the case using indirect observational methods, such as the respiratory sensor method, because of direct visualization of the tumor. Results show that this tracking system facilitates safe treatment in clinical practice.« less

Return on investment of advanced practice medical degrees: NPs vs. PAs.

PubMed

Craig, Christopher K; Holmes, James H; Carter, Jeffery E

2017-06-01

As the United States faces a predicted physician shortage over the next 2 decades, physician assistants (PAs) and NPs are expected to fill the void. At the same time, because education is expensive, student loan and tuition increases have many potential applicants assessing differences in reimbursement and wondering about their return on investment (ROI). An analysis compared PA and NP salaries by incorporating national salary data, federal income tax, and student loans for a comparative analysis of each career pathway. Salaries were abstracted from the 2012 Bureau of Labor Statistics database. The net present value (NPV) of PA and NP salaries was calculated with a 5% discount rate. Principal and interest for student loans was calculated at a 6% interest fixed-rate loan over 30 years. NPVs were then compared with projected ROI at retirement age. Relative career values were also given to each career choice, based on a retirement age of 65 years, which translates to about 41 years of employment for both PAs and NPs. PAs' and NPs' educational loans both equalled $129,484 on total repayment. The median annual salary of a PA was $90,930 and $89,960 for an NP. PA data yielded a 5% NPV of $781,323 compared with $764,348 for NPs. Of note, the 5% NPV of a 4-year nursing degree is $728,436. PAs have a slightly higher ROI compared with NPs. These findings may change due to adjustments in nursing training models. Many PA programs allow matriculation immediately after obtaining a bachelor's degree. NP schools often require nursing experience before entering their program. Some schools are considering an accelerated NP program, allowing immediate matriculation after obtaining a bachelor's degree. Because many NP programs have become doctoral degrees, the increased duration of training, higher tuition, and fewer years worked before retirement lower the overall NP ROI. A similar reduction in ROI was considered marginal in PAs who attend residency programs-though these programs are not required for PAs to practice. Comparison of an RN with a 4-year degree to an NP shows little increase in ROI. If interest rates rise, it will become fiscally preferable to remain in a nursing position. Other intangible qualities exist and need further research (for example, weighing the financial aspects with lifestyle or professional satisfaction).

Histogram-based adaptive gray level scaling for texture feature classification of colorectal polyps

NASA Astrophysics Data System (ADS)

Pomeroy, Marc; Lu, Hongbing; Pickhardt, Perry J.; Liang, Zhengrong

2018-02-01

Texture features have played an ever increasing role in computer aided detection (CADe) and diagnosis (CADx) methods since their inception. Texture features are often used as a method of false positive reduction for CADe packages, especially for detecting colorectal polyps and distinguishing them from falsely tagged residual stool and healthy colon wall folds. While texture features have shown great success there, the performance of texture features for CADx have lagged behind primarily because of the more similar features among different polyps types. In this paper, we present an adaptive gray level scaling and compare it to the conventional equal-spacing of gray level bins. We use a dataset taken from computed tomography colonography patients, with 392 polyp regions of interest (ROIs) identified and have a confirmed diagnosis through pathology. Using the histogram information from the entire ROI dataset, we generate the gray level bins such that each bin contains roughly the same number of voxels Each image ROI is the scaled down to two different numbers of gray levels, using both an equal spacing of Hounsfield units for each bin, and our adaptive method. We compute a set of texture features from the scaled images including 30 gray level co-occurrence matrix (GLCM) features and 11 gray level run length matrix (GLRLM) features. Using a random forest classifier to distinguish between hyperplastic polyps and all others (adenomas and adenocarcinomas), we find that the adaptive gray level scaling can improve performance based on the area under the receiver operating characteristic curve by up to 4.6%.

Effect of Positioning of the ROI on BMD of the Forearm and Its Subregions.

PubMed

Rosen, Elizabeth O; McNamara, Elizabeth A; Whittaker, LaTarsha G; Malabanan, Alan O; Rosen, Harold N

2018-03-21

Inconsistent positioning of patients and region of interest (ROI) is known to influence the precision of bone mineral density (BMD) measurements in the spine and hip. However, it is unknown whether minor shifts in the positioning of the ROI along the shaft of the radius affect the measurement of forearm BMD and its subregions. The ultradistal (UD-), mid-, one-third, and total radius BMDs of 50 consecutive clinical densitometry patients were acquired. At baseline the distal end of the ROI was placed at the tip of the ulnar styloid as usual, and then the forearm was reanalyzed 10 more times, each time shifting the ROI 1 mm proximally. No corrections for multiple comparisons were necessary since the differences that were significant were significant at p < 0.001. The UD-radius BMD increased as the ROI was shifted proximally; the increase was significant when shifted even 1 mm proximally (p < 0.001). These same findings held true for the mid- and total radius bone density, though the percent increase with moving proximally was significantly greater for the UD radius than for the other subregions. However, there was no significant change in the one-third radius BMD when shifted proximally 1-10 mm. Minor proximal shifts of the forearm ROI substantially affect the BMD of the UD-, mid- and total radius, while having no effect on the one-third radius BMD. Since the one-third radius is the only forearm region usually reported, minor proximal shifts of the ROI should not influence forearm BMD results significantly. Copyright © 2018 The International Society for Clinical Densitometry. Published by Elsevier Inc. All rights reserved.

Generating region proposals for histopathological whole slide image retrieval.

PubMed

Ma, Yibing; Jiang, Zhiguo; Zhang, Haopeng; Xie, Fengying; Zheng, Yushan; Shi, Huaqiang; Zhao, Yu; Shi, Jun

2018-06-01

Content-based image retrieval is an effective method for histopathological image analysis. However, given a database of huge whole slide images (WSIs), acquiring appropriate region-of-interests (ROIs) for training is significant and difficult. Moreover, histopathological images can only be annotated by pathologists, resulting in the lack of labeling information. Therefore, it is an important and challenging task to generate ROIs from WSI and retrieve image with few labels. This paper presents a novel unsupervised region proposing method for histopathological WSI based on Selective Search. Specifically, the WSI is over-segmented into regions which are hierarchically merged until the WSI becomes a single region. Nucleus-oriented similarity measures for region mergence and Nucleus-Cytoplasm color space for histopathological image are specially defined to generate accurate region proposals. Additionally, we propose a new semi-supervised hashing method for image retrieval. The semantic features of images are extracted with Latent Dirichlet Allocation and transformed into binary hashing codes with Supervised Hashing. The methods are tested on a large-scale multi-class database of breast histopathological WSIs. The results demonstrate that for one WSI, our region proposing method can generate 7.3 thousand contoured regions which fit well with 95.8% of the ROIs annotated by pathologists. The proposed hashing method can retrieve a query image among 136 thousand images in 0.29 s and reach precision of 91% with only 10% of images labeled. The unsupervised region proposing method can generate regions as predictions of lesions in histopathological WSI. The region proposals can also serve as the training samples to train machine-learning models for image retrieval. The proposed hashing method can achieve fast and precise image retrieval with small amount of labels. Furthermore, the proposed methods can be potentially applied in online computer-aided-diagnosis systems. Copyright © 2018 Elsevier B.V. All rights reserved.

Protection of Health Imagery by Region Based Lossless Reversible Watermarking Scheme

PubMed Central

Priya, R. Lakshmi; Sadasivam, V.

2015-01-01

Providing authentication and integrity in medical images is a problem and this work proposes a new blind fragile region based lossless reversible watermarking technique to improve trustworthiness of medical images. The proposed technique embeds the watermark using a reversible least significant bit embedding scheme. The scheme combines hashing, compression, and digital signature techniques to create a content dependent watermark making use of compressed region of interest (ROI) for recovery of ROI as reported in literature. The experiments were carried out to prove the performance of the scheme and its assessment reveals that ROI is extracted in an intact manner and PSNR values obtained lead to realization that the presented scheme offers greater protection for health imageries. PMID:26649328

Optimizing methods for linking cinematic features to fMRI data.

PubMed

Kauttonen, Janne; Hlushchuk, Yevhen; Tikka, Pia

2015-04-15

One of the challenges of naturalistic neurosciences using movie-viewing experiments is how to interpret observed brain activations in relation to the multiplicity of time-locked stimulus features. As previous studies have shown less inter-subject synchronization across viewers of random video footage than story-driven films, new methods need to be developed for analysis of less story-driven contents. To optimize the linkage between our fMRI data collected during viewing of a deliberately non-narrative silent film 'At Land' by Maya Deren (1944) and its annotated content, we combined the method of elastic-net regularization with the model-driven linear regression and the well-established data-driven independent component analysis (ICA) and inter-subject correlation (ISC) methods. In the linear regression analysis, both IC and region-of-interest (ROI) time-series were fitted with time-series of a total of 36 binary-valued and one real-valued tactile annotation of film features. The elastic-net regularization and cross-validation were applied in the ordinary least-squares linear regression in order to avoid over-fitting due to the multicollinearity of regressors, the results were compared against both the partial least-squares (PLS) regression and the un-regularized full-model regression. Non-parametric permutation testing scheme was applied to evaluate the statistical significance of regression. We found statistically significant correlation between the annotation model and 9 ICs out of 40 ICs. Regression analysis was also repeated for a large set of cubic ROIs covering the grey matter. Both IC- and ROI-based regression analyses revealed activations in parietal and occipital regions, with additional smaller clusters in the frontal lobe. Furthermore, we found elastic-net based regression more sensitive than PLS and un-regularized regression since it detected a larger number of significant ICs and ROIs. Along with the ISC ranking methods, our regression analysis proved a feasible method for ordering the ICs based on their functional relevance to the annotated cinematic features. The novelty of our method is - in comparison to the hypothesis-driven manual pre-selection and observation of some individual regressors biased by choice - in applying data-driven approach to all content features simultaneously. We found especially the combination of regularized regression and ICA useful when analyzing fMRI data obtained using non-narrative movie stimulus with a large set of complex and correlated features. Copyright © 2015. Published by Elsevier Inc.

Definition and visualisation of regions of interest in post-prostatectomy image-guided intensity modulated radiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bell, Linda J, E-mail: linda.bell1@health.nsw.gov.au; Cox, Jennifer; Faculty of Health Sciences, University of Sydney, Lidcombe, New South Wales

2014-09-15

Standard post-prostatectomy radiotherapy (PPRT) image verification uses bony anatomy alignment. However, the prostate bed (PB) moves independently of bony anatomy. Cone beam computed tomography (CBCT) can be used to soft tissue match, so radiation therapists (RTs) must understand pelvic anatomy and PPRT clinical target volumes (CTV). The aims of this study are to define regions of interest (ROI) to be used in soft tissue matching image guidance and determine their visibility on planning CT (PCT) and CBCT. Published CTV guidelines were used to select ROIs. The PCT scans (n = 23) and CBCT scans (n = 105) of 23 post-prostatectomymore » patients were reviewed. Details on ROI identification were recorded. Eighteen patients had surgical clips. All ROIs were identified on PCTs at least 90% of the time apart from mesorectal fascia (MF) (87%) due to superior image quality. When surgical clips are present, the seminal vesicle bed (SVB) was only seen in 2.3% of CBCTs and MF was unidentifiable. Most other structures were well identified on CBCT. The anterior rectal wall (ARW) was identified in 81.4% of images and penile bulb (PB) in 68.6%. In the absence of surgical clips, the MF and SVB were always identified; the ARW was identified in 89.5% of CBCTs and PB in 73.7%. Surgical clips should be used as ROIs when present to define SVB and MF. In the absence of clips, SVB, MF and ARW can be used. RTs must have a strong knowledge of soft tissue anatomy and PPRT CTV to ensure coverage and enable soft tissue matching.« less

Functional connectivity and structural covariance between regions of interest can be measured more accurately using multivariate distance correlation.

PubMed

Geerligs, Linda; Cam-Can; Henson, Richard N

2016-07-15

Studies of brain-wide functional connectivity or structural covariance typically use measures like the Pearson correlation coefficient, applied to data that have been averaged across voxels within regions of interest (ROIs). However, averaging across voxels may result in biased connectivity estimates when there is inhomogeneity within those ROIs, e.g., sub-regions that exhibit different patterns of functional connectivity or structural covariance. Here, we propose a new measure based on "distance correlation"; a test of multivariate dependence of high dimensional vectors, which allows for both linear and non-linear dependencies. We used simulations to show how distance correlation out-performs Pearson correlation in the face of inhomogeneous ROIs. To evaluate this new measure on real data, we use resting-state fMRI scans and T1 structural scans from 2 sessions on each of 214 participants from the Cambridge Centre for Ageing & Neuroscience (Cam-CAN) project. Pearson correlation and distance correlation showed similar average connectivity patterns, for both functional connectivity and structural covariance. Nevertheless, distance correlation was shown to be 1) more reliable across sessions, 2) more similar across participants, and 3) more robust to different sets of ROIs. Moreover, we found that the similarity between functional connectivity and structural covariance estimates was higher for distance correlation compared to Pearson correlation. We also explored the relative effects of different preprocessing options and motion artefacts on functional connectivity. Because distance correlation is easy to implement and fast to compute, it is a promising alternative to Pearson correlations for investigating ROI-based brain-wide connectivity patterns, for functional as well as structural data. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Features of the normal choriocapillaris with OCT-angiography: Density estimation and textural properties.

PubMed

Montesano, Giovanni; Allegrini, Davide; Colombo, Leonardo; Rossetti, Luca M; Pece, Alfredo

2017-01-01

The main objective of our work is to perform an in depth analysis of the structural features of normal choriocapillaris imaged with OCT Angiography. Specifically, we provide an optimal radius for a circular Region of Interest (ROI) to obtain a stable estimate of the subfoveal choriocapillaris density and characterize its textural properties using Markov Random Fields. On each binarized image of the choriocapillaris OCT Angiography we performed simulated measurements of the subfoveal choriocapillaris densities with circular Regions of Interest (ROIs) of different radii and with small random displacements from the center of the Foveal Avascular Zone (FAZ). We then calculated the variability of the density measure with different ROI radii. We then characterized the textural features of choriocapillaris binary images by estimating the parameters of an Ising model. For each image we calculated the Optimal Radius (OR) as the minimum ROI radius required to obtain a standard deviation in the simulation below 0.01. The density measured with the individual OR was 0.52 ± 0.07 (mean ± STD). Similar density values (0.51 ± 0.07) were obtained using a fixed ROI radius of 450 μm. The Ising model yielded two parameter estimates (β = 0.34 ± 0.03; γ = 0.003 ± 0.012; mean ± STD), characterizing pixel clustering and white pixel density respectively. Using the estimated parameters to synthetize new random textures via simulation we obtained a good reproduction of the original choriocapillaris structural features and density. In conclusion, we developed an extensive characterization of the normal subfoveal choriocapillaris that might be used for flow analysis and applied to the investigation pathological alterations.

DBSCAN-based ROI extracted from SAR images and the discrimination of multi-feature ROI

NASA Astrophysics Data System (ADS)

He, Xin Yi; Zhao, Bo; Tan, Shu Run; Zhou, Xiao Yang; Jiang, Zhong Jin; Cui, Tie Jun

2009-10-01

The purpose of the paper is to extract the region of interest (ROI) from the coarse detected synthetic aperture radar (SAR) images and discriminate if the ROI contains a target or not, so as to eliminate the false alarm, and prepare for the target recognition. The automatic target clustering is one of the most difficult tasks in the SAR-image automatic target recognition system. The density-based spatial clustering of applications with noise (DBSCAN) relies on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN was first used in the SAR image processing, which has many excellent features: only two insensitivity parameters (radius of neighborhood and minimum number of points) are needed; clusters of arbitrary shapes which fit in with the coarse detected SAR images can be discovered; and the calculation time and memory can be reduced. In the multi-feature ROI discrimination scheme, we extract several target features which contain the geometry features such as the area discriminator and Radon-transform based target profile discriminator, the distribution characteristics such as the EFF discriminator, and the EM scattering property such as the PPR discriminator. The synthesized judgment effectively eliminates the false alarms.

An automatic target recognition system based on SAR image

NASA Astrophysics Data System (ADS)

Li, Qinfu; Wang, Jinquan; Zhao, Bo; Luo, Furen; Xu, Xiaojian

2009-10-01

In this paper, an automatic target recognition (ATR) system based on synthetic aperture radar (SAR) is proposed. This ATR system can play an important role in the simulation of up-to-data battlefield environment and be used in ATR research. To establish an integral and available system, the processing of SAR image was divided into four main stages which are de-noise, detection, cluster-discrimination and segment-recognition, respectively. The first three stages are used for searching region of interest (ROI). Once the ROIs are extracted, the recognition stage will be taken to compute the similarity between the ROIs and the templates in the electromagnetic simulation software National Electromagnetic Scattering Code (NESC). Due to the lack of the SAR raw data, the electromagnetic simulated images are added to the measured SAR background to simulate the battlefield environment8. The purpose of the system is to find the ROIs which can be the artificial military targets such as tanks, armored cars and so on and to categorize the ROIs into the right classes according to the existing templates. From the results we can see that the proposed system achieves a satisfactory result.

Spectral characterisation and noise performance of Vanilla—an active pixel sensor

NASA Astrophysics Data System (ADS)

Blue, Andrew; Bates, R.; Bohndiek, S. E.; Clark, A.; Arvanitis, Costas D.; Greenshaw, T.; Laing, A.; Maneuski, D.; Turchetta, R.; O'Shea, V.

2008-06-01

This work will report on the characterisation of a new active pixel sensor, Vanilla. The Vanilla comprises of 512×512 (25μm 2) pixels. The sensor has a 12 bit digital output for full-frame mode, although it can also be readout in analogue mode, whereby it can also be read in a fully programmable region-of-interest (ROI) mode. In full frame, the sensor can operate at a readout rate of more than 100 frames per second (fps), while in ROI mode, the speed depends on the size, shape and number of ROIs. For example, an ROI of 6×6 pixels can be read at 20,000 fps in analogue mode. Using photon transfer curve (PTC) measurements allowed for the calculation of the read noise, shot noise, full-well capacity and camera gain constant of the sensor. Spectral response measurements detailed the quantum efficiency (QE) of the detector through the UV and visible region. Analysis of the ROI readout mode was also performed. Such measurements suggest that the Vanilla APS (active pixel sensor) will be suitable for a wide range of applications including particle physics and medical imaging.

Accurate measurement of imaging photoplethysmographic signals based camera using weighted average

NASA Astrophysics Data System (ADS)

Pang, Zongguang; Kong, Lingqin; Zhao, Yuejin; Sun, Huijuan; Dong, Liquan; Hui, Mei; Liu, Ming; Liu, Xiaohua; Liu, Lingling; Li, Xiaohui; Li, Rongji

2018-01-01

Imaging Photoplethysmography (IPPG) is an emerging technique for the extraction of vital signs of human being using video recordings. IPPG technology with its advantages like non-contact measurement, low cost and easy operation has become one research hot spot in the field of biomedicine. However, the noise disturbance caused by non-microarterial area cannot be removed because of the uneven distribution of micro-arterial, different signal strength of each region, which results in a low signal noise ratio of IPPG signals and low accuracy of heart rate. In this paper, we propose a method of improving the signal noise ratio of camera-based IPPG signals of each sub-region of the face using a weighted average. Firstly, we obtain the region of interest (ROI) of a subject's face based camera. Secondly, each region of interest is tracked and feature-based matched in each frame of the video. Each tracked region of face is divided into 60x60 pixel block. Thirdly, the weights of PPG signal of each sub-region are calculated, based on the signal-to-noise ratio of each sub-region. Finally, we combine the IPPG signal from all the tracked ROI using weighted average. Compared with the existing approaches, the result shows that the proposed method takes modest but significant effects on improvement of signal noise ratio of camera-based PPG estimated and accuracy of heart rate measurement.

Stereo Imaging Miniature Endoscope with Single Imaging Chip and Conjugated Multi-Bandpass Filters

NASA Technical Reports Server (NTRS)

Shahinian, Hrayr Karnig (Inventor); Bae, Youngsam (Inventor); White, Victor E. (Inventor); Shcheglov, Kirill V. (Inventor); Manohara, Harish M. (Inventor); Kowalczyk, Robert S. (Inventor)

2018-01-01

A dual objective endoscope for insertion into a cavity of a body for providing a stereoscopic image of a region of interest inside of the body including an imaging device at the distal end for obtaining optical images of the region of interest (ROI), and processing the optical images for forming video signals for wired and/or wireless transmission and display of 3D images on a rendering device. The imaging device includes a focal plane detector array (FPA) for obtaining the optical images of the ROI, and processing circuits behind the FPA. The processing circuits convert the optical images into the video signals. The imaging device includes right and left pupil for receiving a right and left images through a right and left conjugated multi-band pass filters. Illuminators illuminate the ROI through a multi-band pass filter having three right and three left pass bands that are matched to the right and left conjugated multi-band pass filters. A full color image is collected after three or six sequential illuminations with the red, green and blue lights.

Computerized Classification of Pneumoconiosis on Digital Chest Radiography Artificial Neural Network with Three Stages.

PubMed

Okumura, Eiichiro; Kawashita, Ikuo; Ishida, Takayuki

2017-08-01

It is difficult for radiologists to classify pneumoconiosis from category 0 to category 3 on chest radiographs. Therefore, we have developed a computer-aided diagnosis (CAD) system based on a three-stage artificial neural network (ANN) method for classification based on four texture features. The image database consists of 36 chest radiographs classified as category 0 to category 3. Regions of interest (ROIs) with a matrix size of 32 × 32 were selected from chest radiographs. We obtained a gray-level histogram, histogram of gray-level difference, gray-level run-length matrix (GLRLM) feature image, and gray-level co-occurrence matrix (GLCOM) feature image in each ROI. For ROI-based classification, the first ANN was trained with each texture feature. Next, the second ANN was trained with output patterns obtained from the first ANN. Finally, we obtained a case-based classification for distinguishing among four categories with the third ANN method. We determined the performance of the third ANN by receiver operating characteristic (ROC) analysis. The areas under the ROC curve (AUC) of the highest category (severe pneumoconiosis) case and the lowest category (early pneumoconiosis) case were 0.89 ± 0.09 and 0.84 ± 0.12, respectively. The three-stage ANN with four texture features showed the highest performance for classification among the four categories. Our CAD system would be useful for assisting radiologists in classification of pneumoconiosis from category 0 to category 3.

Differential diagnosis of benign and malignant breast masses using diffusion-weighted magnetic resonance imaging.

PubMed

Min, Qinghua; Shao, Kangwei; Zhai, Lulan; Liu, Wei; Zhu, Caisong; Yuan, Lixin; Yang, Jun

2015-02-07

Diffusion-weighted magnetic resonance imaging (DW-MRI) is different from conventional diagnostic methods and has the potential to delineate the microscopic anatomy of a target tissue or organ. The purpose of our study was to evaluate the value of DW-MRI in the diagnosis of benign and malignant breast masses, which would help the clinical surgeon to decide the scope and pattern of operation. A total of 52 female patients with palpable solid breast masses received breast MRI scans using routine sequences, dynamic contrast-enhanced imaging, and diffusion-weighted echo-planar imaging at b values of 400, 600, and 800 s/mm(2), respectively. Two regions of interest (ROIs) were plotted, with a smaller ROI for the highest signal and a larger ROI for the overall lesion. Apparent diffusion coefficient (ADC) values were calculated at three different b values for all detectable lesions and from two different ROIs. The sensitivity, specificity, positive predictive value, and positive likelihood ratio of DW-MRI were determined for comparison with histological results. A total of 49 (49/52, 94.2%) lesions were detected using DW-MRI, including 20 benign lesions (two lesions detected in the same patient) and 29 malignant lesions. Benign lesion had a higher mean ADC value than their malignant counterparts, regardless of b value. According to the receiver operating characteristic (ROC) curve, the smaller-range ROI was more effective in differentiation between benign and malignant lesions. The area under the ROC curve was the largest at a b value of 800 s/mm(2). With a threshold ADC value at 1.23 × 10(-3) mm(2)/s, DW-MRI achieved a sensitivity of 82.8%, specificity of 90.0%, positive predictive value of 92.3%, and positive likelihood ratio of 8.3 for differentiating benign and malignant lesions. DW-MRI is an accurate diagnostic tool for differentiation between benign and malignant breast lesions, with an optimal b value of 800 s/mm(2). A smaller-range ROI focusing on the highest signal has a better differential value.

Automated flaw detection scheme for cast austenitic stainless steel weld specimens using Hilbert-Huang transform of ultrasonic phased array data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Khan, Tariq; Majumdar, Shantanu; Udpa, Lalita

2012-05-17

The objective of this work is to develop processing algorithms to detect and localize flaws using ultrasonic phased-array data. Data was collected on cast austenitic stainless stell (CASS) weld specimens onloan from the U.S. nuclear power industry' Pressurized Walter Reactor Owners Group (PWROG) traveling specimen set. Each specimen consists of a centrifugally cast stainless stell (CCSS) pipe section welded to a statically cst(SCSS) or wrought (WRSS) section. The paper presents a novel automated flaw detection and localization scheme using low frequency ultrasonic phased array inspection singals from the weld and heat affected zone of the based materials. The major stepsmore » of the overall scheme are preprocessing and region of interest (ROI) detection followed by the Hilbert-Huang transform (HHT) of A-scans in the detected ROIs. HHT offers time-frequency-energy distribution for each ROI. The Accumulation of energy in a particular frequency band is used as a classification feature for the particular ROI.« less

Gender Classification Based on Eye Movements: A Processing Effect During Passive Face Viewing

PubMed Central

Sammaknejad, Negar; Pouretemad, Hamidreza; Eslahchi, Changiz; Salahirad, Alireza; Alinejad, Ashkan

2017-01-01

Studies have revealed superior face recognition skills in females, partially due to their different eye movement strategies when encoding faces. In the current study, we utilized these slight but important differences and proposed a model that estimates the gender of the viewers and classifies them into two subgroups, males and females. An eye tracker recorded participant’s eye movements while they viewed images of faces. Regions of interest (ROIs) were defined for each face. Results showed that the gender dissimilarity in eye movements was not due to differences in frequency of fixations in the ROI s per se. Instead, it was caused by dissimilarity in saccade paths between the ROIs. The difference enhanced when saccades were towards the eyes. Females showed significant increase in transitions from other ROI s to the eyes. Consequently, the extraction of temporal transient information of saccade paths through a transition probability matrix, similar to a first order Markov chain model, significantly improved the accuracy of the gender classification results. PMID:29071007

Gender Classification Based on Eye Movements: A Processing Effect During Passive Face Viewing.

PubMed

Sammaknejad, Negar; Pouretemad, Hamidreza; Eslahchi, Changiz; Salahirad, Alireza; Alinejad, Ashkan

2017-01-01

Studies have revealed superior face recognition skills in females, partially due to their different eye movement strategies when encoding faces. In the current study, we utilized these slight but important differences and proposed a model that estimates the gender of the viewers and classifies them into two subgroups, males and females. An eye tracker recorded participant's eye movements while they viewed images of faces. Regions of interest (ROIs) were defined for each face. Results showed that the gender dissimilarity in eye movements was not due to differences in frequency of fixations in the ROI s per se. Instead, it was caused by dissimilarity in saccade paths between the ROIs. The difference enhanced when saccades were towards the eyes. Females showed significant increase in transitions from other ROI s to the eyes. Consequently, the extraction of temporal transient information of saccade paths through a transition probability matrix, similar to a first order Markov chain model, significantly improved the accuracy of the gender classification results.

Identifying minimal hepatic encephalopathy in cirrhotic patients by measuring spontaneous brain activity.

PubMed

Chen, Hua-Jun; Zhang, Ling; Jiang, Long-Feng; Chen, Qiu-Feng; Li, Jun; Shi, Hai-Bin

2016-08-01

It has been demonstrated that minimal hepatic encephalopathy (MHE) is associated with aberrant regional intrinsic brain activity in cirrhotic patients. However, few studies have investigated whether altered intrinsic brain activity can be used as a biomarker of MHE among cirrhotic patients. In this study, 36 cirrhotic patients (with MHE, n = 16; without MHE [NHE], n = 20) underwent resting-state functional magnetic resonance imaging (fMRI). Spontaneous brain activity was measured by examining the amplitude of low-frequency fluctuations (ALFF) in the fMRI signal. MHE was diagnosed based on the Psychometric Hepatic Encephalopathy Score (PHES). A two-sample t-test was used to determine the regions of interest (ROIs) in which ALFF differed significantly between the two groups; then, ALFF values within ROIs were selected as classification features. A linear discriminative analysis was used to differentiate MHE patients from NHE patients. The leave-one-out cross-validation method was used to estimate the performance of the classifier. The classification analysis was 80.6 % accurate (81.3 % sensitivity and 80.0 % specificity) in terms of distinguishing between the two groups. Six ROIs were identified as the most discriminative features, including the bilateral medial frontal cortex/anterior cingulate cortex, posterior cingulate cortex/precuneus, left precentral and postcentral gyrus, right lingual gyrus, middle frontal gyrus, and inferior/superior parietal lobule. The ALFF values within ROIs were correlated with PHES in cirrhotic patients. Our findings suggest that altered regional brain spontaneous activity is a useful biomarker for MHE detection among cirrhotic patients.

Piecewise-Constant-Model-Based Interior Tomography Applied to Dentin Tubules

DOE PAGES

He, Peng; Wei, Biao; Wang, Steve; ...

2013-01-01

Dentin is a hierarchically structured biomineralized composite material, and dentin’s tubules are difficult to study in situ. Nano-CT provides the requisite resolution, but the field of view typically contains only a few tubules. Using a plate-like specimen allows reconstruction of a volume containing specific tubules from a number of truncated projections typically collected over an angular range of about 140°, which is practically accessible. Classical computed tomography (CT) theory cannot exactly reconstruct an object only from truncated projections, needless to say a limited angular range. Recently, interior tomography was developed to reconstruct a region-of-interest (ROI) from truncated data in amore » theoretically exact fashion via the total variation (TV) minimization under the condition that the ROI is piecewise constant. In this paper, we employ a TV minimization interior tomography algorithm to reconstruct interior microstructures in dentin from truncated projections over a limited angular range. Compared to the filtered backprojection (FBP) reconstruction, our reconstruction method reduces noise and suppresses artifacts. Volume rendering confirms the merits of our method in terms of preserving the interior microstructure of the dentin specimen.« less

3D High Resolution Mesh Deformation Based on Multi Library Wavelet Neural Network Architecture

NASA Astrophysics Data System (ADS)

Dhibi, Naziha; Elkefi, Akram; Bellil, Wajdi; Amar, Chokri Ben

2016-12-01

This paper deals with the features of a novel technique for large Laplacian boundary deformations using estimated rotations. The proposed method is based on a Multi Library Wavelet Neural Network structure founded on several mother wavelet families (MLWNN). The objective is to align features of mesh and minimize distortion with a fixed feature that minimizes the sum of the distances between all corresponding vertices. New mesh deformation method worked in the domain of Region of Interest (ROI). Our approach computes deformed ROI, updates and optimizes it to align features of mesh based on MLWNN and spherical parameterization configuration. This structure has the advantage of constructing the network by several mother wavelets to solve high dimensions problem using the best wavelet mother that models the signal better. The simulation test achieved the robustness and speed considerations when developing deformation methodologies. The Mean-Square Error and the ratio of deformation are low compared to other works from the state of the art. Our approach minimizes distortions with fixed features to have a well reconstructed object.

Landmark-based deep multi-instance learning for brain disease diagnosis.

PubMed

Liu, Mingxia; Zhang, Jun; Adeli, Ehsan; Shen, Dinggang

2018-01-01

In conventional Magnetic Resonance (MR) image based methods, two stages are often involved to capture brain structural information for disease diagnosis, i.e., 1) manually partitioning each MR image into a number of regions-of-interest (ROIs), and 2) extracting pre-defined features from each ROI for diagnosis with a certain classifier. However, these pre-defined features often limit the performance of the diagnosis, due to challenges in 1) defining the ROIs and 2) extracting effective disease-related features. In this paper, we propose a landmark-based deep multi-instance learning (LDMIL) framework for brain disease diagnosis. Specifically, we first adopt a data-driven learning approach to discover disease-related anatomical landmarks in the brain MR images, along with their nearby image patches. Then, our LDMIL framework learns an end-to-end MR image classifier for capturing both the local structural information conveyed by image patches located by landmarks and the global structural information derived from all detected landmarks. We have evaluated our proposed framework on 1526 subjects from three public datasets (i.e., ADNI-1, ADNI-2, and MIRIAD), and the experimental results show that our framework can achieve superior performance over state-of-the-art approaches. Copyright © 2017 Elsevier B.V. All rights reserved.

Comparative performance analysis of cervix ROI extraction and specular reflection removal algorithms for uterine cervix image analysis

NASA Astrophysics Data System (ADS)

Xue, Zhiyun; Antani, Sameer; Long, L. Rodney; Jeronimo, Jose; Thoma, George R.

2007-03-01

Cervicography is a technique for visual screening of uterine cervix images for cervical cancer. One of our research goals is the automated detection in these images of acetowhite (AW) lesions, which are sometimes correlated with cervical cancer. These lesions are characterized by the whitening of regions along the squamocolumnar junction on the cervix when treated with 5% acetic acid. Image preprocessing is required prior to invoking AW detection algorithms on cervicographic images for two reasons: (1) to remove Specular Reflections (SR) caused by camera flash, and (2) to isolate the cervix region-of-interest (ROI) from image regions that are irrelevant to the analysis. These image regions may contain medical instruments, film markup, or other non-cervix anatomy or regions, such as vaginal walls. We have qualitatively and quantitatively evaluated the performance of alternative preprocessing algorithms on a test set of 120 images. For cervix ROI detection, all approaches use a common feature set, but with varying combinations of feature weights, normalization, and clustering methods. For SR detection, while one approach uses a Gaussian Mixture Model on an intensity/saturation feature set, a second approach uses Otsu thresholding on a top-hat transformed input image. Empirical results are analyzed to derive conclusions on the performance of each approach.

DWI-based neural fingerprinting technology: a preliminary study on stroke analysis.

PubMed

Ye, Chenfei; Ma, Heather Ting; Wu, Jun; Yang, Pengfei; Chen, Xuhui; Yang, Zhengyi; Ma, Jingbo

2014-01-01

Stroke is a common neural disorder in neurology clinics. Magnetic resonance imaging (MRI) has become an important tool to assess the neural physiological changes under stroke, such as diffusion weighted imaging (DWI) and diffusion tensor imaging (DTI). Quantitative analysis of MRI images would help medical doctors to localize the stroke area in the diagnosis in terms of structural information and physiological characterization. However, current quantitative approaches can only provide localization of the disorder rather than measure physiological variation of subtypes of ischemic stroke. In the current study, we hypothesize that each kind of neural disorder would have its unique physiological characteristics, which could be reflected by DWI images on different gradients. Based on this hypothesis, a DWI-based neural fingerprinting technology was proposed to classify subtypes of ischemic stroke. The neural fingerprint was constructed by the signal intensity of the region of interest (ROI) on the DWI images under different gradients. The fingerprint derived from the manually drawn ROI could classify the subtypes with accuracy 100%. However, the classification accuracy was worse when using semiautomatic and automatic method in ROI segmentation. The preliminary results showed promising potential of DWI-based neural fingerprinting technology in stroke subtype classification. Further studies will be carried out for enhancing the fingerprinting accuracy and its application in other clinical practices.

Simple method for self-referenced and lable-free biosensing by using a capillary sensing element.

PubMed

Liu, Yun; Chen, Shimeng; Liu, Qiang; Liu, Zigeng; Wei, Peng

2017-05-15

We demonstrated a simple method for self-reference and label free biosensing based on a capillary sensing element and common optoelectronic devices. The capillary sensing element is illuminated by a light-emitting diode (LED) light source and detected by a webcam. Part of gold film that deposited on the tubing wall is functionalized to carry on the biological information in the excited SPR modes. The end face of the capillary was monitored and separate regions of interest (ROIs) were selected as the measurement channel and the reference channel. In the ROIs, the biological information can be accurately extracted from the image by simple image processing. Moreover, temperature fluctuation, bulk RI fluctuation, light source fluctuation and other factors can be effectively compensated during detection. Our biosensing device has a sensitivity of 1145%/RIU and a resolution better than 5.287 × 10 -4 RIU, considering a 0.79% noise level. We apply it for concanavalin A (Con A) biological measurement, which has an approximately linear response to the specific analyte concentration. This simple method provides a new approach for multichannel SPR sensing and reference-compensated calibration of SPR signal for label-free detection.

Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network.

PubMed

Zafar, Raheel; Kamel, Nidal; Naufal, Mohamad; Malik, Aamir Saeed; Dass, Sarat C; Ahmad, Rana Fayyaz; Abdullah, Jafri M; Reza, Faruque

2017-01-01

Decoding of human brain activity has always been a primary goal in neuroscience especially with functional magnetic resonance imaging (fMRI) data. In recent years, Convolutional neural network (CNN) has become a popular method for the extraction of features due to its higher accuracy, however it needs a lot of computation and training data. In this study, an algorithm is developed using Multivariate pattern analysis (MVPA) and modified CNN to decode the behavior of brain for different images with limited data set. Selection of significant features is an important part of fMRI data analysis, since it reduces the computational burden and improves the prediction performance; significant features are selected using t-test. MVPA uses machine learning algorithms to classify different brain states and helps in prediction during the task. General linear model (GLM) is used to find the unknown parameters of every individual voxel and the classification is done using multi-class support vector machine (SVM). MVPA-CNN based proposed algorithm is compared with region of interest (ROI) based method and MVPA based estimated values. The proposed method showed better overall accuracy (68.6%) compared to ROI (61.88%) and estimation values (64.17%).

Data-driven regions of interest for longitudinal change in frontotemporal lobar degeneration.

PubMed

Pankov, Aleksandr; Binney, Richard J; Staffaroni, Adam M; Kornak, John; Attygalle, Suneth; Schuff, Norbert; Weiner, Michael W; Kramer, Joel H; Dickerson, Bradford C; Miller, Bruce L; Rosen, Howard J

2016-01-01

Current research is investigating the potential utility of longitudinal measurement of brain structure as a marker of drug effect in clinical trials for neurodegenerative disease. Recent studies in Alzheimer's disease (AD) have shown that measurement of change in empirically derived regions of interest (ROIs) allows more reliable measurement of change over time compared with regions chosen a-priori based on known effects of AD on brain anatomy. Frontotemporal lobar degeneration (FTLD) is a devastating neurodegenerative disorder for which there are no approved treatments. The goal of this study was to identify an empirical ROI that maximizes the effect size for the annual rate of brain atrophy in FTLD compared with healthy age matched controls, and to estimate the effect size and associated power estimates for a theoretical study that would use change within this ROI as an outcome measure. Eighty six patients with FTLD were studied, including 43 who were imaged twice at 1.5 T and 43 at 3 T, along with 105 controls (37 imaged at 1.5 T and 67 at 3 T). Empirically-derived maps of change were generated separately for each field strength and included the bilateral insula, dorsolateral, medial and orbital frontal, basal ganglia and lateral and inferior temporal regions. The extent of regions included in the 3 T map was larger than that in the 1.5 T map. At both field strengths, the effect sizes for imaging were larger than for any clinical measures. At 3 T, the effect size for longitudinal change measured within the empirically derived ROI was larger than the effect sizes derived from frontal lobe, temporal lobe or whole brain ROIs. The effect size derived from the data-driven 1.5 T map was smaller than at 3 T, and was not larger than the effect size derived from a-priori ROIs. It was estimated that measurement of longitudinal change using 1.5 T MR systems requires approximately a 3-fold increase in sample size to obtain effect sizes equivalent to those seen at 3 T. While the results should be confirmed in additional datasets, these results indicate that empirically derived ROIs can reduce the number of subjects needed for a longitudinal study of drug effects in FTLD compared with a-priori ROIs. Field strength may have a significant impact on the utility of imaging for measuring longitudinal change.

SU-E-J-168: Automated Pancreas Segmentation Based On Dynamic MRI

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gou, S; Rapacchi, S; Hu, P

2014-06-01

Purpose: MRI guided radiotherapy is particularly attractive for abdominal targets with low CT contrast. To fully utilize this modality for pancreas tracking, automated segmentation tools are needed. A hybrid gradient, region growth and shape constraint (hGReS) method to segment 2D upper abdominal dynamic MRI is developed for this purpose. Methods: 2D coronal dynamic MR images of 2 healthy volunteers were acquired with a frame rate of 5 f/second. The regions of interest (ROIs) included the liver, pancreas and stomach. The first frame was used as the source where the centers of the ROIs were annotated. These center locations were propagatedmore » to the next dynamic MRI frame. 4-neighborhood region transfer growth was performed from these initial seeds for rough segmentation. To improve the results, gradient, edge and shape constraints were applied to the ROIs before final refinement using morphological operations. Results from hGReS and 3 other automated segmentation methods using edge detection, region growth and level set were compared to manual contouring. Results: For the first patient, hGReS resulted in the organ segmentation accuracy as measure by the Dices index (0.77) for the pancreas. The accuracy was slightly superior to the level set method (0.72), and both are significantly more accurate than the edge detection (0.53) and region growth methods (0.42). For the second healthy volunteer, hGReS reliably segmented the pancreatic region, achieving a Dices index of 0.82, 0.92 and 0.93 for the pancreas, stomach and liver, respectively, comparing to manual segmentation. Motion trajectories derived from the hGReS, level set and manual segmentation methods showed high correlation to respiratory motion calculated using a lung blood vessel as the reference while the other two methods showed substantial motion tracking errors. hGReS was 10 times faster than level set. Conclusion: We have shown the feasibility of automated segmentation of the pancreas anatomy based on dynamic MRI.« less

Intraoral radiographs texture analysis for dental implant planning.

PubMed

Mundim, Mayara B V; Dias, Danilo R; Costa, Ronaldo M; Leles, Cláudio R; Azevedo-Marques, Paulo M; Ribeiro-Rotta, Rejane F

2016-11-01

Computer vision extracts features or attributes from images improving diagnosis accuracy and aiding in clinical decisions. This study aims to investigate the feasibility of using texture analysis of periapical radiograph images as a tool for dental implant treatment planning. Periapical radiograph images of 127 jawbone sites were obtained before and after implant placement. From the superimposition of the pre- and post-implant images, four regions of interest (ROI) were delineated on the pre-implant images for each implant site: mesial, distal and apical peri-implant areas and a central area. Each ROI was analysed using Matlab® software and seven image attributes were extracted: mean grey level (MGL), standard deviation of grey levels (SDGL), coefficient of variation (CV), entropy (En), contrast, correlation (Cor) and angular second moment (ASM). Images were grouped by bone types-Lekholm and Zarb classification (1,2,3,4). Peak insertion torque (PIT) and resonance frequency analysis (RFA) were recorded during implant placement. Differences among groups were tested for each image attribute. Agreement between measurements of the peri-implant ROIs and overall ROI (peri-implant + central area) was tested, as well as the association between primary stability measures (PIT and RFA) and texture attributes. Differences among bone type groups were found for MGL (p = 0.035), SDGL (p = 0.024), CV (p < 0.001) and En (p < 0.001). The apical ROI showed a significant difference from the other regions for all attributes, except Cor. Concordance correlation coefficients were all almost perfect (ρ > 0.93), except for ASM (ρ = 0.62). Texture attributes were significantly associated with the implant stability measures. Texture analysis of periapical radiographs may be a reliable non-invasive quantitative method for the assessment of jawbone and prediction of implant stability, with potential clinical applications. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Role of Language-Related Functional Connectivity in Patients with Benign Childhood Epilepsy with Centrotemporal Spikes

PubMed Central

Kim, Hyeon Jin; Lee, Jung Hwa; Park, Chang-hyun; Hong, Hye-Sun; Choi, Yun Seo; Yoo, Jeong Hyun

2018-01-01

Background and Purpose Benign childhood epilepsy with centrotemporal spikes (BECTS) does not always have a benign cognitive outcome. We investigated the relationship between cognitive performance and altered functional connectivity (FC) in the resting-state brain networks of BECTS patients. Methods We studied 42 subjects, comprising 19 BECTS patients and 23 healthy controls. Cognitive performance was assessed using the Korean version of the Wechsler Intelligence Scale for Children-III, in addition to verbal and visuospatial memory tests and executive function tests. Resting-state functional magnetic resonance imaging was acquired in addition to high-resolution structural data. We selected Rolandic and language-related areas as regions of interest (ROIs) and analyzed the seed-based FC to voxels throughout the brain. We evaluated the correlations between the neuropsychological test scores and seed-based FC values using the same ROIs. Results The verbal intelligence quotient (VIQ) and full-scale intelligence quotient (FSIQ) were lower in BECTS patients than in healthy controls (p<0.001). The prevalence of subjects with a higher performance IQ than VIQ was significantly higher in BECTS patients than in healthy controls (73.7% vs. 26.1%, respectively; p=0.002). Both the Rolandic and language-related ROIs exhibited more enhanced FC to voxels in the left inferior temporal gyrus in BECTS patients than in healthy controls. A particularly interestingly finding was that the enhanced FC was correlated with lower cognitive performance as measured by the VIQ and the FSIQ in both patients and control subjects. Conclusions Our findings suggest that the FC alterations in resting-state brain networks related to the seizure onset zone and language processing areas could be related to adaptive plasticity for coping with cognitive dysfunction. PMID:29629540

An interactive toolbox for atlas-based segmentation and coding of volumetric images

NASA Astrophysics Data System (ADS)

Menegaz, G.; Luti, S.; Duay, V.; Thiran, J.-Ph.

2007-03-01

Medical imaging poses the great challenge of having compression algorithms that are lossless for diagnostic and legal reasons and yet provide high compression rates for reduced storage and transmission time. The images usually consist of a region of interest representing the part of the body under investigation surrounded by a "background", which is often noisy and not of diagnostic interest. In this paper, we propose a ROI-based 3D coding system integrating both the segmentation and the compression tools. The ROI is extracted by an atlas based 3D segmentation method combining active contours with information theoretic principles, and the resulting segmentation map is exploited for ROI based coding. The system is equipped with a GUI allowing the medical doctors to supervise the segmentation process and eventually reshape the detected contours at any point. The process is initiated by the user through the selection of either one pre-de.ned reference image or one image of the volume to be used as the 2D "atlas". The object contour is successively propagated from one frame to the next where it is used as the initial border estimation. In this way, the entire volume is segmented based on a unique 2D atlas. The resulting 3D segmentation map is exploited for adaptive coding of the different image regions. Two coding systems were considered: the JPEG3D standard and the 3D-SPITH. The evaluation of the performance with respect to both segmentation and coding proved the high potential of the proposed system in providing an integrated, low-cost and computationally effective solution for CAD and PAC systems.

Determination Of Constituent Concentration In Fluid Mixtures Using Magnetic Resonance Imaging

NASA Astrophysics Data System (ADS)

Galloway, Robert L.; Collins, Jerry C.; Carroll, Frank E.

1987-01-01

The primary application of magnetic resonance imaging (MRI) has been qualitative and anatomical evaluation of patient status. Recent efforts to analyze image information for quantitative evaluation centered on two relaxation parameters, Tl and T2, as the descriptors for the image data. In our work we have found that relaxation curves for biologic materials cannot be described by a monoexponential function and that, in a spin echo system, calculated Tl values are dependent on repetition time. This finding is not unexpected since, in physiologic imaging, any region of interest (ROI), is composed of a number of distinct substances and the response of that ROI will be a composite of the constituent materials. The purpose of our study was to develop a method by which the relaxation behaviors of a composite of physiological material might be characterized and use that characterization to determine its constituent materials. We created a phantom in which volumes of several "pure" materials (blood, plasma, saline and oil) were available as well as volumes which contained concentric enclosures of the pure materials. Images were formed at a number of repetition times, ranging from 160 milliseconds to 2 seconds. The image data was then transferred to a VAX 11/750 where regions of interest were marked and the mean image intensity for each ROI at each repetition time was calculated. The resultant relaxation curves of the pure materials formed basis vectors for the composite responses and the fractional content of each material was determined by a least-square error fit to the basis vectors. Excellent agreement was seen between known and measured mixture percentages. Ongoing work is centered around optimizing repetition time selection and accounting for the interaction between species in the mixtures.

Computer Aided Solution for Automatic Segmenting and Measurements of Blood Leucocytes Using Static Microscope Images.

PubMed

Abdulhay, Enas; Mohammed, Mazin Abed; Ibrahim, Dheyaa Ahmed; Arunkumar, N; Venkatraman, V

2018-02-17

Blood leucocytes segmentation in medical images is viewed as difficult process due to the variability of blood cells concerning their shape and size and the difficulty towards determining location of Blood Leucocytes. Physical analysis of blood tests to recognize leukocytes is tedious, time-consuming and liable to error because of the various morphological components of the cells. Segmentation of medical imagery has been considered as a difficult task because of complexity of images, and also due to the non-availability of leucocytes models which entirely captures the probable shapes in each structures and also incorporate cell overlapping, the expansive variety of the blood cells concerning their shape and size, various elements influencing the outer appearance of the blood leucocytes, and low Static Microscope Image disparity from extra issues outcoming about because of noise. We suggest a strategy towards segmentation of blood leucocytes using static microscope images which is a resultant of three prevailing systems of computer vision fiction: enhancing the image, Support vector machine for segmenting the image, and filtering out non ROI (region of interest) on the basis of Local binary patterns and texture features. Every one of these strategies are modified for blood leucocytes division issue, in this manner the subsequent techniques are very vigorous when compared with its individual segments. Eventually, we assess framework based by compare the outcome and manual division. The findings outcome from this study have shown a new approach that automatically segments the blood leucocytes and identify it from a static microscope images. Initially, the method uses a trainable segmentation procedure and trained support vector machine classifier to accurately identify the position of the ROI. After that, filtering out non ROI have proposed based on histogram analysis to avoid the non ROI and chose the right object. Finally, identify the blood leucocytes type using the texture feature. The performance of the foreseen approach has been tried in appearing differently in relation to the system against manual examination by a gynaecologist utilizing diverse scales. A total of 100 microscope images were used for the comparison, and the results showed that the proposed solution is a viable alternative to the manual segmentation method for accurately determining the ROI. We have evaluated the blood leucocytes identification using the ROI texture (LBP Feature). The identification accuracy in the technique used is about 95.3%., with 100 sensitivity and 91.66% specificity.

Rapid computation of single PET scan rest-stress myocardial blood flow parametric images by table look up.

PubMed

Guehl, Nicolas J; Normandin, Marc D; Wooten, Dustin W; Rozen, Guy; Ruskin, Jeremy N; Shoup, Timothy M; Woo, Jonghye; Ptaszek, Leon M; Fakhri, Georges El; Alpert, Nathaniel M

2017-09-01

We have recently reported a method for measuring rest-stress myocardial blood flow (MBF) using a single, relatively short, PET scan session. The method requires two IV tracer injections, one to initiate rest imaging and one at peak stress. We previously validated absolute flow quantitation in ml/min/cc for standard bull's eye, segmental analysis. In this work, we extend the method for fast computation of rest-stress MBF parametric images. We provide an analytic solution to the single-scan rest-stress flow model which is then solved using a two-dimensional table lookup method (LM). Simulations were performed to compare the accuracy and precision of the lookup method with the original nonlinear method (NLM). Then the method was applied to 16 single scan rest/stress measurements made in 12 pigs: seven studied after infarction of the left anterior descending artery (LAD) territory, and nine imaged in the native state. Parametric maps of rest and stress MBF as well as maps of left (f LV ) and right (f RV ) ventricular spill-over fractions were generated. Regions of interest (ROIs) for 17 myocardial segments were defined in bull's eye fashion on the parametric maps. The mean of each ROI was then compared to the rest (K 1r ) and stress (K 1s ) MBF estimates obtained from fitting the 17 regional TACs with the NLM. In simulation, the LM performed as well as the NLM in terms of precision and accuracy. The simulation did not show that bias was introduced by the use of a predefined two-dimensional lookup table. In experimental data, parametric maps demonstrated good statistical quality and the LM was computationally much more efficient than the original NLM. Very good agreement was obtained between the mean MBF calculated on the parametric maps for each of the 17 ROIs and the regional MBF values estimated by the NLM (K 1map LM  = 1.019 × K 1 ROI NLM  + 0.019, R 2  = 0.986; mean difference = 0.034 ± 0.036 mL/min/cc). We developed a table lookup method for fast computation of parametric imaging of rest and stress MBF. Our results show the feasibility of obtaining good quality MBF maps using modest computational resources, thus demonstrating that the method can be applied in a clinical environment to obtain full quantitative MBF information. © 2017 American Association of Physicists in Medicine.

Automatic detection and recognition of multiple macular lesions in retinal optical coherence tomography images with multi-instance multilabel learning

NASA Astrophysics Data System (ADS)

Fang, Leyuan; Yang, Liumao; Li, Shutao; Rabbani, Hossein; Liu, Zhimin; Peng, Qinghua; Chen, Xiangdong

2017-06-01

Detection and recognition of macular lesions in optical coherence tomography (OCT) are very important for retinal diseases diagnosis and treatment. As one kind of retinal disease (e.g., diabetic retinopathy) may contain multiple lesions (e.g., edema, exudates, and microaneurysms) and eye patients may suffer from multiple retinal diseases, multiple lesions often coexist within one retinal image. Therefore, one single-lesion-based detector may not support the diagnosis of clinical eye diseases. To address this issue, we propose a multi-instance multilabel-based lesions recognition (MIML-LR) method for the simultaneous detection and recognition of multiple lesions. The proposed MIML-LR method consists of the following steps: (1) segment the regions of interest (ROIs) for different lesions, (2) compute descriptive instances (features) for each lesion region, (3) construct multilabel detectors, and (4) recognize each ROI with the detectors. The proposed MIML-LR method was tested on 823 clinically labeled OCT images with normal macular and macular with three common lesions: epiretinal membrane, edema, and drusen. For each input OCT image, our MIML-LR method can automatically identify the number of lesions and assign the class labels, achieving the average accuracy of 88.72% for the cases with multiple lesions, which better assists macular disease diagnosis and treatment.

Comparison of three methods for the estimation of pineal gland volume using magnetic resonance imaging.

PubMed

Acer, Niyazi; Ilıca, Ahmet Turan; Turgut, Ahmet Tuncay; Ozçelik, Ozlem; Yıldırım, Birdal; Turgut, Mehmet

2012-01-01

Pineal gland is a very important neuroendocrine organ with many physiological functions such as regulating circadian rhythm. Radiologically, the pineal gland volume is clinically important because it is usually difficult to distinguish small pineal tumors via magnetic resonance imaging (MRI). Although many studies have estimated the pineal gland volume using different techniques, to the best of our knowledge, there has so far been no stereological work done on this subject. The objective of the current paper was to determine the pineal gland volume using stereological methods and by the region of interest (ROI) on MRI. In this paper, the pineal gland volumes were calculated in a total of 62 subjects (36 females, 26 males) who were free of any pineal lesions or tumors. The mean ± SD pineal gland volumes of the point-counting, planimetry, and ROI groups were 99.55 ± 51.34, 102.69 ± 40.39, and 104.33 ± 40.45 mm(3), respectively. No significant difference was found among the methods of calculating pineal gland volume (P > 0.05). From these results, it can be concluded that each technique is an unbiased, efficient, and reliable method, ideally suitable for in vivo examination of MRI data for pineal gland volume estimation.

[Development of a Computer-aided Diagnosis System to Distinguish between Benign and Malignant Mammary Tumors in Dynamic Magnetic Resonance Images: Automatic Detection of the Position with the Strongest Washout Effect in the Tumor].

PubMed

Miyazaki, Yoshiaki; Tabata, Nobuyuki; Taroura, Tomomi; Shinozaki, Kenji; Kubo, Yuichiro; Tokunaga, Eriko; Taguchi, Kenichi

We propose a computer-aided diagnostic (CAD) system that uses time-intensity curves to distinguish between benign and malignant mammary tumors. Many malignant tumors show a washout pattern in time-intensity curves. Therefore, we designed a program that automatically detects the position with the strongest washout effect using the technique, such as the subtraction technique, which extracts only the washout area in the tumor, and by scanning data in 2×2 pixel region of interest (ROI). Operation of this independently developed program was verified using a phantom system that simulated tumors. In three cases of malignant tumors, the washout pattern detection rate in images with manually set ROI was ≤6%, whereas the detection rate with our novel method was 100%. In one case of a benign tumor, when the same method was used, we checked that there was no washout effect and detected the persistent pattern. Thus, the distinction between benign and malignant tumors using our method was completely consistent with the pathological diagnoses made. Our novel method is therefore effective for differentiating between benign and malignant mammary tumors in dynamic magnetic resonance images.

Improved assessment of multiple sclerosis lesion segmentation agreement via detection and outline error estimates

PubMed Central

2012-01-01

Background Presented is the method “Detection and Outline Error Estimates” (DOEE) for assessing rater agreement in the delineation of multiple sclerosis (MS) lesions. The DOEE method divides operator or rater assessment into two parts: 1) Detection Error (DE) -- rater agreement in detecting the same regions to mark, and 2) Outline Error (OE) -- agreement of the raters in outlining of the same lesion. Methods DE, OE and Similarity Index (SI) values were calculated for two raters tested on a set of 17 fluid-attenuated inversion-recovery (FLAIR) images of patients with MS. DE, OE, and SI values were tested for dependence with mean total area (MTA) of the raters' Region of Interests (ROIs). Results When correlated with MTA, neither DE (ρ = .056, p=.83) nor the ratio of OE to MTA (ρ = .23, p=.37), referred to as Outline Error Rate (OER), exhibited significant correlation. In contrast, SI is found to be strongly correlated with MTA (ρ = .75, p < .001). Furthermore, DE and OER values can be used to model the variation in SI with MTA. Conclusions The DE and OER indices are proposed as a better method than SI for comparing rater agreement of ROIs, which also provide specific information for raters to improve their agreement. PMID:22812697

Comparison of Three Methods for the Estimation of Pineal Gland Volume Using Magnetic Resonance Imaging

PubMed Central

Acer, Niyazi; Ilıca, Ahmet Turan; Turgut, Ahmet Tuncay; Özçelik, Özlem; Yıldırım, Birdal; Turgut, Mehmet

2012-01-01

Pineal gland is a very important neuroendocrine organ with many physiological functions such as regulating circadian rhythm. Radiologically, the pineal gland volume is clinically important because it is usually difficult to distinguish small pineal tumors via magnetic resonance imaging (MRI). Although many studies have estimated the pineal gland volume using different techniques, to the best of our knowledge, there has so far been no stereological work done on this subject. The objective of the current paper was to determine the pineal gland volume using stereological methods and by the region of interest (ROI) on MRI. In this paper, the pineal gland volumes were calculated in a total of 62 subjects (36 females, 26 males) who were free of any pineal lesions or tumors. The mean ± SD pineal gland volumes of the point-counting, planimetry, and ROI groups were 99.55 ± 51.34, 102.69 ± 40.39, and 104.33 ± 40.45 mm3, respectively. No significant difference was found among the methods of calculating pineal gland volume (P > 0.05). From these results, it can be concluded that each technique is an unbiased, efficient, and reliable method, ideally suitable for in vivo examination of MRI data for pineal gland volume estimation. PMID:22619577

Cortical Thickness Estimations of FreeSurfer and the CAT12 Toolbox in Patients with Alzheimer's Disease and Healthy Controls.

PubMed

Seiger, Rene; Ganger, Sebastian; Kranz, Georg S; Hahn, Andreas; Lanzenberger, Rupert

2018-05-15

Automated cortical thickness (CT) measurements are often used to assess gray matter changes in the healthy and diseased human brain. The FreeSurfer software is frequently applied for this type of analysis. The computational anatomy toolbox (CAT12) for SPM, which offers a fast and easy-to-use alternative approach, was recently made available. In this study, we compared region of interest (ROI)-wise CT estimations of the surface-based FreeSurfer 6 (FS6) software and the volume-based CAT12 toolbox for SPM using 44 elderly healthy female control subjects (HC). In addition, these 44 HCs from the cross-sectional analysis and 34 age- and sex-matched patients with Alzheimer's disease (AD) were used to assess the potential of detecting group differences for each method. Finally, a test-retest analysis was conducted using 19 HC subjects. All data were taken from the OASIS database and MRI scans were recorded at 1.5 Tesla. A strong correlation was observed between both methods in terms of ROI mean CT estimates (R 2 = .83). However, CAT12 delivered significantly higher CT estimations in 32 of the 34 ROIs, indicating a systematic difference between both approaches. Furthermore, both methods were able to reliably detect atrophic brain areas in AD subjects, with the highest decreases in temporal areas. Finally, FS6 as well as CAT12 showed excellent test-retest variability scores. Although CT estimations were systematically higher for CAT12, this study provides evidence that this new toolbox delivers accurate and robust CT estimates and can be considered a fast and reliable alternative to FreeSurfer. © 2018 The Authors. Journal of Neuroimaging published by Wiley Periodicals, Inc. on behalf of American Society of Neuroimaging.

Dynamic gadolinium-enhanced magnetic resonance imaging allows accurate assessment of the synovial inflammatory activity in rheumatoid arthritis knee joints: a comparison with synovial histology.

PubMed

Axelsen, M B; Stoltenberg, M; Poggenborg, R P; Kubassova, O; Boesen, M; Bliddal, H; Hørslev-Petersen, K; Hanson, L G; Østergaard, M

2012-03-01

To determine whether dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) evaluated using semi-automatic image processing software can accurately assess synovial inflammation in rheumatoid arthritis (RA) knee joints. In 17 RA patients undergoing knee surgery, the average grade of histological synovial inflammation was determined from four biopsies obtained during surgery. A preoperative series of T(1)-weighted dynamic fast low-angle shot (FLASH) MR images was obtained. Parameters characterizing contrast uptake dynamics, including the initial rate of enhancement (IRE), were generated by the software in three different areas: (I) the entire slice (Whole slice); (II) a manually outlined region of interest (ROI) drawn quickly around the joint, omitting large artefacts such as blood vessels (Quick ROI); and (III) a manually outlined ROI following the synovial capsule of the knee joint (Precise ROI). Intra- and inter-reader agreement was assessed using the intra-class correlation coefficient (ICC). The IRE from the Quick ROI and the Precise ROI revealed high correlations to the grade of histological inflammation (Spearman's correlation coefficient (rho) = 0.70, p = 0.001 and rho = 0.74, p = 0.001, respectively). Intra- and inter-reader ICCs were very high (0.93-1.00). No Whole slice parameters were correlated to histology. DCE-MRI provides fast and accurate assessment of synovial inflammation in RA patients. Manual outlining of the joint to omit large artefacts is necessary.

Quantification of dental prostheses on cone‐beam CT images by the Taguchi method

PubMed Central

Kuo, Rong‐Fu; Fang, Kwang‐Ming; TY, Wong

2016-01-01

The gray values accuracy of dental cone‐beam computed tomography (CBCT) is affected by dental metal prostheses. The distortion of dental CBCT gray values could lead to inaccuracies of orthodontic and implant treatment. The aim of this study was to quantify the effect of scanning parameters and dental metal prostheses on the accuracy of dental cone‐beam computed tomography (CBCT) gray values using the Taguchi method. Eight dental model casts of an upper jaw including prostheses, and a ninth prosthesis‐free dental model cast, were scanned by two dental CBCT devices. The mean gray value of the selected circular regions of interest (ROIs) were measured using dental CBCT images of eight dental model casts and were compared with those measured from CBCT images of the prosthesis‐free dental model cast. For each image set, four consecutive slices of gingiva were selected. The seven factors (CBCTs, occlusal plane canting, implant connection, prosthesis position, coping material, coping thickness, and types of dental restoration) were used to evaluate scanning parameter and dental prostheses effects. Statistical methods of signal to noise ratio (S/N) and analysis of variance (ANOVA) with 95% confidence were applied to quantify the effects of scanning parameters and dental prostheses on dental CBCT gray values accuracy. For ROIs surrounding dental prostheses, the accuracy of CBCT gray values were affected primarily by implant connection (42%), followed by type of restoration (29%), prostheses position (19%), coping material (4%), and coping thickness (4%). For a single crown prosthesis (without support of implants) placed in dental model casts, gray value differences for ROIs 1–9 were below 12% and gray value differences for ROIs 13–18 away from prostheses were below 10%. We found the gray value differences set to be between 7% and 8% for regions next to a single implant‐supported titanium prosthesis, and between 46% and 59% for regions between double implant‐supported, nickel‐chromium alloys (Ni‐Cr) prostheses. Quantification of the effect of prostheses and scanning parameters on dental CBCT gray values was assessed. PACS numbers: 87.59.bd, 87.57Q PMID:26894354

An MRI-Based Atlas for Correlation of Imaging and Pathologic Findings in Alzheimer's Disease.

PubMed

Raman, Mekala R; Schwarz, Christopher G; Murray, Melissa E; Lowe, Val J; Dickson, Dennis W; Jack, Clifford R; Kantarci, Kejal

2016-05-01

Pathologic diagnosis is the gold standard in evaluating imaging measures developed as biomarkers for pathologically defined disorders. A brain MRI atlas representing autopsy-sampled tissue can be used to directly compare imaging and pathology findings. Our objective was to develop a brain MRI atlas representing the cortical regions that are routinely sampled at autopsy for the diagnosis of Alzheimer's disease (AD). Subjects (n = 22; ages at death = 70-95) with a range of pathologies and antemortem 3T MRI were included. Histology slides from 8 cortical regions sampled from the left hemisphere at autopsy guided the localization of the atlas regions of interest (ROIs) on each subject's antemortem 3D T1 -weighted MRI. These ROIs were then registered to a common template and combined to form one ROI representing the volume of tissue that was sampled by the pathologists. A subset of the subjects (n = 4; ages at death = 79-95) had amyloid PET imaging. Density of β-amyloid immunostain was quantified from the autopsy-sampled regions in the 4 subjects using a custom-designed ImageScope algorithm. Median uptake values were calculated in each ROI on the amyloid-PET images. We found an association between β-amyloid plaque density in 8 ROIs of the 4 subjects (total ROI n = 32) and median PiB SUVR (r(2) = .64; P < .0001). In an atlas developed for imaging and pathologic correlation studies, we demonstrated that antemortem amyloid burden measured in the atlas ROIs on amyloid PET is strongly correlated with β-amyloid density measured on histology. This atlas can be used in imaging and pathologic correlation studies. © 2016 The Authors. Journal of Neuroimaging published by Wiley Periodicals, Inc. on behalf of American Society of Neuroimaging.

Development of a dynamic quality assurance testing protocol for multisite clinical trial DCE-CT accreditation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Driscoll, B.; Keller, H.; Jaffray, D.

2013-08-15

Purpose: Credentialing can have an impact on whether or not a clinical trial produces useful quality data that is comparable between various institutions and scanners. With the recent increase of dynamic contrast enhanced-computed tomography (DCE-CT) usage as a companion biomarker in clinical trials, effective quality assurance, and control methods are required to ensure there is minimal deviation in the results between different scanners and protocols at various institutions. This paper attempts to address this problem by utilizing a dynamic flow imaging phantom to develop and evaluate a DCE-CT quality assurance (QA) protocol.Methods: A previously designed flow phantom, capable of producingmore » predictable and reproducible time concentration curves from contrast injection was fully validated and then utilized to design a DCE-CT QA protocol. The QA protocol involved a set of quantitative metrics including injected and total mass error, as well as goodness of fit comparison to the known truth concentration curves. An additional region of interest (ROI) sensitivity analysis was also developed to provide additional details on intrascanner variability and determine appropriate ROI sizes for quantitative analysis. Both the QA protocol and ROI sensitivity analysis were utilized to test variations in DCE-CT results using different imaging parameters (tube voltage and current) as well as alternate reconstruction methods and imaging techniques. The developed QA protocol and ROI sensitivity analysis was then applied at three institutions that were part of clinical trial involving DCE-CT and results were compared.Results: The inherent specificity of robustness of the phantom was determined through calculation of the total intraday variability and determined to be less than 2.2 ± 1.1% (total calculated output contrast mass error) with a goodness of fit (R{sup 2}) of greater than 0.99 ± 0.0035 (n= 10). The DCE-CT QA protocol was capable of detecting significant deviations from the expected phantom result when scanning at low mAs and low kVp in terms of quantitative metrics (Injected Mass Error 15.4%), goodness of fit (R{sup 2}) of 0.91, and ROI sensitivity (increase in minimum input function ROI radius by 146 ± 86%). These tests also confirmed that the ASIR reconstruction process was beneficial in reducing noise without substantially increasing partial volume effects and that vendor specific modes (e.g., axial shuttle) did not significantly affect the phantom results. The phantom and QA protocol were finally able to quickly (<90 min) and successfully validate the DCE-CT imaging protocol utilized at the three separate institutions of a multicenter clinical trial; thereby enhancing the confidence in the patient data collected.Conclusions: A DCE QA protocol was developed that, in combination with a dynamic multimodality flow phantom, allows the intrascanner variability to be separated from other sources of variability such as the impact of injection protocol and ROI selection. This provides a valuable resource that can be utilized at various clinical trial institutions to test conformance with imaging protocols and accuracy requirements as well as ensure that the scanners are performing as expected for dynamic scans.« less

68 Ga-PSMA-PET/CT for the evaluation of pulmonary metastases and opacities in patients with prostate cancer.

PubMed

Damjanovic, Jonathan; Janssen, Jan-Carlo; Furth, Christian; Diederichs, Gerd; Walter, Thula; Amthauer, Holger; Makowski, Marcus R

2018-05-16

The purpose of this study was to investigate the imaging properties of pulmonary metastases and benign opacities in 68 Ga-PSMA positron emission tomography (PET) in patients with prostate cancer (PC). 68 Ga-PSMA-PET/CT scans of 739 PC patients available in our database were evaluated retrospectively for lung metastases and non-solid focal pulmonary opacities. Maximum standardized uptake values (SUV max ) were assessed by two- and three-dimensional regions of interest (2D/3D ROI). Additionally CT features of the lesions, such as location, morphology and size were identified. Ninety-one pulmonary metastases and fourteen opacities were identified in 34 PC patients. In total, 66 PSMA-positive (72.5%) and 25 PSMA-negative (27.5%) metastases were identified. The mean SUV max of pulmonary opacities was 2.2±0.7 in 2D ROI and 2.4±0.8 in 3D ROI. The mean SUV max of PSMA-positive pulmonary metastases was 4.5±2.7 in 2D ROI and in 4.7±2.9 in 3D ROI; this was significantly higher than the SUV max of pulmonary opacities in both 2D and 3D ROI (p<0.001). The mean SUV max of PSMA-negative metastases was 1.0±0.5 in 2D ROI and 1.0±0.4 in 3D ROI, and significantly lower than that of the pulmonary opacities (p<0.001). A significant (p<0.05) weak linear correlation between size and 3D SUV max in lung metastases (ρ Spearman =0.207) was found. Based on the SUV max in 68 Ga-PSMA-PET alone, it was not possible to differentiate between pulmonary metastases and pulmonary opacities. The majority of lung metastases highly overexpressed PSMA, while a relevant number of metastases were PSMA-negative. Pulmonary opacities demonstrated a moderate tracer uptake, significantly lower than PSMA-positive lung metastases, yet significantly higher than PSMA-negative metastases.

Automated atlas-based clustering of white matter fiber tracts from DTMRI.

PubMed

Maddah, Mahnaz; Mewes, Andrea U J; Haker, Steven; Grimson, W Eric L; Warfield, Simon K

2005-01-01

A new framework is presented for clustering fiber tracts into anatomically known bundles. This work is motivated by medical applications in which variation analysis of known bundles of fiber tracts in the human brain is desired. To include the anatomical knowledge in the clustering, we invoke an atlas of fiber tracts, labeled by the number of bundles of interest. In this work, we construct such an atlas and use it to cluster all fiber tracts in the white matter. To build the atlas, we start with a set of labeled ROIs specified by an expert and extract the fiber tracts initiating from each ROI. Affine registration is used to project the extracted fiber tracts of each subject to the atlas, whereas their B-spline representation is used to efficiently compare them to the fiber tracts in the atlas and assign cluster labels. Expert visual inspection of the result confirms that the proposed method is very promising and efficient in clustering of the known bundles of fiber tracts.

Fast Human Detection for Intelligent Monitoring Using Surveillance Visible Sensors

PubMed Central

Ko, Byoung Chul; Jeong, Mira; Nam, JaeYeal

2014-01-01

Human detection using visible surveillance sensors is an important and challenging work for intruder detection and safety management. The biggest barrier of real-time human detection is the computational time required for dense image scaling and scanning windows extracted from an entire image. This paper proposes fast human detection by selecting optimal levels of image scale using each level's adaptive region-of-interest (ROI). To estimate the image-scaling level, we generate a Hough windows map (HWM) and select a few optimal image scales based on the strength of the HWM and the divide-and-conquer algorithm. Furthermore, adaptive ROIs are arranged per image scale to provide a different search area. We employ a cascade random forests classifier to separate candidate windows into human and nonhuman classes. The proposed algorithm has been successfully applied to real-world surveillance video sequences, and its detection accuracy and computational speed show a better performance than those of other related methods. PMID:25393782

Preprocessing of region of interest localization based on local surface curvature analysis for three-dimensional reconstruction with multiresolution

NASA Astrophysics Data System (ADS)

Li, Wanjing; Schütze, Rainer; Böhler, Martin; Boochs, Frank; Marzani, Franck S.; Voisin, Yvon

2009-06-01

We present an approach to integrate a preprocessing step of the region of interest (ROI) localization into 3-D scanners (laser or stereoscopic). The definite objective is to make the 3-D scanner intelligent enough to localize rapidly in the scene, during the preprocessing phase, the regions with high surface curvature, so that precise scanning will be done only in these regions instead of in the whole scene. In this way, the scanning time can be largely reduced, and the results contain only pertinent data. To test its feasibility and efficiency, we simulated the preprocessing process under an active stereoscopic system composed of two cameras and a video projector. The ROI localization is done in an iterative way. First, the video projector projects a regular point pattern in the scene, and then the pattern is modified iteratively according to the local surface curvature of each reconstructed 3-D point. Finally, the last pattern is used to determine the ROI. Our experiments showed that with this approach, the system is capable to localize all types of objects, including small objects with small depth.

An Ensemble Method for Classifying Regional Disease Patterns of Diffuse Interstitial Lung Disease Using HRCT Images from Different Vendors.

PubMed

Jun, Sanghoon; Kim, Namkug; Seo, Joon Beom; Lee, Young Kyung; Lynch, David A

2017-12-01

We propose the use of ensemble classifiers to overcome inter-scanner variations in the differentiation of regional disease patterns in high-resolution computed tomography (HRCT) images of diffuse interstitial lung disease patients obtained from different scanners. A total of 600 rectangular 20 × 20-pixel regions of interest (ROIs) on HRCT images obtained from two different scanners (GE and Siemens) and the whole lung area of 92 HRCT images were classified as one of six regional pulmonary disease patterns by two expert radiologists. Textual and shape features were extracted from each ROI and the whole lung parenchyma. For automatic classification, individual and ensemble classifiers were trained and tested with the ROI dataset. We designed the following three experimental sets: an intra-scanner study in which the training and test sets were from the same scanner, an integrated scanner study in which the data from the two scanners were merged, and an inter-scanner study in which the training and test sets were acquired from different scanners. In the ROI-based classification, the ensemble classifiers showed better (p < 0.001) accuracy (89.73%, SD = 0.43) than the individual classifiers (88.38%, SD = 0.31) in the integrated scanner test. The ensemble classifiers also showed partial improvements in the intra- and inter-scanner tests. In the whole lung classification experiment, the quantification accuracies of the ensemble classifiers with integrated training (49.57%) were higher (p < 0.001) than the individual classifiers (48.19%). Furthermore, the ensemble classifiers also showed better performance in both the intra- and inter-scanner experiments. We concluded that the ensemble classifiers provide better performance when using integrated scanner images.

Computerized scheme for detection of diffuse lung diseases on CR chest images

NASA Astrophysics Data System (ADS)

Pereira, Roberto R., Jr.; Shiraishi, Junji; Li, Feng; Li, Qiang; Doi, Kunio

2008-03-01

We have developed a new computer-aided diagnostic (CAD) scheme for detection of diffuse lung disease in computed radiographic (CR) chest images. One hundred ninety-four chest images (56 normals and 138 abnormals with diffuse lung diseases) were used. The 138 abnormal cases were classified into three levels of severity (34 mild, 60 moderate, and 44 severe) by an experienced chest radiologist with use of five different patterns, i.e., reticular, reticulonodular, nodular, air-space opacity, and emphysema. In our computerized scheme, the first moment of the power spectrum, the root-mean-square variation, and the average pixel value were determined for each region of interest (ROI), which was selected automatically in the lung fields. The average pixel value and its dependence on the location of the ROI were employed for identifying abnormal patterns due to air-space opacity or emphysema. A rule-based method was used for determining three levels of abnormality for each ROI (0: normal, 1: mild, 2: moderate, and 3: severe). The distinction between normal lungs and abnormal lungs with diffuse lung disease was determined based on the fractional number of abnormal ROIs by taking into account the severity of abnormalities. Preliminary results indicated that the area under the ROC curve was 0.889 for the 44 severe cases, 0.825 for the 104 severe and moderate cases, and 0.794 for all cases. We have identified a number of problems and reasons causing false positives on normal cases, and also false negatives on abnormal cases. In addition, we have discussed potential approaches for improvement of our CAD scheme. In conclusion, the CAD scheme for detection of diffuse lung diseases based on texture features extracted from CR chest images has the potential to assist radiologists in their interpretation of diffuse lung diseases.

Digital mammography: observer performance study of the effects of pixel size on radiologists' characterization of malignant and benign microcalcifications

NASA Astrophysics Data System (ADS)

Chan, Heang-Ping; Helvie, Mark A.; Petrick, Nicholas; Sahiner, Berkman; Adler, Dorit D.; Blane, Caroline E.; Joynt, Lynn K.; Paramagul, Chintana; Roubidoux, Marilyn A.; Wilson, Todd E.; Hadjiiski, Lubomir M.; Goodsitt, Mitchell M.

1999-05-01

A receiver operating characteristic (ROC) experiment was conducted to evaluate the effects of pixel size on the characterization of mammographic microcalcifications. Digital mammograms were obtained by digitizing screen-film mammograms with a laser film scanner. One hundred twelve two-view mammograms with biopsy-proven microcalcifications were digitized at a pixel size of 35 micrometer X 35 micrometer. A region of interest (ROI) containing the microcalcifications was extracted from each image. ROI images with pixel sizes of 70 micrometers, 105 micrometers, and 140 micrometers were derived from the ROI of 35 micrometer pixel size by averaging 2 X 2, 3 X 3, and 4 X 4 neighboring pixels, respectively. The ROI images were printed on film with a laser imager. Seven MQSA-approved radiologists participated as observers. The likelihood of malignancy of the microcalcifications was rated on a 10-point confidence rating scale and analyzed with ROC methodology. The classification accuracy was quantified by the area, Az, under the ROC curve. The statistical significance of the differences in the Az values for different pixel sizes was estimated with the Dorfman-Berbaum-Metz (DBM) method for multi-reader, multi-case ROC data. It was found that five of the seven radiologists demonstrated a higher classification accuracy with the 70 micrometer or 105 micrometer images. The average Az also showed a higher classification accuracy in the range of 70 to 105 micrometer pixel size. However, the differences in A(subscript z/ between different pixel sizes did not achieve statistical significance. The low specificity of image features of microcalcifications an the large interobserver and intraobserver variabilities may have contributed to the relatively weak dependence of classification accuracy on pixel size.

A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology.

PubMed

Qiu, Yuchen; Yan, Shiju; Gundreddy, Rohith Reddy; Wang, Yunzhi; Cheng, Samuel; Liu, Hong; Zheng, Bin

2017-01-01

To develop and test a deep learning based computer-aided diagnosis (CAD) scheme of mammograms for classifying between malignant and benign masses. An image dataset involving 560 regions of interest (ROIs) extracted from digital mammograms was used. After down-sampling each ROI from 512×512 to 64×64 pixel size, we applied an 8 layer deep learning network that involves 3 pairs of convolution-max-pooling layers for automatic feature extraction and a multiple layer perceptron (MLP) classifier for feature categorization to process ROIs. The 3 pairs of convolution layers contain 20, 10, and 5 feature maps, respectively. Each convolution layer is connected with a max-pooling layer to improve the feature robustness. The output of the sixth layer is fully connected with a MLP classifier, which is composed of one hidden layer and one logistic regression layer. The network then generates a classification score to predict the likelihood of ROI depicting a malignant mass. A four-fold cross validation method was applied to train and test this deep learning network. The results revealed that this CAD scheme yields an area under the receiver operation characteristic curve (AUC) of 0.696±0.044, 0.802±0.037, 0.836±0.036, and 0.822±0.035 for fold 1 to 4 testing datasets, respectively. The overall AUC of the entire dataset is 0.790±0.019. This study demonstrates the feasibility of applying a deep learning based CAD scheme to classify between malignant and benign breast masses without a lesion segmentation, image feature computation and selection process.

Gender, personality, and serotonin-2A receptor binding in healthy subjects

PubMed Central

Soloff, Paul H.; Price, Julie C.; Mason, Neale Scott; Becker, Carl; Meltzer, Carolyn C.

2009-01-01

The vulnerability to mood disorders, impulsive-aggression, eating disorders, and suicidal behavior varies greatly with gender, and may reflect gender differences in central serotonergic function. We investigated the relationships of gender, mood, impulsivity, aggression and temperament to 5HT2A receptor binding in 21 healthy subjects using [18F]altanserin and PET neuro-imaging. Binding potentials in pre-defined Regions of Interest (ROI) were calculated using the Logan graphical method, corrected for partial volume effects, and compared by gender with age co-varied. SPM analysis was used for voxel level comparisons. Altanserin binding (BPp) was greater in male than female subjects in 9 ROIs: hippocampus (HIP) and Lt. HIP, lateral orbital frontal cortex (LOF) and Lt.LOF, left medial frontal cortex (Lt.MFC), left medial temporal cortex (Lt. MTC), left occipital cortex (Lt. OCC), thalamus (THL) and Lt. THL. Differences in Lt. HIP and Lt. MTL remained significant after Bonferroni correction. Gender differences were noted in the co-variation of psychological traits with BPp values in specific ROIs. Among males alone, aggression was negatively correlated with BPp values in Lt. LOF and Lt. MFC, and Suspiciousness positively correlated in LOF, Lt. LOF and Lt. MFC. Among female subjects alone, Negativism was positively correlated with BPp values in HIP, and Verbal Hostility in Lt. HIP. Altanserin binding in Lt. MTC was positively correlated with Persistence, with no significant gender effect. Gender differences in 5HT2A receptor function in specific ROIs may mediate expression of psychological characteristics such as aggression, suspiciousness and negativism. Future studies of 5HT2A receptor function and its relationship to behavior should control for gender. PMID:19959344

Diagnostic accuracy of hepatorenal index in the detection and grading of hepatic steatosis.

PubMed

Chauhan, Anil; Sultan, Laith R; Furth, Emma E; Jones, Lisa P; Khungar, Vandana; Sehgal, Chandra M

2016-11-12

The objectives of our study were to assess the accuracy of hepatorenal index (HRI) in detection and grading of hepatic steatosis and to evaluate various factors that can affect the HRI measurement. Forty-five patients, who had undergone an abdominal sonographic examination within 30 days of liver biopsy, were enrolled. The HRI was calculated as the ratio of the mean brightness levels of the liver and renal parenchymas. The effect of the measurement technique on the HRI was evaluated by using various sizes, depths, and locations of the regions of interest (ROIs) in the liver. The measurements were obtained by two observers. The HRI was compared with the subjective grading of steatosis. The optimal HRI cutoff to detect steatosis was 2.01, yielding a sensitivity of 62.5% and specificity of 95.2%. Subjective grading had a sensitivity of 87.5% and specificity of 62.5%. HRIs of the hepatic steatosis group were statistically different from the no-steatosis group (p < 0.05). However, there was no statistically significant difference between mild steatosis and no-steatosis groups (p value = 0.72). There was a strong correlation between different HRIs based on variable placements of ROIs, except when the ROIs were positioned randomly. Interclass correlation coefficient for measurements performed by two observers was 0.74 (confidence interval: 0.58-0.86). The HRI is an effective tool for detecting hepatic steatosis. It provides similar accuracy for different methods of ROI placement (except for random placement) and has good interobserver agreement. It, however, is unable to effectively differentiate between absent and mild steatosis. © 2016 Wiley Periodicals, Inc. J Clin Ultrasound 44:580-586, 2016. © 2016 Wiley Periodicals, Inc.

Improving performance of breast cancer risk prediction using a new CAD-based region segmentation scheme

NASA Astrophysics Data System (ADS)

Heidari, Morteza; Zargari Khuzani, Abolfazl; Danala, Gopichandh; Qiu, Yuchen; Zheng, Bin

2018-02-01

Objective of this study is to develop and test a new computer-aided detection (CAD) scheme with improved region of interest (ROI) segmentation combined with an image feature extraction framework to improve performance in predicting short-term breast cancer risk. A dataset involving 570 sets of "prior" negative mammography screening cases was retrospectively assembled. In the next sequential "current" screening, 285 cases were positive and 285 cases remained negative. A CAD scheme was applied to all 570 "prior" negative images to stratify cases into the high and low risk case group of having cancer detected in the "current" screening. First, a new ROI segmentation algorithm was used to automatically remove useless area of mammograms. Second, from the matched bilateral craniocaudal view images, a set of 43 image features related to frequency characteristics of ROIs were initially computed from the discrete cosine transform and spatial domain of the images. Third, a support vector machine model based machine learning classifier was used to optimally classify the selected optimal image features to build a CAD-based risk prediction model. The classifier was trained using a leave-one-case-out based cross-validation method. Applying this improved CAD scheme to the testing dataset, an area under ROC curve, AUC = 0.70+/-0.04, which was significantly higher than using the extracting features directly from the dataset without the improved ROI segmentation step (AUC = 0.63+/-0.04). This study demonstrated that the proposed approach could improve accuracy on predicting short-term breast cancer risk, which may play an important role in helping eventually establish an optimal personalized breast cancer paradigm.

Regionally-Specific Diffusion Tensor Imaging in Mild Cognitive Impairment and Alzheimer’s Disease

PubMed Central

Mielke, M.M.; Kozauer, N.A.; Chan, K.C.G.; George, M.; Toroney, J.; Zerrate, M.; Bandeen-Roche, K.; Wang, M-C; vanZijl, P.; Pekar, J.J.; Mori, S.; Lyketsos, C.G.; Albert, M.

2009-01-01

Background Diffusion tensor imaging (DTI) studies have shown significant cross-sectional differences among normal controls (Bozzali et al., 2002), mild cognitive impairment (Robbins et al.) and Alzheimer’s disease (AD) patients in several fiber tracts in the brain, but longitudinal assessment is needed. Methods We studied 75 participants (25 NC, 25 amnestic MCI, and 25 mild AD) at baseline and 3 months later, with both imaging and clinical evaluations. Fractional anisotropy (Bozzali et al., 2002) was analyzed in regions of interest (ROIs) in: (1) fornix, (2) cingulum bundle, (3) splenium, and (4) cerebral peduncles. Clinical data included assessments of clinical severity and cognitive function. Cross-sectional and longitudinal differences in FA, within each ROI, were analyzed with generalized estimating equations (GEE). Results Cross-sectionally, AD patients had lower FA than NC (p<0.05) at baseline and 3 months in the fornix and anterior portion of the cingulum bundle. Compared to MCI, AD cases had lower FA (p<0.05) in these regions and the splenium at 0 and 3 months. Both the fornix and anterior cingulum correlated across all clinical cognitive scores; lower FA in these ROIs corresponded to worse performance. Over the course of 3 months, when the subjects were clinically stable, the ROIs were also largely stable. Conclusions Using DTI, findings indicate FA is decreased in specific fiber tracts among groups of subjects that vary along the spectrum from normal to AD, and that this measure is stable over short periods of time. The fornix is a predominant outflow tract of the hippocampus and may be an important indicator of AD progression. PMID:19457371

Noninvasive Visualization and Analysis of the Human Parafoveal Capillary Network Using Swept Source OCT Optical Microangiography.

PubMed

Kuehlewein, Laura; Tepelus, Tudor C; An, Lin; Durbin, Mary K; Srinivas, Sowmya; Sadda, Srinivas R

2015-06-01

We characterized the foveal avascular zone (FAZ) and the parafoveal capillary network in healthy subjects using swept source OCT optical microangiography (OMAG). We acquired OMAG images of the macula of 19 eyes (13 healthy individuals) using a prototype swept source laser OCT. En face images of the retinal vasculature were generated for superficial and deep inner retinal layers (SRL/DRL) in regions of interest 250 (ROI-250) and 500 (ROI-500) μm from the FAZ border. The mean area (mm2) of the FAZ was 0.304 ± 0.132 for the SRL and 0.486 ± 0.162 for the DRL (P < 0.001). Mean vessel density (%) was 67.3 ± 6.4 for the SRL and 34.5 ± 8.6 for the DRL in the ROI-250 (P < 0.001), and 74.2 ± 3.9 for the SRL and 72.3 ± 4.9 for the DRL in the ROI-500 (P = 0.160). Swept source OMAG images of healthy subjects allowed analysis of the FAZ and the density of the parafoveal capillary network at different retinal layers.

A no-reference video quality assessment metric based on ROI

NASA Astrophysics Data System (ADS)

Jia, Lixiu; Zhong, Xuefei; Tu, Yan; Niu, Wenjuan

2015-01-01

A no reference video quality assessment metric based on the region of interest (ROI) was proposed in this paper. In the metric, objective video quality was evaluated by integrating the quality of two compressed artifacts, i.e. blurring distortion and blocking distortion. The Gaussian kernel function was used to extract the human density maps of the H.264 coding videos from the subjective eye tracking data. An objective bottom-up ROI extraction model based on magnitude discrepancy of discrete wavelet transform between two consecutive frames, center weighted color opponent model, luminance contrast model and frequency saliency model based on spectral residual was built. Then only the objective saliency maps were used to compute the objective blurring and blocking quality. The results indicate that the objective ROI extraction metric has a higher the area under the curve (AUC) value. Comparing with the conventional video quality assessment metrics which measured all the video quality frames, the metric proposed in this paper not only decreased the computation complexity, but improved the correlation between subjective mean opinion score (MOS) and objective scores.

Foot roll-over evaluation based on 3D dynamic foot scan.

PubMed

Samson, William; Van Hamme, Angèle; Sanchez, Stéphane; Chèze, Laurence; Van Sint Jan, Serge; Feipel, Véronique

2014-01-01

Foot roll-over is commonly analyzed to evaluate gait pathologies. The current study utilized a dynamic foot scanner (DFS) to analyze foot roll-over. The right feet of ten healthy subjects were assessed during gait trials with a DFS system integrated into a walkway. A foot sole picture was computed by vertically projecting points from the 3D foot shape which were lower than a threshold height of 15 mm. A 'height' value of these projected points was determined; corresponding to the initial vertical coordinates prior to projection. Similar to pedobarographic analysis, the foot sole picture was segmented into anatomical regions of interest (ROIs) to process mean height (average of height data by ROI) and projected surface (area of the projected foot sole by ROI). Results showed that these variables evolved differently to plantar pressure data previously reported in the literature, mainly due to the specificity of each physical quantity (millimeters vs Pascals). Compared to plantar pressure data arising from surface contact by the foot, the current method takes into account the whole plantar aspect of the foot, including the parts that do not make contact with the support surface. The current approach using height data could contribute to a better understanding of specific aspects of foot motion during walking, such as plantar arch height and the windlass mechanism. Results of this study show the underlying method is reliable. Further investigation is required to validate the DFS measurements within a clinical context, prior to implementation into clinical practice. Copyright © 2013 Elsevier B.V. All rights reserved.

An automated approach to improve efficacy in detecting residual malignant cancer cell for facilitating prognostic assessment of leukemia: an initial study

NASA Astrophysics Data System (ADS)

Qiu, Yuchen; Lu, Xianglan; Tan, Maxine; Li, Shibo; Liu, Hong; Zheng, Bin

2015-03-01

The purpose of this study is to investigate the feasibility of applying automatic interphase FISH cells analysis method for detecting the residual malignancy of post chemotherapy leukemia patients. In the experiment, two clinical specimens with translocation between chromosome No. 9 and 22 or No. 11 and 14 were selected from the patients underwent leukemia diagnosis and treatment. The entire slide of each specimen was first digitalized by a commercial fluorescent microscope using a 40× objective lens. Then, the scanned images were processed by a computer-aided detecting (CAD) scheme to identify the analyzable FISH cells, which is accomplished by applying a series of features including the region size, Brenner gradient and maximum intensity. For each identified cell, the scheme detected and counted the number of the FISH signal dots inside the nucleus, using the adaptive threshold of the region size and distance of the labeled FISH dots. The results showed that the new CAD scheme detected 8093 and 6675 suspicious regions of interest (ROI) in two specimens, among which 4546 and 3807 ROI contain analyzable interphase FISH cell. In these analyzable ROIs, CAD selected 334 and 405 residual malignant cancer cells, which is substantially more than those visually detected in a cytogenetic laboratory of our medical center (334 vs. 122, 405 vs. 160). This investigation indicates that an automatic interphase FISH cell scanning and CAD method has the potential to improve the accuracy and efficiency of the prognostic assessment for leukemia and other genetic related cancer patients in the future.

Robust Estimation of Mahalanobis Distances in Hyperspectral Images

DTIC Science & Technology

2006-12-01

each method used to fit the MD distribution from the DFC ROI. No- tice how the F -mixture is affected by the last two data points (points most unlike...bottom spectra are the minimum and maximum in magnitude. Notice the decrease in variability compared to DFC and MCFC. For this ROI, the variability is...performance for DFC MD Data (ROI = 11,557 pix- els). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 6.5. Summary of performance

A comparative study of automatic image segmentation algorithms for target tracking in MR-IGRT.

PubMed

Feng, Yuan; Kawrakow, Iwan; Olsen, Jeff; Parikh, Parag J; Noel, Camille; Wooten, Omar; Du, Dongsu; Mutic, Sasa; Hu, Yanle

2016-03-08

On-board magnetic resonance (MR) image guidance during radiation therapy offers the potential for more accurate treatment delivery. To utilize the real-time image information, a crucial prerequisite is the ability to successfully segment and track regions of interest (ROI). The purpose of this work is to evaluate the performance of different segmentation algorithms using motion images (4 frames per second) acquired using a MR image-guided radiotherapy (MR-IGRT) system. Manual con-tours of the kidney, bladder, duodenum, and a liver tumor by an experienced radiation oncologist were used as the ground truth for performance evaluation. Besides the manual segmentation, images were automatically segmented using thresholding, fuzzy k-means (FKM), k-harmonic means (KHM), and reaction-diffusion level set evolution (RD-LSE) algorithms, as well as the tissue tracking algorithm provided by the ViewRay treatment planning and delivery system (VR-TPDS). The performance of the five algorithms was evaluated quantitatively by comparing with the manual segmentation using the Dice coefficient and target registration error (TRE) measured as the distance between the centroid of the manual ROI and the centroid of the automatically segmented ROI. All methods were able to successfully segment the bladder and the kidney, but only FKM, KHM, and VR-TPDS were able to segment the liver tumor and the duodenum. The performance of the thresholding, FKM, KHM, and RD-LSE algorithms degraded as the local image contrast decreased, whereas the performance of the VP-TPDS method was nearly independent of local image contrast due to the reference registration algorithm. For segmenting high-contrast images (i.e., kidney), the thresholding method provided the best speed (< 1 ms) with a satisfying accuracy (Dice = 0.95). When the image contrast was low, the VR-TPDS method had the best automatic contour. Results suggest an image quality determination procedure before segmentation and a combination of different methods for optimal segmentation with the on-board MR-IGRT system.

A hybrid deep learning approach to predict malignancy of breast lesions using mammograms

NASA Astrophysics Data System (ADS)

Wang, Yunzhi; Heidari, Morteza; Mirniaharikandehei, Seyedehnafiseh; Gong, Jing; Qian, Wei; Qiu, Yuchen; Zheng, Bin

2018-03-01

Applying deep learning technology to medical imaging informatics field has been recently attracting extensive research interest. However, the limited medical image dataset size often reduces performance and robustness of the deep learning based computer-aided detection and/or diagnosis (CAD) schemes. In attempt to address this technical challenge, this study aims to develop and evaluate a new hybrid deep learning based CAD approach to predict likelihood of a breast lesion detected on mammogram being malignant. In this approach, a deep Convolutional Neural Network (CNN) was firstly pre-trained using the ImageNet dataset and serve as a feature extractor. A pseudo-color Region of Interest (ROI) method was used to generate ROIs with RGB channels from the mammographic images as the input to the pre-trained deep network. The transferred CNN features from different layers of the CNN were then obtained and a linear support vector machine (SVM) was trained for the prediction task. By applying to a dataset involving 301 suspicious breast lesions and using a leave-one-case-out validation method, the areas under the ROC curves (AUC) = 0.762 and 0.792 using the traditional CAD scheme and the proposed deep learning based CAD scheme, respectively. An ensemble classifier that combines the classification scores generated by the two schemes yielded an improved AUC value of 0.813. The study results demonstrated feasibility and potentially improved performance of applying a new hybrid deep learning approach to develop CAD scheme using a relatively small dataset of medical images.

Simplifying volumes-of-interest (VOIs) definition in quantitative SPECT: Beyond manual definition of 3D whole-organ VOIs.

PubMed

Vicente, Esther M; Lodge, Martin A; Rowe, Steven P; Wahl, Richard L; Frey, Eric C

2017-05-01

We investigated the feasibility of using simpler methods than manual whole-organ volume-of-interest (VOI) definition to estimate the organ activity concentration in single photon emission computed tomography (SPECT) in cases where the activity in the organ can be assumed to be uniformly distributed on the scale of the voxel size. In particular, we investigated an anatomic region-of-interest (ROI) defined in a single transaxial slice, and a single sphere placed inside the organ boundaries. The evaluation was carried out using Monte Carlo simulations based on patient indium 111 In pentetreotide SPECT and computed tomography (CT) images. We modeled constant activity concentrations in each organ, validating this assumption by comparing the distribution of voxel values inside the organ VOIs of the simulated data with the patient data. We simulated projection data corresponding to 100, 50, and 25% of the clinical count level to study the effects of noise level due to shortened acquisition time. Images were reconstructed using a previously validated quantitative SPECT reconstruction method. The evaluation was performed in terms of the accuracy and precision of the activity concentration estimates. The results demonstrated that the non-uniform image intensity observed in the reconstructed images in the organs with normal uptake was consistent with uniform activity concentration in the organs on the scale of the voxel size; observed non-uniformities in image intensity were due to a combination of partial-volume effects at the boundaries of the organ, artifacts in the reconstructed image due to collimator-detector response compensation, and noise. Using an ROI defined in a single transaxial slice produced similar biases compared to the three-dimensional (3D) whole-organ VOIs, provided that the transaxial slice was near the central plane of the organ and that the pixels from the organ boundaries were not included in the ROI. Although this slice method was sensitive to noise, biases were less than 10% for all the noise levels studied. The use of spherical VOIs was more sensitive to noise. The method was more accurate for larger spheres and larger organs such as the liver in comparison to the kidneys. Biases lower than 7% were found in the liver when using large enough spheres (radius ≥ 28 mm), regardless of the position, of the VOI inside the organ even with shortened acquisition times. The biases were more position-dependent for smaller organs. Both of the simpler methods provided suitable surrogates in terms of accuracy and precision. The results suggested that a spherical VOI was more appropriate for estimating the activity concentration in larger organs such as the liver, regardless of the position of the sphere inside the organ. Larger spheres resulted in better estimates. A single-slice ROI was more suitable for activity estimation in smaller organs such as the kidneys, providing that the transaxial slice selected was near the central plane of the organ and that voxels from the organ boundaries were excluded. Under those conditions, activity concentrations with biases lower than 5% were observed for all the studied count levels and coefficients of variation were less than 9% and 5% for the 25% and 100% count levels, respectively. © 2017 American Association of Physicists in Medicine.

Flexible reduced field of view magnetic resonance imaging based on single-shot spatiotemporally encoded technique

NASA Astrophysics Data System (ADS)

Li, Jing; Cai, Cong-Bo; Chen, Lin; Chen, Ying; Qu, Xiao-Bo; Cai, Shu-Hui

2015-10-01

In many ultrafast imaging applications, the reduced field-of-view (rFOV) technique is often used to enhance the spatial resolution and field inhomogeneity immunity of the images. The stationary-phase characteristic of the spatiotemporally-encoded (SPEN) method offers an inherent applicability to rFOV imaging. In this study, a flexible rFOV imaging method is presented and the superiority of the SPEN approach in rFOV imaging is demonstrated. The proposed method is validated with phantom and in vivo rat experiments, including cardiac imaging and contrast-enhanced perfusion imaging. For comparison, the echo planar imaging (EPI) experiments with orthogonal RF excitation are also performed. The results show that the signal-to-noise ratios of the images acquired by the proposed method can be higher than those obtained with the rFOV EPI. Moreover, the proposed method shows better performance in the cardiac imaging and perfusion imaging of rat kidney, and it can scan one or more regions of interest (ROIs) with high spatial resolution in a single shot. It might be a favorable solution to ultrafast imaging applications in cases with severe susceptibility heterogeneities, such as cardiac imaging and perfusion imaging. Furthermore, it might be promising in applications with separate ROIs, such as mammary and limb imaging. Project supported by the National Natural Science Foundation of China (Grant Nos. 11474236, 81171331, and U1232212).

The male to female ratio at birth in the Republic of Ireland and Northern Ireland: influence of societal stress

PubMed Central

2015-01-01

Introduction Male live births occur slightly in excess of female births. The ratio of male divided by total births is referred to as M/F. Many factors reduce M/F including toxins, stress, and privation, with excess male foetal loss. “The Troubles” (1969-1998) of Northern Ireland (NI) and the economic downturn of Republic of Ireland (ROI) from 2007 posed stresses with corresponding controls. This study analysed M/F in NI and ROI. Methods Annual male and female live births in NI and the ROI were compared using chi tests. Results M/F was significantly higher in NI than in ROI. M/F in NI dropped after 1974. M/F rose in ROI up to 1994, then fell. Discussion Violence-related stress may have been the cause for the M/F drop in NI. Economic improvement followed by recession may have caused parallel M/F changes in ROI. These findings agree with the stress hypothesis of M/F. PMID:26668416

SU-F-J-29: Dosimetric Effect of Image Registration ROI Size and Focus in Automated CBCT Registration for Spine SBRT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Magnelli, A; Smith, A; Chao, S

2016-06-15

Purpose: Spinal stereotactic body radiotherapy (SBRT) involves highly conformal dose distributions and steep dose gradients due to the proximity of the spinal cord to the treatment volume. To achieve the planning goals while limiting the spinal cord dose, patients are setup using kV cone-beam CT (kV-CBCT) with 6 degree corrections. The kV-CBCT registration with the reference CT is dependent on a user selected region of interest (ROI). The objective of this work is to determine the dosimetric impact of ROI selection. Methods: Twenty patients were selected for this study. For each patient, the kV-CBCT was registered to the reference CTmore » using three ROIs including: 1) the external body, 2) a large anatomic region, and 3) a small region focused in the target volume. Following each registration, the aligned CBCTs and contours were input to the treatment planning system for dose evaluation. The minimum dose, dose to 99% and 90% of the tumor volume (D99%, D90%), dose to 0.03cc and the dose to 10% of the spinal cord subvolume (V10Gy) were compared to the planned values. Results: The average deviations in the tumor minimum dose were 2.68%±1.7%, 4.6%±4.0%, 14.82%±9.9% for small, large and the external ROIs, respectively. The average deviations in tumor D99% were 1.15%±0.7%, 3.18%±1.7%, 10.0%±6.6%, respectively. The average deviations in tumor D90% were 1.00%±0.96%, 1.14%±1.05%, 3.19%±4.77% respectively. The average deviations in the maximum dose to the spinal cord were 2.80%±2.56%, 7.58%±8.28%, 13.35%±13.14%, respectively. The average deviation in V10Gy to the spinal cord were 1.69%±0.88%, 1.98%±2.79%, 2.71%±5.63%. Conclusion: When using automated registration algorithms for CBCT-Reference alignment, a small target-focused ROI results in the least dosimetric deviation from the plan. It is recommended to focus narrowly on the target volume to keep the spinal cord dose below tolerance.« less

Evaluation of intrinsic respiratory signal determination methods for 4D CBCT adapted for mice

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martin, Rachael; Pan, Tinsu, E-mail: tpan@mdanderson.org; Rubinstein, Ashley

Purpose: 4D CT imaging in mice is important in a variety of areas including studies of lung function and tumor motion. A necessary step in 4D imaging is obtaining a respiratory signal, which can be done through an external system or intrinsically through the projection images. A number of methods have been developed that can successfully determine the respiratory signal from cone-beam projection images of humans, however only a few have been utilized in a preclinical setting and most of these rely on step-and-shoot style imaging. The purpose of this work is to assess and make adaptions of several successfulmore » methods developed for humans for an image-guided preclinical radiation therapy system. Methods: Respiratory signals were determined from the projection images of free-breathing mice scanned on the X-RAD system using four methods: the so-called Amsterdam shroud method, a method based on the phase of the Fourier transform, a pixel intensity method, and a center of mass method. The Amsterdam shroud method was modified so the sharp inspiration peaks associated with anesthetized mouse breathing could be detected. Respiratory signals were used to sort projections into phase bins and 4D images were reconstructed. Error and standard deviation in the assignment of phase bins for the four methods compared to a manual method considered to be ground truth were calculated for a range of region of interest (ROI) sizes. Qualitative comparisons were additionally made between the 4D images obtained using each of the methods and the manual method. Results: 4D images were successfully created for all mice with each of the respiratory signal extraction methods. Only minimal qualitative differences were noted between each of the methods and the manual method. The average error (and standard deviation) in phase bin assignment was 0.24 ± 0.08 (0.49 ± 0.11) phase bins for the Fourier transform method, 0.09 ± 0.03 (0.31 ± 0.08) phase bins for the modified Amsterdam shroud method, 0.09 ± 0.02 (0.33 ± 0.07) phase bins for the intensity method, and 0.37 ± 0.10 (0.57 ± 0.08) phase bins for the center of mass method. Little dependence on ROI size was noted for the modified Amsterdam shroud and intensity methods while the Fourier transform and center of mass methods showed a noticeable dependence on the ROI size. Conclusions: The modified Amsterdam shroud, Fourier transform, and intensity respiratory signal methods are sufficiently accurate to be used for 4D imaging on the X-RAD system and show improvement over the existing center of mass method. The intensity and modified Amsterdam shroud methods are recommended due to their high accuracy and low dependence on ROI size.« less

SU-D-303-03: Impact of Uncertainty in T1 Measurements On Quantification of Dynamic Contrast Enhanced MRI

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aryal, M; Cao, Y

2015-06-15

Purpose: Quantification of dynamic contrast enhanced (DCE) MRI requires native longitudinal relaxation time (T1) measurement. This study aimed to assess uncertainty in T1 measurements using two different methods. Methods and Materials: Brain MRI scans were performed on a 3T scanner in 9 patients who had low grade/benign tumors and partial brain radiotherapy without chemotherapy at pre-RT, week-3 during RT (wk-3), end-RT, and 1, 6 and 18 months after RT. T1-weighted images were acquired using gradient echo sequences with 1) 2 different flip angles (50 and 150), and 2) 5 variable TRs (100–2000ms). After creating quantitative T1 maps, average T1 wasmore » calculated in regions of interest (ROI), which were distant from tumors and received a total of accumulated radiation doses < 5 Gy at wk-3. ROIs included left and right normal Putamen and Thalamus (gray matter: GM), and frontal and parietal white matter (WM). Since there were no significant or even a trend of T1 changes from pre-RT to wk-3 in these ROIs, a relative repeatability coefficient (RC) of T1 as a measure of uncertainty was estimated in each ROI using the data pre-RT and at wk-3. The individual T1 changes at later time points were evaluated compared to the estimated RCs. Results: The 2-flip angle method produced small RCs in GM (9.7–11.7%) but large RCs in WM (12.2–13.6%) compared to the saturation-recovery (SR) method (11.0–17.7% for GM and 7.5–11.2% for WM). More than 81% of individual T1 changes were within T1 uncertainty ranges defined by RCs. Conclusion: Our study suggests that the impact of T1 uncertainty on physiological parameters derived from DCE MRI is not negligible. A short scan with 2 flip angles is able to achieve repeatability of T1 estimates similar to a long scan with 5 different TRs, and is desirable to be integrated in the DCE protocol. Present study was supported by National Institute of Health (NIH) under grant numbers; UO1 CA183848 and RO1 NS064973.« less

Impact of the definition of peak standardized uptake value on quantification of treatment response.

PubMed

Vanderhoek, Matt; Perlman, Scott B; Jeraj, Robert

2012-01-01

PET-based treatment response assessment typically measures the change in maximum standardized uptake value (SUV(max)), which is adversely affected by noise. Peak SUV (SUV(peak)) has been recommended as a more robust alternative, but its associated region of interest (ROI(peak)) is not uniquely defined. We investigated the impact of different ROI(peak) definitions on quantification of SUV(peak) and tumor response. Seventeen patients with solid malignancies were treated with a multitargeted receptor tyrosine kinase inhibitor resulting in a variety of responses. Using the cellular proliferation marker 3'-deoxy-3'-(18)F-fluorothymidine ((18)F-FLT), whole-body PET/CT scans were acquired at baseline and during treatment. (18)F-FLT-avid lesions (∼2/patient) were segmented on PET images, and tumor response was assessed via the relative change in SUV(peak). For each tumor, 24 different SUV(peaks) were determined by changing ROI(peak) shape (circles vs. spheres), size (7.5-20 mm), and location (centered on SUV(max) vs. placed in highest-uptake region), encompassing different definitions from the literature. Within each tumor, variations in the 24 SUV(peaks) and tumor responses were measured using coefficient of variation (CV), standardized deviation (SD), and range. For each ROI(peak) definition, a population average SUV(peak) and tumor response were determined over all tumors. A substantial variation in both SUV(peak) and tumor response resulted from changing the ROI(peak) definition. The variable ROI(peak) definition led to an intratumor SUV(peak) variation ranging from 49% above to 46% below the mean (CV, 17%) and an intratumor SUV(peak) response variation ranging from 49% above to 35% below the mean (SD, 9%). The variable ROI(peak) definition led to a population average SUV(peak) variation ranging from 24% above to 28% below the mean (CV, 14%) and a population average SUV(peak) response variation ranging from only 3% above to 3% below the mean (SD, 2%). The size of ROI(peak) caused more variation in intratumor response than did the location or shape of ROI(peak). Population average tumor response was independent of size, shape, and location of ROI(peak). Quantification of individual tumor response using SUV(peak) is highly sensitive to the ROI(peak) definition, which can significantly affect the use of SUV(peak) for assessment of treatment response. Clinical trials are necessary to compare the efficacy of SUV(peak) and SUV(max) for quantification of response to therapy.

Dynamic connectivity regression: Determining state-related changes in brain connectivity

PubMed Central

Cribben, Ivor; Haraldsdottir, Ragnheidur; Atlas, Lauren Y.; Wager, Tor D.; Lindquist, Martin A.

2014-01-01

Most statistical analyses of fMRI data assume that the nature, timing and duration of the psychological processes being studied are known. However, often it is hard to specify this information a priori. In this work we introduce a data-driven technique for partitioning the experimental time course into distinct temporal intervals with different multivariate functional connectivity patterns between a set of regions of interest (ROIs). The technique, called Dynamic Connectivity Regression (DCR), detects temporal change points in functional connectivity and estimates a graph, or set of relationships between ROIs, for data in the temporal partition that falls between pairs of change points. Hence, DCR allows for estimation of both the time of change in connectivity and the connectivity graph for each partition, without requiring prior knowledge of the nature of the experimental design. Permutation and bootstrapping methods are used to perform inference on the change points. The method is applied to various simulated data sets as well as to an fMRI data set from a study (N=26) of a state anxiety induction using a socially evaluative threat challenge. The results illustrate the method’s ability to observe how the networks between different brain regions changed with subjects’ emotional state. PMID:22484408

Influence of parameter settings in voxel-based morphometry 8. Using DARTEL and region-of-interest on reproducibility in gray matter volumetry.

PubMed

Goto, M; Abe, O; Aoki, S; Hayashi, N; Miyati, T; Takao, H; Matsuda, H; Yamashita, F; Iwatsubo, T; Mori, H; Kunimatsu, A; Ino, K; Yano, K; Ohtomo, K

2015-01-01

To investigate whether reproducibility of gray matter volumetry is influenced by parameter settings for VBM 8 using Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL) with region-of-interest (ROI) analyses. We prepared three-dimensional T1-weighted magnetic resonance images (3D-T1WIs) of 21 healthy subjects. All subjects were imaged with each of five MRI systems. Voxel-based morphometry 8 (VBM 8) and WFU PickAtlas software were used for gray matter volumetry. The bilateral ROI labels used were those provided as default settings with the software: Frontal Lobe, Hippocampus, Occipital Lobe, Orbital Gyrus, Parietal Lobe, Putamen, and Temporal Lobe. All 3D-T1WIs were segmented to gray matter with six parameters of VBM 8, with each parameter having between three and eight selectable levels. Reproducibility was evaluated as the standard deviation (mm³) of measured values for the five MRI systems. Reproducibility was influenced by 'Bias regularization (BiasR)', 'Bias FWHM', and 'De-noising filter' settings, but not by 'MRF weighting', 'Sampling distance', or 'Warping regularization' settings. Reproducibility in BiasR was influenced by ROI. Superior reproducibility was observed in Frontal Lobe with the BiasR1 setting, and in Hippocampus, Parietal Lobe, and Putamen with the BiasR3*, BiasR1, and BiasR5 settings, respectively. Reproducibility of gray matter volumetry was influenced by parameter settings in VBM 8 using DARTEL and ROI. In multi-center studies, the use of appropriate settings in VBM 8 with DARTEL results in reduced scanner effect.

A hybrid lung and vessel segmentation algorithm for computer aided detection of pulmonary embolism

NASA Astrophysics Data System (ADS)

Raghupathi, Laks; Lakare, Sarang

2009-02-01

Advances in multi-detector technology have made CT pulmonary angiography (CTPA) a popular radiological tool for pulmonary emboli (PE) detection. CTPA provide rich detail of lung anatomy and is a useful diagnostic aid in highlighting even very small PE. However analyzing hundreds of slices is laborious and time-consuming for the practicing radiologist which may also cause misdiagnosis due to the presence of various PE look-alike. Computer-aided diagnosis (CAD) can be a potential second reader in providing key diagnostic information. Since PE occurs only in vessel arteries, it is important to mark this region of interest (ROI) during CAD preprocessing. In this paper, we present a new lung and vessel segmentation algorithm for extracting contrast-enhanced vessel ROI in CTPA. Existing approaches to segmentation either provide only the larger lung area without highlighting the vessels or is computationally prohibitive. In this paper, we propose a hybrid lung and vessel segmentation which uses an initial lung ROI and determines the vessels through a series of refinement steps. We first identify a coarse vessel ROI by finding the "holes" from the lung ROI. We then use the initial ROI as seed-points for a region-growing process while carefully excluding regions which are not relevant. The vessel segmentation mask covers 99% of the 259 PE from a real-world set of 107 CTPA. Further, our algorithm increases the net sensitivity of a prototype CAD system by 5-9% across all PE categories in the training and validation data sets. The average run-time of algorithm was only 100 seconds on a standard workstation.

An image-processing strategy to extract important information suitable for a low-size stimulus pattern in a retinal prosthesis.

PubMed

Chen, Yili; Fu, Jixiang; Chu, Dawei; Li, Rongmao; Xie, Yaoqin

2017-11-27

A retinal prosthesis is designed to help the blind to obtain some sight. It consists of an external part and an internal part. The external part is made up of a camera, an image processor and an RF transmitter. The internal part is made up of an RF receiver, implant chip and microelectrode. Currently, the number of microelectrodes is in the hundreds, and we do not know the mechanism for using an electrode to stimulate the optic nerve. A simple hypothesis is that the pixels in an image correspond to the electrode. The images captured by the camera should be processed by suitable strategies to correspond to stimulation from the electrode. Thus, it is a question of how to obtain the important information from the image captured in the picture. Here, we use the region of interest (ROI), a useful algorithm for extracting the ROI, to retain the important information, and to remove the redundant information. This paper explains the details of the principles and functions of the ROI. Because we are investigating a real-time system, we need a fast processing ROI as a useful algorithm to extract the ROI. Thus, we simplified the ROI algorithm and used it in an outside image-processing digital signal processing (DSP) system of the retinal prosthesis. The results show that our image-processing strategies are suitable for a real-time retinal prosthesis and can eliminate redundant information and provide useful information for expression in a low-size image.

Muscle shear elastic modulus is linearly related to muscle torque over the entire range of isometric contraction intensity.

PubMed

Ateş, Filiz; Hug, François; Bouillard, Killian; Jubeau, Marc; Frappart, Thomas; Couade, Mathieu; Bercoff, Jeremy; Nordez, Antoine

2015-08-01

Muscle shear elastic modulus is linearly related to muscle torque during low-level contractions (<60% of Maximal Voluntary Contraction, MVC). This measurement can therefore be used to estimate changes in individual muscle force. However, it is not known if this relationship remains valid for higher intensities. The aim of this study was to determine: (i) the relationship between muscle shear elastic modulus and muscle torque over the entire range of isometric contraction and (ii) the influence of the size of the region of interest (ROI) used to average the shear modulus value. Ten healthy males performed two incremental isometric little finger abductions. The joint torque produced by Abductor Digiti Minimi was considered as an index of muscle torque and elastic modulus. A high coefficient of determination (R(2)) (range: 0.86-0.98) indicated that the relationship between elastic modulus and torque can be accurately modeled by a linear regression over the entire range (0% to 100% of MVC). The changes in shear elastic modulus as a function of torque were highly repeatable. Lower R(2) values (0.89±0.13 for 1/16 of ROI) and significantly increased absolute errors were observed when the shear elastic modulus was averaged over smaller ROI, half, 1/4 and 1/16 of the full ROI) than the full ROI (mean size: 1.18±0.24cm(2)). It suggests that the ROI should be as large as possible for accurate measurement of muscle shear modulus. Copyright © 2015 Elsevier Ltd. All rights reserved.

What’s Wrong with the Murals at the Mogao Grottoes: A Near-Infrared Hyperspectral Imaging Method

PubMed Central

Sun, Meijun; Zhang, Dong; Wang, Zheng; Ren, Jinchang; Chai, Bolong; Sun, Jizhou

2015-01-01

Although a significant amount of work has been performed to preserve the ancient murals in the Mogao Grottoes by Dunhuang Cultural Research, non-contact methods need to be developed to effectively evaluate the degree of flaking of the murals. In this study, we propose to evaluate the flaking by automatically analyzing hyperspectral images that were scanned at the site. Murals with various degrees of flaking were scanned in the 126th cave using a near-infrared (NIR) hyperspectral camera with a spectral range of approximately 900 to 1700 nm. The regions of interest (ROIs) of the murals were manually labeled and grouped into four levels: normal, slight, moderate, and severe. The average spectral data from each ROI and its group label were used to train our classification model. To predict the degree of flaking, we adopted four algorithms: deep belief networks (DBNs), partial least squares regression (PLSR), principal component analysis with a support vector machine (PCA + SVM) and principal component analysis with an artificial neural network (PCA + ANN). The experimental results show the effectiveness of our method. In particular, better results are obtained using DBNs when the training data contain a significant amount of striping noise. PMID:26394926

Measurement of in vivo local shear modulus using MR elastography multiple-phase patchwork offsets.

PubMed

Suga, Mikio; Matsuda, Tetsuya; Minato, Kotaro; Oshiro, Osamu; Chihara, Kunihiro; Okamoto, Jun; Takizawa, Osamu; Komori, Masaru; Takahashi, Takashi

2003-07-01

Magnetic resonance elastography (MRE) is a method that can visualize the propagating and standing shear waves in an object being measured. The quantitative value of a shear modulus can be calculated by estimating the local shear wavelength. Low-frequency mechanical motion must be used for soft, tissue-like objects because a propagating shear wave rapidly attenuates at a higher frequency. Moreover, a propagating shear wave is distorted by reflections from the boundaries of objects. However, the distortions are minimal around the wave front of the propagating shear wave. Therefore, we can avoid the effect of reflection on a region of interest (ROI) by adjusting the duration of mechanical vibrations. Thus, the ROI is often shorter than the propagating shear wavelength. In the MRE sequence, a motion-sensitizing gradient (MSG) is synchronized with mechanical cyclic motion. MRE images with multiple initial phase offsets can be generated with increasing delays between the MSG and mechanical vibrations. This paper proposes a method for measuring the local shear wavelength using MRE multiple initial phase patchwork offsets that can be used when the size of the object being measured is shorter than the local wavelength. To confirm the reliability of the proposed method, computer simulations, a simulated tissue study and in vitro and in vivo studies were performed.

A cascade method for TFT-LCD defect detection

NASA Astrophysics Data System (ADS)

Yi, Songsong; Wu, Xiaojun; Yu, Zhiyang; Mo, Zhuoya

2017-07-01

In this paper, we propose a novel cascade detection algorithm which focuses on point and line defects on TFT-LCD. At the first step of the algorithm, we use the gray level difference of su-bimage to segment the abnormal area. The second step is based on phase only transform (POT) which corresponds to the Discrete Fourier Transform (DFT), normalized by the magnitude. It can remove regularities like texture and noise. After that, we improve the method of setting regions of interest (ROI) with the method of edge segmentation and polar transformation. The algorithm has outstanding performance in both computation speed and accuracy. It can solve most of the defect detections including dark point, light point, dark line, etc.

Effect of echo artifacts on characterization of pulsatile tissues in neonatal cranial ultrasonic movies

NASA Astrophysics Data System (ADS)

Fukuzawa, Masayuki; Takahashi, Kazuki; Tabata, Yuki; Kitsunezuka, Yoshiki

2016-04-01

Effect of echo artifacts on characterization of pulsatile tissues has been examined in neonatal cranial ultrasonic movies by characterizing pulsatile intensities with different regions of interest (ROIs). The pulsatile tissue, which is a key point in pediatric diagnosis of brain tissue, was detected from a heartbeat-frequency component in Fourier transform of a time-variation of 64 samples of echo intensity at each pixel in a movie fragment. The averages of pulsatile intensity and power were evaluated in two ROIs: common fan-shape and individual cranial-shape. The area of pulsatile region was also evaluated as the number of pixels where the pulsatile intensity exceeds a proper threshold. The extracranial pulsatile region was found mainly in the sections where mirror image was dominant echo artifact. There was significant difference of pulsatile area between two ROIs especially in the specific sections where mirror image was included, suggesting the suitability of cranial-shape ROI for statistical study on pulsatile tissues in brain. The normalized average of pulsatile power in the cranial-shape ROI exhibited most similar tendency to the normalized pulsatile area which was treated as a conventional measure in spite of its requirement of thresholding. It suggests the potential of pulsatile power as an alternative measure for pulsatile area in further statistical study of pulsatile tissues because it was neither affected by echo artifacts nor threshold.

Characterizing region of interest in image using MPEG-7 visual descriptors

NASA Astrophysics Data System (ADS)

Ryu, Min-Sung; Park, Soo-Jun; Won, Chee Sun

2005-08-01

In this paper, we propose a region-based image retrieval system using EHD (Edge Histogram Descriptor) and CLD (Color Layout Descriptor) of MPEG-7 descriptors. The combined descriptor can efficiently describe edge and color features in terms of sub-image regions. That is, the basic unit for the selection of the region-of-interest (ROI) in the image is the sub-image block of the EHD, which corresponds to 16 (i.e., 4x4) non-overlapping image blocks in the image space. This implies that, to have a one-to-one region correspondence between EHD and CLD, we need to take an 8x8 inverse DCT (IDCT) for the CLD. Experimental results show that the proposed retrieval scheme can be used for image retrieval with the ROI based image retrieval for MPEG-7 indexed images.

The MAJORANA DEMONSTRATOR for 0νββ: Current Status and Future Plans

NASA Astrophysics Data System (ADS)

Green, M. P.; Abgrall, N.; Aguayo, E.; Avignone, F. T.; Barabash, A. S.; Bertrand, F. E.; Boswell, M.; Brudanin, V.; Busch, M.; Byram, D.; Caldwell, A. S.; Chan, Y.-D.; Christofferson, C. D.; Combs, D. C.; Cuesta, C.; Detwiler, J. A.; Doe, P. J.; Efremenko, Yu.; Egorov, V.; Ejiri, H.; Elliott, S. R.; Fast, J. E.; Finnerty, P.; Fraenkle, F. M.; Galindo-Uribarri, A.; Giovanetti, G. K.; Goett, J.; Gruszko, J.; Guiseppe, V. E.; Gusev, K.; Hallin, A. L.; Hazama, R.; Hegai, A.; Henning, R.; Hoppe, E. W.; Howard, S.; Howe, M. A.; Keeter, K. J.; Kidd, M. F.; Kochetov, O.; Konovalov, S. I.; Kouzes, R. T.; LaFerriere, B. D.; Leon, J.; Leviner, L. E.; Loach, J. C.; MacMullin, J.; MacMullin, S.; Martin, R. D.; Meijer, S.; Mertens, S.; Nomachi, M.; Orrell, J. L.; O'Shaughnessy, C.; Overman, N. R.; Phillips, D. G.; Poon, A. W. P.; Pushkin, K.; Radford, D. C.; Rager, J.; Rielage, K.; Robertson, R. G. H.; Romero-Romero, E.; Ronquest, M. C.; Schubert, A. G.; Shanks, B.; Shima, T.; Shirchenko, M.; Snavely, K. J.; Snyder, N.; Suriano, A. M.; Thompson, J.; Timkin, V.; Tornow, W.; Trimble, J. E.; Varner, R. L.; Vasilyev, S.; Vetter, K.; Vorren, K.; White, B. R.; Wilkerson, J. F.; Wiseman, C.; Xu, W.; Yakushev, E.; Young, A. R.; Yu, C.-H.; Yumatov, V.

The MAJORANA DEMONSTRATOR will search for neutrinoless-double-beta decay (0νββ) in 76Ge, while establishing the feasibility of a future tonne-scale germanium-based 0νββ experiment, and performing searches for new physics beyond the Standard Model. The experiment, currently under construction at the Sanford Underground Research Facility in Lead, SD, will consist of a pair of modular high-purity germanium detector arrays housed inside of a compact copper, lead, and polyethylene shield. Through a combination of strict materials qualifications and assay, low-background design, and powerful background rejection techniques, the Demonstrator aims to achieve a background rate in the 0νββ region of interest (ROI) of no more than 3 counts in the 0νββ-decay ROI per tonne of target isotope per year (cnts/(ROI-t-y)). The current status of the Demonstrator is discussed, as are plans for its completion.

Development of a software for quantitative evaluation radiotherapy target and organ-at-risk segmentation comparison.

PubMed

Kalpathy-Cramer, Jayashree; Awan, Musaddiq; Bedrick, Steven; Rasch, Coen R N; Rosenthal, David I; Fuller, Clifton D

2014-02-01

Modern radiotherapy requires accurate region of interest (ROI) inputs for plan optimization and delivery. Target delineation, however, remains operator-dependent and potentially serves as a major source of treatment delivery error. In order to optimize this critical, yet observer-driven process, a flexible web-based platform for individual and cooperative target delineation analysis and instruction was developed in order to meet the following unmet needs: (1) an open-source/open-access platform for automated/semiautomated quantitative interobserver and intraobserver ROI analysis and comparison, (2) a real-time interface for radiation oncology trainee online self-education in ROI definition, and (3) a source for pilot data to develop and validate quality metrics for institutional and cooperative group quality assurance efforts. The resultant software, Target Contour Testing/Instructional Computer Software (TaCTICS), developed using Ruby on Rails, has since been implemented and proven flexible, feasible, and useful in several distinct analytical and research applications.

Momentum-based morphometric analysis with application to Parkinson's disease

NASA Astrophysics Data System (ADS)

Chen, Jingyun; Khan, Ali R.; McKeown, Martin J.; Beg, Mirza F.

2011-03-01

We apply the initial momentum shape representation of diffeomorphic metric mapping from a template region of interest (ROI) to a given ROI as a morphometic marker in Parkinson's disease. We used a three-step segmentation-registrationmomentum process to derive feature vectors from ROIs in a group of 42 subjects consisting of 19 Parkinson's Disease (PD) subjects and 23 normal control (NC) subjects. Significant group differences between PD and NC subjects were detected in four basal ganglia structures including the caudate, putamen, thalamus and globus pallidus. The magnitude of regionally significant between-group differences detected ranged between 34-75%. Visualization of the different structural deformation pattern between-groups revealed that some parts of basal ganglia structure actually hypertrophy, presumably as a compensatory response to more widespread atrophy. Our results of both hypertrophy and atrophy in the same structures further demonstrate the importance of morphological measures as opposed to overall volume in the assessment of neurodegenerative disease.

Optimization of a Multi-Stage ATR System for Small Target Identification

NASA Technical Reports Server (NTRS)

Lin, Tsung-Han; Lu, Thomas; Braun, Henry; Edens, Western; Zhang, Yuhan; Chao, Tien- Hsin; Assad, Christopher; Huntsberger, Terrance

2010-01-01

An Automated Target Recognition system (ATR) was developed to locate and target small object in images and videos. The data is preprocessed and sent to a grayscale optical correlator (GOC) filter to identify possible regionsof- interest (ROIs). Next, features are extracted from ROIs based on Principal Component Analysis (PCA) and sent to neural network (NN) to be classified. The features are analyzed by the NN classifier indicating if each ROI contains the desired target or not. The ATR system was found useful in identifying small boats in open sea. However, due to "noisy background," such as weather conditions, background buildings, or water wakes, some false targets are mis-classified. Feedforward backpropagation and Radial Basis neural networks are optimized for generalization of representative features to reduce false-alarm rate. The neural networks are compared for their performance in classification accuracy, classifying time, and training time.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dolly, S; University of Missouri, Columbia, MO; Chen, H

Purpose: Local noise power spectrum (NPS) properties are significantly affected by calculation variables and CT acquisition and reconstruction parameters, but a thoughtful analysis of these effects is absent. In this study, we performed a complete analysis of the effects of calculation and imaging parameters on the NPS. Methods: The uniformity module of a Catphan phantom was scanned with a Philips Brilliance 64-slice CT simulator using various scanning protocols. Images were reconstructed using both FBP and iDose4 reconstruction algorithms. From these images, local NPS were calculated for regions of interest (ROI) of varying locations and sizes, using four image background removalmore » methods. Additionally, using a predetermined ground truth, NPS calculation accuracy for various calculation parameters was compared for computer simulated ROIs. A complete analysis of the effects of calculation, acquisition, and reconstruction parameters on the NPS was conducted. Results: The local NPS varied with ROI size and image background removal method, particularly at low spatial frequencies. The image subtraction method was the most accurate according to the computer simulation study, and was also the most effective at removing low frequency background components in the acquired data. However, first-order polynomial fitting using residual sum of squares and principle component analysis provided comparable accuracy under certain situations. Similar general trends were observed when comparing the NPS for FBP to that of iDose4 while varying other calculation and scanning parameters. However, while iDose4 reduces the noise magnitude compared to FBP, this reduction is spatial-frequency dependent, further affecting NPS variations at low spatial frequencies. Conclusion: The local NPS varies significantly depending on calculation parameters, image acquisition parameters, and reconstruction techniques. Appropriate local NPS calculation should be performed to capture spatial variations of noise; calculation methodology should be selected with consideration of image reconstruction effects and the desired purpose of CT simulation for radiotherapy tasks.« less

An experimental extrapolation technique using the Gafchromic EBT3 film for relative output factor measurements in small x-ray fields

DOE Office of Scientific and Technical Information (OSTI.GOV)

Morales, Johnny E., E-mail: johnny.morales@lh.org.

Purpose: An experimental extrapolation technique is presented, which can be used to determine the relative output factors for very small x-ray fields using the Gafchromic EBT3 film. Methods: Relative output factors were measured for the Brainlab SRS cones ranging in diameters from 4 to 30 mm{sup 2} on a Novalis Trilogy linear accelerator with 6 MV SRS x-rays. The relative output factor was determined from an experimental reducing circular region of interest (ROI) extrapolation technique developed to remove the effects of volume averaging. This was achieved by scanning the EBT3 film measurements with a high scanning resolution of 1200 dpi.more » From the high resolution scans, the size of the circular regions of interest was varied to produce a plot of relative output factors versus area of analysis. The plot was then extrapolated to zero to determine the relative output factor corresponding to zero volume. Results: Results have shown that for a 4 mm field size, the extrapolated relative output factor was measured as a value of 0.651 ± 0.018 as compared to 0.639 ± 0.019 and 0.633 ± 0.021 for 0.5 and 1.0 mm diameter of analysis values, respectively. This showed a change in the relative output factors of 1.8% and 2.8% at these comparative regions of interest sizes. In comparison, the 25 mm cone had negligible differences in the measured output factor between zero extrapolation, 0.5 and 1.0 mm diameter ROIs, respectively. Conclusions: This work shows that for very small fields such as 4.0 mm cone sizes, a measureable difference can be seen in the relative output factor based on the circular ROI and the size of the area of analysis using radiochromic film dosimetry. The authors recommend to scan the Gafchromic EBT3 film at a resolution of 1200 dpi for cone sizes less than 7.5 mm and to utilize an extrapolation technique for the output factor measurements of very small field dosimetry.« less

Influence of ROI definition on the heart-to-mediastinum ratio in planar 123I-MIBG imaging.

PubMed

Klene, Christiane; Jungen, Christiane; Okuda, Koichi; Kobayashi, Yuske; Helberg, Annabelle; Mester, Janos; Meyer, Christian; Nakajima, Kenichi

2018-02-01

Iodine-123-metaiodobenzylguanidine ( 123 I-MIBG) imaging with estimation of the heart-to-mediastinum ratio (HMR) has been established for risk assessment in patients with chronic heart failure. Our aim was to evaluate the effect of different methods of ROI definition on the renderability of HMR to normal or decreased sympathetic innervation. The results of three different methods of ROI definition (clinical routine (CLI), simple standardization (STA), and semi-automated (AUT) were compared. Ranges of 95% limits of agreement (LoA) of inter-observer variabilities were 0.28 and 0.13 for STA and AUT, respectively. Considering a HMR of 1.60 as the lower limit of normal, 13 of 32 (41%) for method STA and 5 of 32 (16%) for method AUT of all HMR measurements could not be classified to normal or pathologic. Ranges of 95% LoA of inter-method variabilities were 0.72 for CLI vs AUT, 0.65 for CLI vs STA, and 0.31 for STA vs AUT. Different methods of ROI definition result in different ranges of the LoA of the measured HMR with relevance for rendering the results to normal or pathological innervation. We could demonstrate that standardized protocols can help keep methodological variabilities limited, narrowing the gray zone of renderability.

Centroids evaluation of the images obtained with the conical null-screen corneal topographer

NASA Astrophysics Data System (ADS)

Osorio-Infante, Arturo I.; Armengol-Cruz, Victor de Emanuel; Campos-García, Manuel; Cossio-Guerrero, Cesar; Marquez-Flores, Jorge; Díaz-Uribe, José Rufino

2016-09-01

In this work, we propose some algorithms to recover the centroids of the resultant image obtained by a conical nullscreen based corneal topographer. With these algorithms, we obtain the region of interest (roi) of the original image and using an image-processing algorithm, we calculate the geometric centroid of each roi. In order to improve our algorithm performance, we use different settings of null-screen targets, changing their size and number. We also improved the illumination system to avoid inhomogeneous zones in the corneal images. Finally, we report some corneal topographic measurements with the best setting we found.

Efficient mining of association rules for the early diagnosis of Alzheimer's disease

NASA Astrophysics Data System (ADS)

Chaves, R.; Górriz, J. M.; Ramírez, J.; Illán, I. A.; Salas-Gonzalez, D.; Gómez-Río, M.

2011-09-01

In this paper, a novel technique based on association rules (ARs) is presented in order to find relations among activated brain areas in single photon emission computed tomography (SPECT) imaging. In this sense, the aim of this work is to discover associations among attributes which characterize the perfusion patterns of normal subjects and to make use of them for the early diagnosis of Alzheimer's disease (AD). Firstly, voxel-as-feature-based activation estimation methods are used to find the tridimensional activated brain regions of interest (ROIs) for each patient. These ROIs serve as input to secondly mine ARs with a minimum support and confidence among activation blocks by using a set of controls. In this context, support and confidence measures are related to the proportion of functional areas which are singularly and mutually activated across the brain. Finally, we perform image classification by comparing the number of ARs verified by each subject under test to a given threshold that depends on the number of previously mined rules. Several classification experiments were carried out in order to evaluate the proposed methods using a SPECT database that consists of 41 controls (NOR) and 56 AD patients labeled by trained physicians. The proposed methods were validated by means of the leave-one-out cross validation strategy, yielding up to 94.87% classification accuracy, thus outperforming recent developed methods for computer aided diagnosis of AD.

Automatic registration of ICG images using mutual information and perfusion analysis

NASA Astrophysics Data System (ADS)

Kim, Namkug; Seo, Jong-Mo; Lee, June-goo; Kim, Jong Hyo; Park, Kwangsuk; Yu, Hyeong-Gon; Yu, Young Suk; Chung, Hum

2005-04-01

Introduction: Indocyanin green fundus angiographic images (ICGA) of the eyes is useful method in detecting and characterizing the choroidal neovascularization (CNV), which is the major cause of the blindness over 65 years of age. To investigate the quantitative analysis of the blood flow on ICGA, systematic approach for automatic registration of using mutual information and a quantitative analysis was developed. Methods: Intermittent sequential images of indocyanin green angiography were acquired by Heidelberg retinal angiography that uses the laser scanning system for the image acquisition. Misalignment of the each image generated by the minute eye movement of the patients was corrected by the mutual information method because the distribution of the contrast media on image is changing throughout the time sequences. Several region of interest (ROI) were selected by a physician and the intensities of the selected region were plotted according to the time sequences. Results: The registration of ICGA time sequential images is required not only translate transform but also rotational transform. Signal intensities showed variation based on gamma-variate function depending on ROIs and capillary vessels show more variance of signal intensity than major vessels. CNV showed intermediate variance of signal intensity and prolonged transit time. Conclusion: The resulting registered images can be used not only for quantitative analysis, but also for perfusion analysis. Various investigative approached on CNV using this method will be helpful in the characterization of the lesion and follow-up.

Determination of multiple sclerosis plaque size with diffusion-tensor MR Imaging: comparison study with healthy volunteers.

PubMed

Kealey, Susan M; Kim, Youngjoo; Whiting, Wythe L; Madden, David J; Provenzale, James M

2005-08-01

To use diffusion-tensor magnetic resonance (MR) imaging to measure involvement of normal-appearing white matter (WM) immediately adjacent to multiple sclerosis (MS) plaques and thus redefine actual plaque size on diffusion-tensor images through comparison with T2-weighted images of equivalent areas in healthy volunteers. Informed consent was not required given the retrospective nature of the study on an anonymized database. The study complied with requirements of the Health Insurance Portability and Accountability Act. Twelve patients with MS (four men, eight women; mean age, 35 years) and 14 healthy volunteers (six men, eight women; mean age, 25 years) were studied. The authors obtained fractional anisotropy (FA) values in MS plaques and in the adjacent normal-appearing WM in patients with MS and in equivalent areas in healthy volunteers. They placed regions of interest (ROIs) around the periphery of plaques and defined the total ROIs (ie, plaques plus peripheral ROIs) as abnormal if their mean FA values were at least 2 standard deviations below those of equivalent ROIs within equivalent regions in healthy volunteers. The combined area of the plaque and the peripheral ROI was compared with the area of the plaque seen on T2-weighted MR images by means of a Student paired t test (P = .05). The mean plaque size on T2-weighted images was 72 mm2 +/- 21 (standard deviation). The mean plaque FA value was 0.285 +/- 0.088 (0.447 +/- 0.069 in healthy volunteers [P < .001]; mean percentage reduction in FA in MS plaques, 37%). The mean plaque size on FA maps was 91 mm2 +/- 35, a mean increase of 127% compared with the size of the original plaque on T2-weighted images (P = .03). A significant increase in plaque size was seen when normal-appearing WM was interrogated with diffusion-tensor MR imaging. This imaging technique may represent a more sensitive method of assessing disease burden and may have a future role in determining disease burden and activity.

SU-F-J-58: Evaluation of RayStation Hybrid Deformable Image Registration for Accurate Contour Propagation in Adaptive Planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rong, Y; Rao, S; Daly, M

Purpose: Adaptive radiotherapy requires complete new sets of regions of interests (ROIs) delineation on the mid-treatment CT images. This work aims at evaluating the accuracy of the RayStation hybrid deformable image registration (DIR) algorithm for its overall integrity and accuracy in contour propagation for adaptive planning. Methods: The hybrid DIR is based on the combination of intensity-based algorithm and anatomical information provided by contours. Patients who received mid-treatment CT scans were identified for the study, including six lung patients (two mid-treatment CTs) and six head-and-neck (HN) patients (one mid-treatment CT). DIRpropagated ROIs were compared with physician-drawn ROIs for 8 ITVsmore » and 7 critical organs (lungs, heart, esophagus, and etc.) for the lung patients, as well as 14 GTVs and 20 critical organs (mandible, eyes, parotids, and etc.) for the HN patients. Volume difference, center of mass (COM) difference, and Dice index were used for evaluation. Clinical-relevance of each propagated ROI was scored by two physicians, and correlated with the Dice index. Results: For critical organs, good agreement (Dice>0.9) were seen on all 7 for lung patients and 13 out of 20 for HN patients, with the rest requiring minimal edits. For targets, COM differences were within 5 mm on average for all patients. For Lung, 5 out of 8 ITVs required minimal edits (Dice 0.8–0.9), with the rest 2 needed re-drawn due to their small volumes (<10 cc). However, the propagated HN GTVs resulted in relatively low Dice values (0.5–0.8) due to their small volumes (3–40 cc) and high variability, among which 2 required re-drawn due to new nodal target identified on the mid-treatment CT scans. Conclusion: The hybrid DIR algorithm was found to be clinically useful and efficient for lung and HN patients, especially for propagated critical organ ROIs. It has potential to significantly improve the workflow in adaptive planning.« less

SU-E-J-113: Effects of Deformable Registration On First-Order Texture Maps Calculated From Thoracic Lung CT Scans

DOE Office of Scientific and Technical Information (OSTI.GOV)

Smith, C; Cunliffe, A; Al-Hallaq, H

Purpose: To determine the stability of eight first-order texture features following the deformable registration of serial computed tomography (CT) scans. Methods: CT scans at two different time points from 10 patients deemed to have no lung abnormalities by a radiologist were collected. Following lung segmentation using an in-house program, texture maps were calculated from 32×32-pixel regions of interest centered at every pixel in the lungs. The texture feature value of the ROI was assigned to the center pixel of the ROI in the corresponding location of the texture map. Pixels in the square ROI not contained within the segmented lungmore » were not included in the calculation. To quantify the agreement between ROI texture features in corresponding pixels of the baseline and follow-up texture maps, the Fraunhofer MEVIS EMPIRE10 deformable registration algorithm was used to register the baseline and follow-up scans. Bland-Altman analysis was used to compare registered scan pairs by computing normalized bias (nBias), defined as the feature value change normalized to the mean feature value, and normalized range of agreement (nRoA), defined as the range spanned by the 95% limits of agreement normalized to the mean feature value. Results: Each patient’s scans contained between 6.8–15.4 million ROIs. All of the first-order features investigated were found to have an nBias value less than 0.04% and an nRoA less than 19%, indicating that the variability introduced by deformable registration was low. Conclusion: The eight first-order features investigated were found to be registration stable. Changes in CT texture maps could allow for temporal-spatial evaluation of the evolution of lung abnormalities relating to a variety of diseases on a patient-by-patient basis. SGA and HA receives royalties and licensing fees through the University of Chicago for computer-aided diagnosis technology. Research reported in this publication was supported by the National Institute Of General Medical Sciences of the National Institutes of Health under Award Number R25GM109439.« less

Focal Hemodynamic Responses in the Stimulated Hemisphere During High-Definition Transcranial Direct Current Stimulation.

PubMed

Muthalib, Makii; Besson, Pierre; Rothwell, John; Perrey, Stéphane

2017-07-17

High-definition transcranial direct current stimulation (HD-tDCS) using a 4 × 1 electrode montage has been previously shown using modeling and physiological studies to constrain the electric field within the spatial extent of the electrodes. The aim of this proof-of-concept study was to determine if functional near-infrared spectroscopy (fNIRS) neuroimaging can be used to determine a hemodynamic correlate of this 4 × 1 HD-tDCS electric field on the brain. In a three session cross-over study design, 13 healthy males received one sham (2 mA, 30 sec) and two real (HD-tDCS-1 and HD-tDCS-2, 2 mA, 10 min) anodal HD-tDCS targeting the left M1 via a 4 × 1 electrode montage (anode on C3 and 4 return electrodes 3.5 cm from anode). The two real HD-tDCS sessions afforded a within-subject replication of the findings. fNIRS was used to measure changes in brain hemodynamics (oxygenated hemoglobin integral-O 2 Hb int ) during each 10 min session from two regions of interest (ROIs) in the stimulated left hemisphere that corresponded to "within" (L in ) and "outside" (L out ) the spatial extent of the 4 × 1 electrode montage, and two corresponding ROIs (R in and R out ) in the right hemisphere. The ANOVA showed that both real anodal HD-tDCS compared to sham induced a significantly greater O 2 Hb int in the L in than L out ROIs of the stimulated left hemisphere; while there were no significant differences between the real and sham sessions for the right hemisphere ROIs. Intra-class correlation coefficients showed "fair-to-good" reproducibility for the left stimulated hemisphere ROIs. The greater O 2 Hb int "within" than "outside" the spatial extent of the 4 × 1 electrode montage represents a hemodynamic correlate of the electrical field distribution, and thus provides a prospective reliable method to determine the dose of stimulation that is necessary to optimize HD-tDCS parameters in various applications. © 2017 International Neuromodulation Society.

Cost and Return on Investment of a Work-Family Intervention in the Extended Care Industry: Evidence From the Work, Family, and Health Network.

PubMed

Dowd, William N; Bray, Jeremy W; Barbosa, Carolina; Brockwood, Krista; Kaiser, David J; Mills, Michael J; Hurtado, David A; Wipfli, Brad

2017-10-01

To estimate the cost and return on investment (ROI) of an intervention targeting work-family conflict (WFC) in the extended care industry. Costs to deliver the intervention during a group-randomized controlled trial were estimated, and data on organizational costs-presenteeism, health care costs, voluntary termination, and sick time-were collected from interviews and administrative data. Generalized linear models were used to estimate the intervention's impact on organizational costs. Combined, these results produced ROI estimates. A cluster-robust confidence interval (CI) was estimated around the ROI estimate. The per-participant cost of the intervention was $767. The ROI was -1.54 (95% CI: -4.31 to 2.18). The intervention was associated with a $668 reduction in health care costs (P < 0.05). This paper builds upon and expands prior ROI estimation methods to a new setting.

Recent advances in patterned photostimulation for optogenetics

NASA Astrophysics Data System (ADS)

Ronzitti, Emiliano; Ventalon, Cathie; Canepari, Marco; Forget, Benoît C.; Papagiakoumou, Eirini; Emiliani, Valentina

2017-11-01

An important technological revolution is underway in the field of neuroscience as we begin the 21st century. The combination of optical methods with genetically encoded photosensitive tools (optogenetics) offers the opportunity to quickly modulate and monitor a large number of neuronal events and the ability to recreate the physiological, spatial, and temporal patterns of brain activity. The use of light instead of electrical stimulation is less invasive, and permits superior spatial and temporal specificity and flexibility. This ongoing revolution has motivated the development of new optical methods for light stimulation. They can be grouped in two main categories: scanning and parallel photostimulation techniques, each with its advantages and limitations. In scanning approaches, a small light spot is displaced in targeted regions of interest (ROIs), using galvanometric mirrors or acousto-optic deflectors, whereas in parallel approaches, the light beam can be spatially shaped to simultaneously cover all ROIs by modulating either the light intensity or the phase of the illumination beam. With amplitude modulation, light patterns are created by selectively blocking light rays that illuminate regions of no interest, while with phase modulation, the wavefront of the light beam is locally modified so that light rays are directed onto the target, thus allowing for higher intensity efficiency. In this review, we will describe the principle of each of these photostimulation techniques and review the use of these approaches in optogenetics experiments by presenting their advantages and drawbacks. Finally, we will review the challenges that need to be faced when photostimulation methods are combined with two-photon imaging approaches to reach an all-optical brain control through optogenetics and functional reporters (Ca2+ and voltage indicators).

Economic Evidence for U.S. Asthma Self-Management Education and Home-Based Interventions

PubMed Central

Hsu, Joy; Wilhelm, Natalie; Lewis, Lillianne; Herman, Elizabeth

2016-01-01

The health and economic burden of asthma in the United States is substantial. Asthma self-management education (AS-ME) and home-based interventions for asthma can improve asthma control and prevent asthma exacerbations, and interest in health care-public health collaboration regarding asthma is increasing. However, outpatient AS-ME and home-based asthma intervention programs are not widely available; economic sustainability is a common concern. Thus, we conducted a narrative review of existing literature regarding economic outcomes of outpatient AS-ME and home-based intervention programs for asthma in the United States. We identified 9 outpatient AS-ME programs and 17 home-based intervention programs with return on investment (ROI) data. Most programs were associated with a positive ROI; a few programs observed positive ROIs only among selected populations (e.g., higher health care utilization). Interpretation of existing data is limited by heterogeneous ROI calculations. Nevertheless, the literature suggests promise for sustainable opportunities to expand access to outpatient AS-ME and home-based asthma intervention programs in the United States. More definitive knowledge about how to maximize program benefit and sustainability could be gained through more controlled studies of specific populations and increased uniformity in economic assessments. PMID:27658535

The neuroeconomics of nicotine dependence: a preliminary functional magnetic resonance imaging study of delay discounting of monetary and cigarette rewards in smokers.

PubMed

MacKillop, James; Amlung, Michael T; Wier, Lauren M; David, Sean P; Ray, Lara A; Bickel, Warren K; Sweet, Lawrence H

2012-04-30

Neuroeconomics integrates behavioral economics and cognitive neuroscience to understand the neurobiological basis for normative and maladaptive decision making. Delay discounting is a behavioral economic index of impulsivity that reflects capacity to delay gratification and has been consistently associated with nicotine dependence. This preliminary study used functional magnetic resonance imaging to examine delay discounting for money and cigarette rewards in 13 nicotine dependent adults. Significant differences between preferences for smaller immediate rewards and larger delayed rewards were evident in a number of regions of interest (ROIs), including the medial prefrontal cortex, anterior insular cortex, middle temporal gyrus, middle frontal gyrus, and cingulate gyrus. Significant differences between money and cigarette rewards were generally lateralized, with cigarette choices associated with left hemisphere activation and money choices associated with right hemisphere activation. Specific ROI differences included the posterior parietal cortex, medial and middle frontal gyrus, ventral striatum, temporoparietal cortex, and angular gyrus. Impulsivity as measured by behavioral choices was significantly associated with both individual ROIs and a combined ROI model. These findings provide initial evidence in support of applying a neuroeconomic approach to understanding nicotine dependence. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Privacy-Aware Image Encryption Based on Logistic Map and Data Hiding

NASA Astrophysics Data System (ADS)

Sun, Jianglin; Liao, Xiaofeng; Chen, Xin; Guo, Shangwei

The increasing need for image communication and storage has created a great necessity for securely transforming and storing images over a network. Whereas traditional image encryption algorithms usually consider the security of the whole plain image, region of interest (ROI) encryption schemes, which are of great importance in practical applications, protect the privacy regions of plain images. Existing ROI encryption schemes usually adopt approximate techniques to detect the privacy region and measure the quality of encrypted images; however, their performance is usually inconsistent with a human visual system (HVS) and is sensitive to statistical attacks. In this paper, we propose a novel privacy-aware ROI image encryption (PRIE) scheme based on logistical mapping and data hiding. The proposed scheme utilizes salient object detection to automatically, adaptively and accurately detect the privacy region of a given plain image. After private pixels have been encrypted using chaotic cryptography, the significant bits are embedded into the nonprivacy region of the plain image using data hiding. Extensive experiments are conducted to illustrate the consistency between our automatic ROI detection and HVS. Our experimental results also demonstrate that the proposed scheme exhibits satisfactory security performance.

CT dose minimization using personalized protocol optimization and aggressive bowtie

NASA Astrophysics Data System (ADS)

Wang, Hui; Yin, Zhye; Jin, Yannan; Wu, Mingye; Yao, Yangyang; Tao, Kun; Kalra, Mannudeep K.; De Man, Bruno

2016-03-01

In this study, we propose to use patient-specific x-ray fluence control to reduce the radiation dose to sensitive organs while still achieving the desired image quality (IQ) in the region of interest (ROI). The mA modulation profile is optimized view by view, based on the sensitive organs and the ROI, which are obtained from an ultra-low-dose volumetric CT scout scan [1]. We use a clinical chest CT scan to demonstrate the feasibility of the proposed concept: the breast region is selected as the sensitive organ region while the cardiac region is selected as IQ ROI. Two groups of simulations are performed based on the clinical CT dataset: (1) a constant mA scan adjusted based on the patient attenuation (120 kVp, 300 mA), which serves as baseline; (2) an optimized scan with aggressive bowtie and ROI centering combined with patient-specific mA modulation. The results shows that the combination of the aggressive bowtie and the optimized mA modulation can result in 40% dose reduction in the breast region, while the IQ in the cardiac region is maintained. More generally, this paper demonstrates the general concept of using a 3D scout scan for optimal scan planning.

Semiautomatic segmentation of liver metastases on volumetric CT images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yan, Jiayong; Schwartz, Lawrence H.; Zhao, Binsheng, E-mail: bz2166@cumc.columbia.edu

2015-11-15

Purpose: Accurate segmentation and quantification of liver metastases on CT images are critical to surgery/radiation treatment planning and therapy response assessment. To date, there are no reliable methods to perform such segmentation automatically. In this work, the authors present a method for semiautomatic delineation of liver metastases on contrast-enhanced volumetric CT images. Methods: The first step is to manually place a seed region-of-interest (ROI) in the lesion on an image. This ROI will (1) serve as an internal marker and (2) assist in automatically identifying an external marker. With these two markers, lesion contour on the image can be accuratelymore » delineated using traditional watershed transformation. Density information will then be extracted from the segmented 2D lesion and help determine the 3D connected object that is a candidate of the lesion volume. The authors have developed a robust strategy to automatically determine internal and external markers for marker-controlled watershed segmentation. By manually placing a seed region-of-interest in the lesion to be delineated on a reference image, the method can automatically determine dual threshold values to approximately separate the lesion from its surrounding structures and refine the thresholds from the segmented lesion for the accurate segmentation of the lesion volume. This method was applied to 69 liver metastases (1.1–10.3 cm in diameter) from a total of 15 patients. An independent radiologist manually delineated all lesions and the resultant lesion volumes served as the “gold standard” for validation of the method’s accuracy. Results: The algorithm received a median overlap, overestimation ratio, and underestimation ratio of 82.3%, 6.0%, and 11.5%, respectively, and a median average boundary distance of 1.2 mm. Conclusions: Preliminary results have shown that volumes of liver metastases on contrast-enhanced CT images can be accurately estimated by a semiautomatic segmentation method.« less

Population based MRI and DTI templates of the adult ferret brain and tools for voxelwise analysis.

PubMed

Hutchinson, E B; Schwerin, S C; Radomski, K L; Sadeghi, N; Jenkins, J; Komlosh, M E; Irfanoglu, M O; Juliano, S L; Pierpaoli, C

2017-05-15

Non-invasive imaging has the potential to play a crucial role in the characterization and translation of experimental animal models to investigate human brain development and disorders, especially when employed to study animal models that more accurately represent features of human neuroanatomy. The purpose of this study was to build and make available MRI and DTI templates and analysis tools for the ferret brain as the ferret is a well-suited species for pre-clinical MRI studies with folded cortical surface, relatively high white matter volume and body dimensions that allow imaging with pre-clinical MRI scanners. Four ferret brain templates were built in this study - in-vivo MRI and DTI and ex-vivo MRI and DTI - using brain images across many ferrets and region of interest (ROI) masks corresponding to established ferret neuroanatomy were generated by semi-automatic and manual segmentation. The templates and ROI masks were used to create a web-based ferret brain viewing software for browsing the MRI and DTI volumes with annotations based on the ROI masks. A second objective of this study was to provide a careful description of the imaging methods used for acquisition, processing, registration and template building and to demonstrate several voxelwise analysis methods including Jacobian analysis of morphometry differences between the female and male brain and bias-free identification of DTI abnormalities in an injured ferret brain. The templates, tools and methodological optimization presented in this study are intended to advance non-invasive imaging approaches for human-similar animal species that will enable the use of pre-clinical MRI studies for understanding and treating brain disorders. Published by Elsevier Inc.

Automatic determination of the artery vein ratio in retinal images

NASA Astrophysics Data System (ADS)

Niemeijer, Meindert; van Ginneken, Bram; Abràmoff, Michael D.

2010-03-01

A lower ratio between the width of the arteries and veins (Arteriolar-to-Venular diameter Ratio, AVR) on the retina, is well established to be predictive of stroke and other cardiovascular events in adults, as well as an increased risk of retinopathy of prematurity in premature infants. This work presents an automatic method that detects the location of the optic disc, determines the appropriate region of interest (ROI), classifies the vessels in the ROI into arteries and veins, measures their widths and calculates the AVR. After vessel segmentation and vessel width determination the optic disc is located and the system eliminates all vessels outside the AVR measurement ROI. The remaining vessels are thinned, vessel crossing and bifurcation points are removed leaving a set of vessel segments containing centerline pixels. Features are extracted from each centerline pixel that are used to assign them a soft label indicating the likelihood the pixel is part of a vein. As all centerline pixels in a connected segment should be the same type, the median soft label is assigned to each centerline pixel in the segment. Next artery vein pairs are matched using an iterative algorithm and the widths of the vessels is used to calculate the AVR. We train and test the algorithm using a set of 25 high resolution digital color fundus photographs a reference standard that indicates for the major vessels in the images whether they are an artery or a vein. We compared the AVR values produced by our system with those determined using a computer assisted method in 15 high resolution digital color fundus photographs and obtained a correlation coefficient of 0.881.

eBits: Compact stream of mesh refinements for remote visualization

DOE PAGES

Sati, Mukul; Lindstrom, Peter; Rossignac, Jarek

2016-05-12

Here, we focus on applications where a remote client needs to visualize or process a complex, manifold triangle mesh, M, but only in a relatively small, user controlled, Region of Interest (RoI) at a time. The client first downloads a coarse base mesh, pre-computed on the server via a series of simplification passes on M, one per Level of Detail (LoD), each pass identifying an independent set of triangles, collapsing them, and, for each collapse, storing, in a Vertex Expansion Record (VER), the information needed to reverse the collapse. On each client initiated RoI modification request, the server pushes tomore » the client a selected subset of these VERs, which, when decoded and applied to refine the mesh locally, ensure that the portion in the RoI is always at full resolution. The eBits approach proposed here offers state of the art compression ratios (using less than 2.5 bits per new full resolution RoI triangle when the RoI has more than 2000 vertices to transmit the connectivity for the selective refinements) and fine-grain control (allowing the user to adjust the RoI by small increments). The effectiveness of eBits results from several novel ideas and novel variations of previous solutions. We represent the VERs using persistent labels so that they can be applied in different orders within a given LoD. The server maintains a shadow copy of the client’s mesh. To avoid sending IDs identifying which vertices should be expanded, we either transmit, for each new vertex, a compact encoding of its death tag–the LoD at which it will be expanded if it lies in the Rol–or transmit vertex masks for the RoI and its neighboring vertices. We also propose a three-step simplification that reduces the overall transmission cost by increasing both the simplification effectiveness and the regularity of the valences in the resulting meshes.« less

Lung texture in serial thoracic CT scans: correlation with radiologist-defined severity of acute changes following radiation therapy

NASA Astrophysics Data System (ADS)

Cunliffe, Alexandra R.; Armato, Samuel G., III; Straus, Christopher; Malik, Renuka; Al-Hallaq, Hania A.

2014-09-01

This study examines the correlation between the radiologist-defined severity of normal tissue damage following radiation therapy (RT) for lung cancer treatment and a set of mathematical descriptors of computed tomography (CT) scan texture (‘texture features’). A pre-therapy CT scan and a post-therapy CT scan were retrospectively collected under IRB approval for each of the 25 patients who underwent definitive RT (median dose: 66 Gy). Sixty regions of interest (ROIs) were automatically identified in the non-cancerous lung tissue of each post-therapy scan. A radiologist compared post-therapy scan ROIs with pre-therapy scans and categorized each as containing no abnormality, mild abnormality, moderate abnormality, or severe abnormality. Twenty texture features that characterize gray-level intensity, region morphology, and gray-level distribution were calculated in post-therapy scan ROIs and compared with anatomically matched ROIs in the pre-therapy scan. Linear regression and receiver operating characteristic (ROC) analysis were used to compare the percent feature value change (ΔFV) between ROIs at each category of visible radiation damage. Most ROIs contained no (65%) or mild abnormality (30%). ROIs with moderate (3%) or severe (2%) abnormalities were observed in 9 patients. For 19 of 20 features, ΔFV was significantly different among severity levels. For 12 features, significant differences were observed at every level. Compared with regions with no abnormalities, ΔFV for these 12 features increased, on average, by 1.5%, 12%, and 30%, respectively, for mild, moderate, and severe abnormalitites. Area under the ROC curve was largest when comparing ΔFV in the highest severity level with the remaining three categories (mean AUC across features: 0.84). In conclusion, 19 features that characterized the severity of radiologic changes from pre-therapy scans were identified. These features may be used in future studies to quantify acute normal lung tissue damage following RT. Presented, in part at the IASLC 15th World Conference on Lung Conference, Sydney, AUS (2013).

Reference-tissue correction of T2-weighted signal intensity for prostate cancer detection

NASA Astrophysics Data System (ADS)

Peng, Yahui; Jiang, Yulei; Oto, Aytekin

2014-03-01

The purpose of this study was to investigate whether correction with respect to reference tissue of T2-weighted MRimage signal intensity (SI) improves its effectiveness for classification of regions of interest (ROIs) as prostate cancer (PCa) or normal prostatic tissue. Two image datasets collected retrospectively were used in this study: 71 cases acquired with GE scanners (dataset A), and 59 cases acquired with Philips scanners (dataset B). Through a consensus histology- MR correlation review, 175 PCa and 108 normal-tissue ROIs were identified and drawn manually. Reference-tissue ROIs were selected in each case from the levator ani muscle, urinary bladder, and pubic bone. T2-weighted image SI was corrected as the ratio of the average T2-weighted image SI within an ROI to that of a reference-tissue ROI. Area under the receiver operating characteristic curve (AUC) was used to evaluate the effectiveness of T2-weighted image SIs for differentiation of PCa from normal-tissue ROIs. AUC (+/- standard error) for uncorrected T2-weighted image SIs was 0.78+/-0.04 (datasets A) and 0.65+/-0.05 (datasets B). AUC for corrected T2-weighted image SIs with respect to muscle, bladder, and bone reference was 0.77+/-0.04 (p=1.0), 0.77+/-0.04 (p=1.0), and 0.75+/-0.04 (p=0.8), respectively, for dataset A; and 0.81+/-0.04 (p=0.002), 0.78+/-0.04 (p<0.001), and 0.79+/-0.04 (p<0.001), respectively, for dataset B. Correction in reference to the levator ani muscle yielded the most consistent results between GE and Phillips images. Correction of T2-weighted image SI in reference to three types of extra-prostatic tissue can improve its effectiveness for differentiation of PCa from normal-tissue ROIs, and correction in reference to the levator ani muscle produces consistent T2-weighted image SIs between GE and Phillips MR images.

eBits: Compact stream of mesh refinements for remote visualization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sati, Mukul; Lindstrom, Peter; Rossignac, Jarek

2016-05-12

Here, we focus on applications where a remote client needs to visualize or process a complex, manifold triangle mesh, M, but only in a relatively small, user controlled, Region of Interest (RoI) at a time. The client first downloads a coarse base mesh, pre-computed on the server via a series of simplification passes on M, one per Level of Detail (LoD), each pass identifying an independent set of triangles, collapsing them, and, for each collapse, storing, in a Vertex Expansion Record (VER), the information needed to reverse the collapse. On each client initiated RoI modification request, the server pushes tomore » the client a selected subset of these VERs, which, when decoded and applied to refine the mesh locally, ensure that the portion in the RoI is always at full resolution. The eBits approach proposed here offers state of the art compression ratios (using less than 2.5 bits per new full resolution RoI triangle when the RoI has more than 2000 vertices to transmit the connectivity for the selective refinements) and fine-grain control (allowing the user to adjust the RoI by small increments). The effectiveness of eBits results from several novel ideas and novel variations of previous solutions. We represent the VERs using persistent labels so that they can be applied in different orders within a given LoD. The server maintains a shadow copy of the client’s mesh. To avoid sending IDs identifying which vertices should be expanded, we either transmit, for each new vertex, a compact encoding of its death tag ​–the LoD at which it will be expanded if it lies in the RoI–or transmit vertex masks for the RoI and its neighboring vertices. We also propose a three-step simplification that reduces the overall transmission cost by increasing both the simplification effectiveness and the regularity of the valences in the resulting meshes.« less

SU-F-J-156: The Feasibility of MR-Only IMRT Planning for Prostate Anatomy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vaitheeswaran, R; Sivaramakrishnan, KR; Kumar, Prashant

Purpose: For prostate anatomy, previous investigations have shown that simulated CT (sCT) generated from MR images can be used for accurate dose computation. In this study, we demonstrate the feasibility of MR-only IMRT planning for prostate case. Methods: Regular CT (rCT) and MR images of the same patient were acquired for prostate anatomy. Regions-of-interest (ROIs) i.e. target and risk structures are delineated on the rCT. A simulated CT (sCT) is generated from the MR image using the method described by Schadewaldt N et al. Their work establishes the clinical acceptability of dose calculation results on the sCT when compared tomore » rCT. rCT and sCT are rigidly registered to ensure proper alignment between the two images. rCT and sCT are overlaid on each other and slice-wise visual inspection confirms excellent agreement between the two images. ROIs on the rCT are copied over to sCT. Philips AutoPlanning solution is used for generating treatment plans. The same treatment technique protocol (plan parameters and clinical goals) is used to generate AutoPlan-rCT and AutoPlan-sCT respectively for rCT and and sCT. DVH comparison on ROIs and slice-wise evaluation of dose is performed between AutoPlan-rCT and AutoPlan-sCT. Delivery parameters i.e. beam and corresponding segments from the AutoPlan-sCT are copied over to rCT and dose is computed to get AutoPlan-sCT-on-rCT. Results: Plan evaluation is done based on Dose Volume Histogram (DVH) of ROIs and manual slice-wise inspection of dose distribution. Both AutoPlan-rCT and AutoPlan-sCT provide a clinically acceptable plan. Also, AutoPlan-sCT-on-rCT shows excellent agreement with AutoPlan-sCT. Conclusion: The study demonstrates that it is feasible to do IMRT planning on the simulated CT image obtained from MR image for prostate anatomy. The research is supported by Philips India Ltd.« less

Spatially resolved assessment of hepatic function using 99mTc-IDA SPECT

PubMed Central

Wang, Hesheng; Cao, Yue

2013-01-01

Purpose: 99mTc-iminodiacetic acid (IDA) hepatobiliary imaging is usually quantified for hepatic function on the entire liver or regions of interest (ROIs) in the liver. The authors presented a method to estimate the hepatic extraction fraction (HEF) voxel-by-voxel from single-photon emission computed tomography (SPECT)/CT with a 99mTc-labeled IDA agent of mebrofenin and evaluated the spatially resolved HEF measurements with an independent physiological measurement. Methods: Fourteen patients with intrahepatic cancers were treated with radiation therapy (RT) and imaged by 99mTc-mebrofenin SPECT before and 1 month after RT. The dynamic SPECT volumes were with a resolution of 3.9 × 3.9 × 2.5 mm3. Throughout the whole liver with approximate 50 000 voxels, voxelwise HEF quantifications were estimated and compared between using arterial input function (AIF) from the heart and using vascular input function (VIF) from the spleen. The correlation between mean of the HEFs over the nontumor liver tissue and the overall liver function measured by Indocyanine green clearance half-time (T1/2) was assessed. Variation of the voxelwise estimation was evaluated in ROIs drawn in relatively homogeneous regions of the livers. The authors also examined effects of the time range parameter on the voxelwise HEF quantification. Results: Mean of the HEFs over the liver estimated using AIF significantly correlated with the physiological measurement T1/2 (r = 0.52, p = 0.0004), and the correlation was greatly improved by using VIF (r = 0.79, p < 0.0001). The parameter of time range for the retention phase did not lead to a significant difference in the means of the HEFs in the ROIs. Using VIF and a retention phase time range of 7–30 min, the relative variation of the voxelwise HEF in the ROIs was 10% ± 6% of respective mean HEF. Conclusions: The voxelwise HEF derived from 99mTc-IDA SPECT by the deconvolution analysis is feasible to assess the spatial distribution of hepatic function in the liver. PMID:24007177

MEG Frequency Analysis Depicts the Impaired Neurophysiological Condition of Ischemic Brain

PubMed Central

Ikeda, Hidetoshi; Tsuyuguchi, Naohiro; Uda, Takehiro; Okumura, Eiichi; Asakawa, Takashi; Haruta, Yasuhiro; Nishiyama, Hideki; Okada, Toyoji; Kamada, Hajime; Ohata, Kenji; Miki, Yukio

2016-01-01

Purpose Quantitative imaging of neuromagnetic fields based on automated region of interest (ROI) setting was analyzed to determine the characteristics of cerebral neural activity in ischemic areas. Methods Magnetoencephalography (MEG) was used to evaluate spontaneous neuromagnetic fields in the ischemic areas of 37 patients with unilateral internal carotid artery (ICA) occlusive disease. Voxel-based time-averaged intensity of slow waves was obtained in two frequency bands (0.3–4 Hz and 4–8 Hz) using standardized low-resolution brain electromagnetic tomography (sLORETA) modified for a quantifiable method (sLORETA-qm). ROIs were automatically applied to the anterior cerebral artery (ACA), anterior middle cerebral artery (MCAa), posterior middle cerebral artery (MCAp), and posterior cerebral artery (PCA) using statistical parametric mapping (SPM). Positron emission tomography with 15O-gas inhalation (15O-PET) was also performed to evaluate cerebral blood flow (CBF) and oxygen extraction fraction (OEF). Statistical analyses were performed using laterality index of MEG and 15O-PET in each ROI with respect to distribution and intensity. Results MEG revealed statistically significant laterality in affected MCA regions, including 4–8 Hz waves in MCAa, and 0.3–4 Hz and 4–8 Hz waves in MCAp (95% confidence interval: 0.020–0.190, 0.030–0.207, and 0.034–0.213), respectively. We found that 0.3–4 Hz waves in MCAp were highly correlated with CBF in MCAa and MCAp (r = 0.74, r = 0.68, respectively), whereas 4–8 Hz waves were moderately correlated with CBF in both the MCAa and MCAp (r = 0.60, r = 0.63, respectively). We also found that 4–8 Hz waves in MCAp were statistically significant for misery perfusion identified on 15O-PET (p<0.05). Conclusions Quantitatively imaged spontaneous neuromagnetic fields using the automated ROI setting enabled clear depiction of cerebral ischemic areas. Frequency analysis may reveal unique neural activity that is distributed in the impaired vascular metabolic territory, in which the cerebral infarction has not yet been completed. PMID:27992543

A Whole-Tumor Histogram Analysis of Apparent Diffusion Coefficient Maps for Differentiating Thymic Carcinoma from Lymphoma.

PubMed

Zhang, Wei; Zhou, Yue; Xu, Xiao-Quan; Kong, Ling-Yan; Xu, Hai; Yu, Tong-Fu; Shi, Hai-Bin; Feng, Qing

2018-01-01

To assess the performance of a whole-tumor histogram analysis of apparent diffusion coefficient (ADC) maps in differentiating thymic carcinoma from lymphoma, and compare it with that of a commonly used hot-spot region-of-interest (ROI)-based ADC measurement. Diffusion weighted imaging data of 15 patients with thymic carcinoma and 13 patients with lymphoma were retrospectively collected and processed with a mono-exponential model. ADC measurements were performed by using a histogram-based and hot-spot-ROI-based approach. In the histogram-based approach, the following parameters were generated: mean ADC (ADC mean ), median ADC (ADC median ), 10th and 90th percentile of ADC (ADC 10 and ADC 90 ), kurtosis, and skewness. The difference in ADCs between thymic carcinoma and lymphoma was compared using a t test. Receiver operating characteristic analyses were conducted to determine and compare the differentiating performance of ADCs. Lymphoma demonstrated significantly lower ADC mean , ADC median , ADC 10 , ADC 90 , and hot-spot-ROI-based mean ADC than those found in thymic carcinoma (all p values < 0.05). There were no differences found in the kurtosis ( p = 0.412) and skewness ( p = 0.273). The ADC 10 demonstrated optimal differentiating performance (cut-off value, 0.403 × 10 -3 mm 2 /s; area under the receiver operating characteristic curve [AUC], 0.977; sensitivity, 92.3%; specificity, 93.3%), followed by the ADC mean , ADC median , ADC 90 , and hot-spot-ROI-based mean ADC. The AUC of ADC 10 was significantly higher than that of the hot spot ROI based ADC (0.977 vs. 0.797, p = 0.036). Compared with the commonly used hot spot ROI based ADC measurement, a histogram analysis of ADC maps can improve the differentiating performance between thymic carcinoma and lymphoma.

The effect of face exploration on postural control in healthy children.

PubMed

Goulème, Nathalie; Seassau, Magali; Bucci, Maria Pia

2015-07-01

The objective was to explore how face exploration affects postural control in healthy children. The novelty here is that eye movements and posture were simultaneously recorded. Three groups of children participated in the study: 12 children of 7.8±0.5 years old, 13 children of 10.4±0.5 years old and 12 children of 15.7±0.9 years old. Eye movements were recorded by video-oculography and postural stability was recorded by a platform. Children were invited to explore five emotional faces (neutral, happy, sad fear and angry). Analysis of eye movements was done on saccadic latency, percentage of exploration time spent and number of saccades for each specific region of interest (ROI): eyes, nose and mouth. Analysis of posture was made on surface area, sway length and mean velocity of the center of pressures (CoP). Results showed that visual strategies, exploration and postural control develop during childhood and adolescence. Indeed, after nine years-old, children started to look the eyes ROI firstly, then the nose ROI and finally the mouth ROI. The number of saccades decreased with the age of children. The percentage of exploration time spent in eyes ROI was longer than the others ROIs and greater for unpleasant faces (sad, fear and angry) with respect to pleasant emotional face (happy). We found that in front of sad and happy faces the surface area of the CoP was significantly larger compared to other faces (neutral and angry). These results suggest that visual strategies and postural control change during children's development and can be influenced by the emotional face. Copyright © 2015 Elsevier B.V. All rights reserved.

Validating a new methodology for optical probe design and image registration in fNIRS studies

PubMed Central

Wijeakumar, Sobanawartiny; Spencer, John P.; Bohache, Kevin; Boas, David A.; Magnotta, Vincent A.

2015-01-01

Functional near-infrared spectroscopy (fNIRS) is an imaging technique that relies on the principle of shining near-infrared light through tissue to detect changes in hemodynamic activation. An important methodological issue encountered is the creation of optimized probe geometry for fNIRS recordings. Here, across three experiments, we describe and validate a processing pipeline designed to create an optimized, yet scalable probe geometry based on selected regions of interest (ROIs) from the functional magnetic resonance imaging (fMRI) literature. In experiment 1, we created a probe geometry optimized to record changes in activation from target ROIs important for visual working memory. Positions of the sources and detectors of the probe geometry on an adult head were digitized using a motion sensor and projected onto a generic adult atlas and a segmented head obtained from the subject's MRI scan. In experiment 2, the same probe geometry was scaled down to fit a child's head and later digitized and projected onto the generic adult atlas and a segmented volume obtained from the child's MRI scan. Using visualization tools and by quantifying the amount of intersection between target ROIs and channels, we show that out of 21 ROIs, 17 and 19 ROIs intersected with fNIRS channels from the adult and child probe geometries, respectively. Further, both the adult atlas and adult subject-specific MRI approaches yielded similar results and can be used interchangeably. However, results suggest that segmented heads obtained from MRI scans be used for registering children's data. Finally, in experiment 3, we further validated our processing pipeline by creating a different probe geometry designed to record from target ROIs involved in language and motor processing. PMID:25705757

Research on infrared ship detection method in sea-sky background

NASA Astrophysics Data System (ADS)

Tang, Da; Sun, Gang; Wang, Ding-he; Niu, Zhao-dong; Chen, Zeng-ping

2013-09-01

An approach to infrared ship detection based on sea-sky-line(SSL) detection, ROI extraction and feature recognition is proposed in this paper. Firstly, considering that far ships are expected to be adjacent to the SSL, SSL is detected to find potential target areas. Radon transform is performed on gradient image to choose candidate SSLs, and detection result is given by fuzzy synthetic evaluation values. Secondly, in view of recognizable condition that there should be enough differences between target and background in infrared image, two gradient masks have been created and improved as practical guidelines in eliminating false alarm. Thirdly, extract ROI near the SSL by using multi-grade segmentation and fusion method after image sharpening, and unsuitable candidates are screened out according to the gradient masks and ROI shape. Finally, we segment the rest of ROIs by two-stage modified OTSU, and calculate target confidence as a standard measuring the facticity of target. Compared with other ship detection methods, proposed method is suitable for bipolar targets, which offers a good practicability and accuracy, and achieves a satisfying detection speed. Detection experiments with 200 thousand frames show that the proposed method is widely applicable, powerful in resistance to interferences and noises with a detection rate of above 95%, which satisfies the engineering needs commendably.

FIB-SEM tomography in biology.

PubMed

Kizilyaprak, Caroline; Bittermann, Anne Greet; Daraspe, Jean; Humbel, Bruno M

2014-01-01

Three-dimensional information is much easier to understand than a set of two-dimensional images. Therefore a layman is thrilled by the pseudo-3D image taken in a scanning electron microscope (SEM) while, when seeing a transmission electron micrograph, his imagination is challenged. First approaches to gain insight in the third dimension were to make serial microtome sections of a region of interest (ROI) and then building a model of the object. Serial microtome sectioning is a tedious and skill-demanding work and therefore seldom done. In the last two decades with the increase of computer power, sophisticated display options, and the development of new instruments, an SEM with a built-in microtome as well as a focused ion beam scanning electron microscope (FIB-SEM), serial sectioning, and 3D analysis has become far easier and faster.Due to the relief like topology of the microtome trimmed block face of resin-embedded tissue, the ROI can be searched in the secondary electron mode, and at the selected spot, the ROI is prepared with the ion beam for 3D analysis. For FIB-SEM tomography, a thin slice is removed with the ion beam and the newly exposed face is imaged with the electron beam, usually by recording the backscattered electrons. The process, also called "slice and view," is repeated until the desired volume is imaged.As FIB-SEM allows 3D imaging of biological fine structure at high resolution of only small volumes, it is crucial to perform slice and view at carefully selected spots. Finding the region of interest is therefore a prerequisite for meaningful imaging. Thin layer plastification of biofilms offers direct access to the original sample surface and allows the selection of an ROI for site-specific FIB-SEM tomography just by its pronounced topographic features.

Application of CT perfusion to assess hemodynamics in symptomatic Moyamoya syndrome: focus on affected side and parameter characteristic.

PubMed

Huang, Shuran; Gao, Lingyun; Chen, Yueqin; Guo, Xiang; Liu, Deguo; Wang, Jiehuan; Shi, Zhitao; Sun, Zhanguo; Jin, Feng; Chen, Weijian; Yang, Yunjun

2018-01-27

Vascular and hemodynamic changes were not consistent in symptomatic and non-symptomatic cerebral hemisphere in patients with symptomatic moyamoya syndrome (MMS). Thus, the purpose of this study is to evaluate the hemodynamic difference between symptomatic and non-symptomatic cerebral hemisphere in patients with symptomatic MMS. Patients who were diagnosed with symptomatic MMS were retrospectively collected. All cases underwent CTP examination. Regions of interest (ROIs) were chosen in the mirroring bilateral frontal lobes, temporal lobes, the basal ganglia, and the brainstem as control region. The relative perfusion parameter values of symptomatic side were compared with non-symptomatic side. Of the 40 patients, 33 patients were taken into assessment. In all cases (n = 33), rCBF, rMTT, and rTTP in all regions of interest (ROIs) of the symptomatic side were significantly different from those of contralateral side. In unilateral MMS patients (n = 7), rCBF values were not significantly different between two sides in the temporal lobe and basal ganglia area; rTTP values were significantly higher in the symptomatic side. rMTT values were significantly higher only in the temporal lobe of symptomatic side. In bilateral MMS patients (n = 26), rCBF and rMTT in all ROIs of the symptomatic side were significantly different from those of contralateral side. However, there were no significant differences between two sides in all ROIs on rTTP values. This study demonstrates that rCBF and rMTT were more sensitive than rTTP for evaluating hemodynamic changes in patients with symptomatic bilateral MMS. Furthermore, patients with unilateral MMS may have a preserved rCBF compared to those with bilateral disease.

An Apparent Diffusion Coefficient Histogram Method Versus a Traditional 2-Dimensional Measurement Method for Identifying Non-Puerperal Mastitis From Breast Cancer at 3.0 T.

PubMed

Tang, Qi; Li, Qiang; Xie, Dong; Chu, Ketao; Liu, Lidong; Liao, Chengcheng; Qin, Yunying; Wang, Zheng; Su, Danke

2018-05-21

This study aimed to investigate the utility of a volumetric apparent diffusion coefficient (ADC) histogram method for distinguishing non-puerperal mastitis (NPM) from breast cancer (BC) and to compare this method with a traditional 2-dimensional measurement method. Pretreatment diffusion-weighted imaging data at 3.0 T were obtained for 80 patients (NPM, n = 27; BC, n = 53) and were retrospectively assessed. Two readers measured ADC values according to 2 distinct region-of-interest (ROI) protocols. The first protocol included the generation of ADC histograms for each lesion, and various parameters were examined. In the second protocol, 3 freehand (TF) ROIs for local lesions were generated to obtain a mean ADC value (defined as ADC-ROITF). All of the ADC values were compared by an independent-samples t test or the Mann-Whitney U test. Receiver operating characteristic curves and a leave-one-out cross-validation method were also used to determine diagnostic deficiencies of the significant parameters. The ADC values for NPM were characterized by significantly higher mean, 5th to 95th percentiles, and maximum and mode ADCs compared with the corresponding ADCs for BC (all P < 0.05). However, the minimum, skewness, and kurtosis ADC values, as well as ADC-ROITF, did not significantly differ between the NPM and BC cases. Thus, the generation of volumetric ADC histograms seems to be a superior method to the traditional 2-dimensional method that was examined, and it also seems to represent a promising image analysis method for distinguishing NPM from BC.

Smartphone-based quantitative measurements on holographic sensors.

PubMed

Khalili Moghaddam, Gita; Lowe, Christopher Robin

2017-01-01

The research reported herein integrates a generic holographic sensor platform and a smartphone-based colour quantification algorithm in order to standardise and improve the determination of the concentration of analytes of interest. The utility of this approach has been exemplified by analysing the replay colour of the captured image of a holographic pH sensor in near real-time. Personalised image encryption followed by a wavelet-based image compression method were applied to secure the image transfer across a bandwidth-limited network to the cloud. The decrypted and decompressed image was processed through four principal steps: Recognition of the hologram in the image with a complex background using a template-based approach, conversion of device-dependent RGB values to device-independent CIEXYZ values using a polynomial model of the camera and computation of the CIEL*a*b* values, use of the colour coordinates of the captured image to segment the image, select the appropriate colour descriptors and, ultimately, locate the region of interest (ROI), i.e. the hologram in this case, and finally, application of a machine learning-based algorithm to correlate the colour coordinates of the ROI to the analyte concentration. Integrating holographic sensors and the colour image processing algorithm potentially offers a cost-effective platform for the remote monitoring of analytes in real time in readily accessible body fluids by minimally trained individuals.

A dataset of multiresolution functional brain parcellations in an elderly population with no or mild cognitive impairment.

PubMed

Tam, Angela; Dansereau, Christian; Badhwar, AmanPreet; Orban, Pierre; Belleville, Sylvie; Chertkow, Howard; Dagher, Alain; Hanganu, Alexandru; Monchi, Oury; Rosa-Neto, Pedro; Shmuel, Amir; Breitner, John; Bellec, Pierre

2016-12-01

We present group eight resolutions of brain parcellations for clusters generated from resting-state functional magnetic resonance images for 99 cognitively normal elderly persons and 129 patients with mild cognitive impairment, pooled from four independent datasets. This dataset was generated as part of the following study: Common Effects of Amnestic Mild Cognitive Impairment on Resting-State Connectivity Across Four Independent Studies (Tam et al., 2015) [1]. The brain parcellations have been registered to both symmetric and asymmetric MNI brain templates and generated using a method called bootstrap analysis of stable clusters (BASC) (Bellec et al., 2010) [2]. We present two variants of these parcellations. One variant contains bihemisphereic parcels (4, 6, 12, 22, 33, 65, 111, and 208 total parcels across eight resolutions). The second variant contains spatially connected regions of interest (ROIs) that span only one hemisphere (10, 17, 30, 51, 77, 199, and 322 total ROIs across eight resolutions). We also present maps illustrating functional connectivity differences between patients and controls for four regions of interest (striatum, dorsal prefrontal cortex, middle temporal lobe, and medial frontal cortex). The brain parcels and associated statistical maps have been publicly released as 3D volumes, available in .mnc and .nii file formats on figshare and on Neurovault. Finally, the code used to generate this dataset is available on Github.

Smartphone-based quantitative measurements on holographic sensors

PubMed Central

Khalili Moghaddam, Gita

2017-01-01

The research reported herein integrates a generic holographic sensor platform and a smartphone-based colour quantification algorithm in order to standardise and improve the determination of the concentration of analytes of interest. The utility of this approach has been exemplified by analysing the replay colour of the captured image of a holographic pH sensor in near real-time. Personalised image encryption followed by a wavelet-based image compression method were applied to secure the image transfer across a bandwidth-limited network to the cloud. The decrypted and decompressed image was processed through four principal steps: Recognition of the hologram in the image with a complex background using a template-based approach, conversion of device-dependent RGB values to device-independent CIEXYZ values using a polynomial model of the camera and computation of the CIEL*a*b* values, use of the colour coordinates of the captured image to segment the image, select the appropriate colour descriptors and, ultimately, locate the region of interest (ROI), i.e. the hologram in this case, and finally, application of a machine learning-based algorithm to correlate the colour coordinates of the ROI to the analyte concentration. Integrating holographic sensors and the colour image processing algorithm potentially offers a cost-effective platform for the remote monitoring of analytes in real time in readily accessible body fluids by minimally trained individuals. PMID:29141008

FLIM-FRET image analysis of tryptophan in prostate cancer cells

NASA Astrophysics Data System (ADS)

Periasamy, Ammasi; Alam, Shagufta R.; Svindrych, Zdenek; Wallrabe, Horst

2017-07-01

A region of interest (ROI) based quantitative FLIM-FRET image analysis is developed to quantitate the autofluorescence signals of the essential amino acid tryptophan as a biomarker to investigate the metabolism in prostate cancer cells.

Identifying regions of interest in medical images using self-organizing maps.

PubMed

Teng, Wei-Guang; Chang, Ping-Lin

2012-10-01

Advances in data acquisition, processing and visualization techniques have had a tremendous impact on medical imaging in recent years. However, the interpretation of medical images is still almost always performed by radiologists. Developments in artificial intelligence and image processing have shown the increasingly great potential of computer-aided diagnosis (CAD). Nevertheless, it has remained challenging to develop a general approach to process various commonly used types of medical images (e.g., X-ray, MRI, and ultrasound images). To facilitate diagnosis, we recommend the use of image segmentation to discover regions of interest (ROI) using self-organizing maps (SOM). We devise a two-stage SOM approach that can be used to precisely identify the dominant colors of a medical image and then segment it into several small regions. In addition, by appropriately conducting the recursive merging steps to merge smaller regions into larger ones, radiologists can usually identify one or more ROIs within a medical image.

Intracranial electrical impedance tomography: a method of continuous monitoring in an animal model of head trauma.

PubMed

Manwaring, Preston K; Moodie, Karen L; Hartov, Alexander; Manwaring, Kim H; Halter, Ryan J

2013-10-01

Electrical impedance tomography (EIT) is a method that can render continuous graphical cross-sectional images of the brain's electrical properties. Because these properties can be altered by variations in water content, shifts in sodium concentration, bleeding, and mass deformation, EIT has promise as a sensitive instrument for head injury monitoring to improve early recognition of deterioration and to observe the benefits of therapeutic intervention. This study presents a swine model of head injury used to determine the detection capabilities of an inexpensive bedside EIT monitoring system with a novel intracranial pressure (ICP)/EIT electrode combination sensor on induced intraparenchymal mass effect, intraparenchymal hemorrhage, and cessation of brain blood flow. Conductivity difference images are shown in conjunction with ICP data, confirming the effects. Eight domestic piglets (3-4 weeks of age, mean 10 kg), under general anesthesia, were subjected to 4 injuries: induced intraparenchymal mass effect using an inflated, and later, deflated 0.15-mL Fogarty catheter; hemorrhage by intraparenchymal injection of 1-mL arterial blood; and ischemia/infarction by euthanasia. EIT and ICP data were recorded 10 minutes before inducing the injury until 10 minutes after injury. Continuous EIT and ICP monitoring were facilitated by a ring of circumferentially disposed cranial Ag/AgCl electrodes and 1 intraparenchymal ICP/EIT sensor electrode combination. Data were recorded at 100 Hz. Two-dimensional tomographic conductivity difference (Δσ) images, rendered using data before and after an injury, were displayed in real time on an axial circular mesh. Regions of interest (ROI) within the images were automatically selected as the upper or lower 5% of conductivity data depending on the nature of the injury. Mean Δσ within the ROIs and background were statistically analyzed. ROI Δσ was compared with the background Δσ after an injury event using an unpaired, unequal variance t test. Conductivity change within an ROI after injury was likewise compared with the same ROI before the injury making use of unpaired t tests with unequal variance. Eight animal subjects were studied, each undergoing 4 injury events including euthanasia. Changes in conductivity due to injury showed expected pathophysiologic effects in an ROI identified within the middle of the left hemisphere; this localization is reasonable given the actual site of injury (left hemisphere) and spatial warping associated with estimating a 3-dimensional conductivity distribution in 2-dimensional space. Results are shown as mean ± 1 SD. When averaged across all 8 animals, balloon inflation caused the mean Δσ within the ROI to shift by -11.4 ± 10.9 mS/m; balloon deflation by +9.4 ± 8.8 mS/m; blood injection by +19.5 ± 11.5 mS/m; death by -12.6 ± 13.2 mS/m. All induced injuries were detectable to statistical significance (P < 0.0001). This study confirms that the bedside EIT system with ICP/EIT combination sensor can detect induced trauma. Such a technique may hold promise for further research in the monitoring and management of traumatically brain-injured individuals.

Computationally optimized ECoG stimulation with local safety constraints.

PubMed

Guler, Seyhmus; Dannhauer, Moritz; Roig-Solvas, Biel; Gkogkidis, Alexis; Macleod, Rob; Ball, Tonio; Ojemann, Jeffrey G; Brooks, Dana H

2018-06-01

Direct stimulation of the cortical surface is used clinically for cortical mapping and modulation of local activity. Future applications of cortical modulation and brain-computer interfaces may also use cortical stimulation methods. One common method to deliver current is through electrocorticography (ECoG) stimulation in which a dense array of electrodes are placed subdurally or epidurally to stimulate the cortex. However, proximity to cortical tissue limits the amount of current that can be delivered safely. It may be desirable to deliver higher current to a specific local region of interest (ROI) while limiting current to other local areas more stringently than is guaranteed by global safety limits. Two commonly used global safety constraints bound the total injected current and individual electrode currents. However, these two sets of constraints may not be sufficient to prevent high current density locally (hot-spots). In this work, we propose an efficient approach that prevents current density hot-spots in the entire brain while optimizing ECoG stimulus patterns for targeted stimulation. Specifically, we maximize the current along a particular desired directional field in the ROI while respecting three safety constraints: one on the total injected current, one on individual electrode currents, and the third on the local current density magnitude in the brain. This third set of constraints creates a computational barrier due to the huge number of constraints needed to bound the current density at every point in the entire brain. We overcome this barrier by adopting an efficient two-step approach. In the first step, the proposed method identifies the safe brain region, which cannot contain any hot-spots solely based on the global bounds on total injected current and individual electrode currents. In the second step, the proposed algorithm iteratively adjusts the stimulus pattern to arrive at a solution that exhibits no hot-spots in the remaining brain. We report on simulations on a realistic finite element (FE) head model with five anatomical ROIs and two desired directional fields. We also report on the effect of ROI depth and desired directional field on the focality of the stimulation. Finally, we provide an analysis of optimization runtime as a function of different safety and modeling parameters. Our results suggest that optimized stimulus patterns tend to differ from those used in clinical practice. Copyright © 2018 Elsevier Inc. All rights reserved.

Double Dissociation in the Anatomy of Socioemotional Disinhibition and Executive Functioning in Dementia

PubMed Central

Krueger, Casey E.; Laluz, Victor; Rosen, Howard J.; Neuhaus, John M.; Miller, Bruce L.; Kramer, Joel H.

2010-01-01

Objective To determine if socioemotional disinhibition and executive dysfunction are related to dissociable patterns of brain atrophy in neurodegenerative disease. Previous studies have indicated that behavioral and cognitive dysfunction in neurodegenerative disease are linked to atrophy in different parts of the frontal lobe, but these prior studies did not establish that these relationships were specific, which would best be demonstrated by a double dissociation. Method Subjects included 157 patients with neurodegenerative disease. A semi-automated parcellation program (Freesurfer) was used to generate regional cortical volumes from structural MRI scans. Regions of interest (ROIs) included anterior cingulate cortex (ACC), orbitofrontal cortex (OFC), middle frontal gyrus (MFG) and inferior frontal gyrus (IFG). Socioemotional disinhibition was measured using the Neuropsychiatric Inventory. Principal component analysis including three tasks of executive function (EF; verbal fluency, Stroop Interference, modified Trails) was used to generate a single factor score to represent EF. Results Partial correlations between ROIs, disinhibition, and EF were computed after controlling for total intracranial volume, MMSE, diagnosis, age, and education. Brain regions significantly correlated with disinhibition (ACC, OFC, IFG, and temporal lobes) and EF (MFG) were entered into separate hierarchical regressions to determine which brain regions predicted disinhibition and EF. OFC was the only brain region to significantly predict disinhibition and MFG significantly predicted executive functioning performance. A multivariate general linear model demonstrated a significant interaction between ROIs and cognitive-behavioral functions. Conclusions These results support a specific association between orbitofrontal areas and behavioral management as compared to dorsolateral areas and EF. PMID:21381829

Reorganization of functional brain networks mediates the improvement of cognitive performance following real-time neurofeedback training of working memory.

PubMed

Zhang, Gaoyan; Yao, Li; Shen, Jiahui; Yang, Yihong; Zhao, Xiaojie

2015-05-01

Working memory (WM) is essential for individuals' cognitive functions. Neuroimaging studies indicated that WM fundamentally relied on a frontoparietal working memory network (WMN) and a cinguloparietal default mode network (DMN). Behavioral training studies demonstrated that the two networks can be modulated by WM training. Different from the behavioral training, our recent study used a real-time functional MRI (rtfMRI)-based neurofeedback method to conduct WM training, demonstrating that WM performance can be significantly improved after successfully upregulating the activity of the target region of interest (ROI) in the left dorsolateral prefrontal cortex (Zhang et al., [2013]: PloS One 8:e73735); however, the neural substrate of rtfMRI-based WM training remains unclear. In this work, we assessed the intranetwork and internetwork connectivity changes of WMN and DMN during the training, and their correlations with the change of brain activity in the target ROI as well as with the improvement of post-training behavior. Our analysis revealed an "ROI-network-behavior" correlation relationship underlying the rtfMRI training. Further mediation analysis indicated that the reorganization of functional brain networks mediated the effect of self-regulation of the target brain activity on the improvement of cognitive performance following the neurofeedback training. The results of this study enhance our understanding of the neural basis of real-time neurofeedback and suggest a new direction to improve WM performance by regulating the functional connectivity in the WM related networks. © 2014 Wiley Periodicals, Inc.

Computer-aided diagnosis for phase-contrast X-ray computed tomography: quantitative characterization of human patellar cartilage with high-dimensional geometric features.

PubMed

Nagarajan, Mahesh B; Coan, Paola; Huber, Markus B; Diemoz, Paul C; Glaser, Christian; Wismüller, Axel

2014-02-01

Phase-contrast computed tomography (PCI-CT) has shown tremendous potential as an imaging modality for visualizing human cartilage with high spatial resolution. Previous studies have demonstrated the ability of PCI-CT to visualize (1) structural details of the human patellar cartilage matrix and (2) changes to chondrocyte organization induced by osteoarthritis. This study investigates the use of high-dimensional geometric features in characterizing such chondrocyte patterns in the presence or absence of osteoarthritic damage. Geometrical features derived from the scaling index method (SIM) and statistical features derived from gray-level co-occurrence matrices were extracted from 842 regions of interest (ROI) annotated on PCI-CT images of ex vivo human patellar cartilage specimens. These features were subsequently used in a machine learning task with support vector regression to classify ROIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver-operating characteristic curve (AUC). SIM-derived geometrical features exhibited the best classification performance (AUC, 0.95 ± 0.06) and were most robust to changes in ROI size. These results suggest that such geometrical features can provide a detailed characterization of the chondrocyte organization in the cartilage matrix in an automated and non-subjective manner, while also enabling classification of cartilage as healthy or osteoarthritic with high accuracy. Such features could potentially serve as imaging markers for evaluating osteoarthritis progression and its response to different therapeutic intervention strategies.

Dissimilarity representations in lung parenchyma classification

NASA Astrophysics Data System (ADS)

Sørensen, Lauge; de Bruijne, Marleen

2009-02-01

A good problem representation is important for a pattern recognition system to be successful. The traditional approach to statistical pattern recognition is feature representation. More specifically, objects are represented by a number of features in a feature vector space, and classifiers are built in this representation. This is also the general trend in lung parenchyma classification in computed tomography (CT) images, where the features often are measures on feature histograms. Instead, we propose to build normal density based classifiers in dissimilarity representations for lung parenchyma classification. This allows for the classifiers to work on dissimilarities between objects, which might be a more natural way of representing lung parenchyma. In this context, dissimilarity is defined between CT regions of interest (ROI)s. ROIs are represented by their CT attenuation histogram and ROI dissimilarity is defined as a histogram dissimilarity measure between the attenuation histograms. In this setting, the full histograms are utilized according to the chosen histogram dissimilarity measure. We apply this idea to classification of different emphysema patterns as well as normal, healthy tissue. Two dissimilarity representation approaches as well as different histogram dissimilarity measures are considered. The approaches are evaluated on a set of 168 CT ROIs using normal density based classifiers all showing good performance. Compared to using histogram dissimilarity directly as distance in a emph{k} nearest neighbor classifier, which achieves a classification accuracy of 92.9%, the best dissimilarity representation based classifier is significantly better with a classification accuracy of 97.0% (text{emph{p" border="0" class="imgtopleft"> = 0.046).

Fluorescence multispectral imaging-based diagnostic system for atherosclerosis.

PubMed

Ho, Cassandra Su Lyn; Horiuchi, Toshikatsu; Taniguchi, Hiroaki; Umetsu, Araya; Hagisawa, Kohsuke; Iwaya, Keiichi; Nakai, Kanji; Azmi, Amalina; Zulaziz, Natasha; Azhim, Azran; Shinomiya, Nariyoshi; Morimoto, Yuji

2016-08-20

Composition of atherosclerotic arterial walls is rich in lipids such as cholesterol, unlike normal arterial walls. In this study, we aimed to utilize this difference to diagnose atherosclerosis via multispectral fluorescence imaging, which allows for identification of fluorescence originating from the substance in the arterial wall. The inner surface of extracted arteries (rabbit abdominal aorta, human coronary artery) was illuminated by 405 nm excitation light and multispectral fluorescence images were obtained. Pathological examination of human coronary artery samples were carried out and thickness of arteries were calculated by measuring combined media and intima thickness. The fluorescence spectra in atherosclerotic sites were different from those in normal sites. Multiple regions of interest (ROI) were selected within each sample and a ratio between two fluorescence intensity differences (where each intensity difference is calculated between an identifier wavelength and a base wavelength) from each ROI was determined, allowing for discrimination of atherosclerotic sites. Fluorescence intensity and thickness of artery were found to be significantly correlated. These results indicate that multispectral fluorescence imaging provides qualitative and quantitative evaluations of atherosclerosis and is therefore a viable method of diagnosing the disease.

Advanced 3D image processing techniques for liver and hepatic tumor location and volumetry

NASA Astrophysics Data System (ADS)

Chemouny, Stephane; Joyeux, Henri; Masson, Bruno; Borne, Frederic; Jaeger, Marc; Monga, Olivier

1999-05-01

To assist radiologists and physicians in diagnosing, and in treatment planning and evaluating in liver oncology, we have developed a fast and accurate segmentation of the liver and its lesions within CT-scan exams. The first step of our method is to reduce spatial resolution of CT images. This will have two effects: obtain near isotropic 3D data space and drastically decrease computational time for further processing. On a second step a 3D non-linear `edge- preserving' smoothing filtering is performed throughout the entire exam. On a third step the 3D regions coming out from the second step are homogeneous enough to allow a quite simple segmentation process, based on morphological operations, under supervisor control, ending up with accurate 3D regions of interest (ROI) of the liver and all the hepatic tumors. On a fourth step the ROIs are eventually set back into the original images, features like volume and location are immediately computed and displayed. The segmentation we get is as precise as a manual one but is much faster.

Financial analysis of technology acquisition using fractionated lasers as a model.

PubMed

Jutkowitz, Eric; Carniol, Paul J; Carniol, Alan R

2010-08-01

Ablative fractional lasers are among the most advanced and costly devices on the market. Yet, there is a dearth of published literature on the cost and potential return on investment (ROI) of such devices. The objective of this study was to provide a methodological framework for physicians to evaluate ROI. To facilitate this analysis, we conducted a case study on the potential ROI of eight ablative fractional lasers. In the base case analysis, a 5-year lease and a 3-year lease were assumed as the purchase option with a $0 down payment and 3-month payment deferral. In addition to lease payments, service contracts, labor cost, and disposables were included in the total cost estimate. Revenue was estimated as price per procedure multiplied by total number of procedures in a year. Sensitivity analyses were performed to account for variability in model assumptions. Based on the assumptions of the model, all lasers had higher ROI under the 5-year lease agreement compared with that for the 3-year lease agreement. When comparing results between lasers, those with lower operating and purchase cost delivered a higher ROI. Sensitivity analysis indicates the model is most sensitive to purchase method. If physicians opt to purchase the device rather than lease, they can significantly enhance ROI. ROI analysis is an important tool for physicians who are considering making an expensive device acquisition. However, physicians should not rely solely on ROI and must also consider the clinical benefits of a laser. (c) Thieme Medical Publishers.

An Implementation of Privacy Protection for a Surveillance Camera Using ROI Coding of JPEG2000 with Face Detection

NASA Astrophysics Data System (ADS)

Muneyasu, Mitsuji; Odani, Shuhei; Kitaura, Yoshihiro; Namba, Hitoshi

On the use of a surveillance camera, there is a case where privacy protection should be considered. This paper proposes a new privacy protection method by automatically degrading the face region in surveillance images. The proposed method consists of ROI coding of JPEG2000 and a face detection method based on template matching. The experimental result shows that the face region can be detected and hidden correctly.

Classification of teeth in cone-beam CT using deep convolutional neural network.

PubMed

Miki, Yuma; Muramatsu, Chisako; Hayashi, Tatsuro; Zhou, Xiangrong; Hara, Takeshi; Katsumata, Akitoshi; Fujita, Hiroshi

2017-01-01

Dental records play an important role in forensic identification. To this end, postmortem dental findings and teeth conditions are recorded in a dental chart and compared with those of antemortem records. However, most dentists are inexperienced at recording the dental chart for corpses, and it is a physically and mentally laborious task, especially in large scale disasters. Our goal is to automate the dental filing process by using dental x-ray images. In this study, we investigated the application of a deep convolutional neural network (DCNN) for classifying tooth types on dental cone-beam computed tomography (CT) images. Regions of interest (ROIs) including single teeth were extracted from CT slices. Fifty two CT volumes were randomly divided into 42 training and 10 test cases, and the ROIs obtained from the training cases were used for training the DCNN. For examining the sampling effect, random sampling was performed 3 times, and training and testing were repeated. We used the AlexNet network architecture provided in the Caffe framework, which consists of 5 convolution layers, 3 pooling layers, and 2 full connection layers. For reducing the overtraining effect, we augmented the data by image rotation and intensity transformation. The test ROIs were classified into 7 tooth types by the trained network. The average classification accuracy using the augmented training data by image rotation and intensity transformation was 88.8%. Compared with the result without data augmentation, data augmentation resulted in an approximately 5% improvement in classification accuracy. This indicates that the further improvement can be expected by expanding the CT dataset. Unlike the conventional methods, the proposed method is advantageous in obtaining high classification accuracy without the need for precise tooth segmentation. The proposed tooth classification method can be useful in automatic filing of dental charts for forensic identification. Copyright © 2016 Elsevier Ltd. All rights reserved.

Medical image reconstruction algorithm based on the geometric information between sensor detector and ROI

NASA Astrophysics Data System (ADS)

Ham, Woonchul; Song, Chulgyu; Lee, Kangsan; Roh, Seungkuk

2016-05-01

In this paper, we propose a new image reconstruction algorithm considering the geometric information of acoustic sources and senor detector and review the two-step reconstruction algorithm which was previously proposed based on the geometrical information of ROI(region of interest) considering the finite size of acoustic sensor element. In a new image reconstruction algorithm, not only mathematical analysis is very simple but also its software implementation is very easy because we don't need to use the FFT. We verify the effectiveness of the proposed reconstruction algorithm by showing the simulation results by using Matlab k-wave toolkit.

AutoTag and AutoSnap: Standardized, semi-automatic capture of regions of interest from whole slide images

PubMed Central

Marien, Koen M.; Andries, Luc; De Schepper, Stefanie; Kockx, Mark M.; De Meyer, Guido R.Y.

2015-01-01

Tumor angiogenesis is measured by counting microvessels in tissue sections at high power magnification as a potential prognostic or predictive biomarker. Until now, regions of interest1 (ROIs) were selected by manual operations within a tumor by using a systematic uniform random sampling2 (SURS) approach. Although SURS is the most reliable sampling method, it implies a high workload. However, SURS can be semi-automated and in this way contribute to the development of a validated quantification method for microvessel counting in the clinical setting. Here, we report a method to use semi-automated SURS for microvessel counting: • Whole slide imaging with Pannoramic SCAN (3DHISTECH) • Computer-assisted sampling in Pannoramic Viewer (3DHISTECH) extended by two self-written AutoHotkey applications (AutoTag and AutoSnap) • The use of digital grids in Photoshop® and Bridge® (Adobe Systems) This rapid procedure allows traceability essential for high throughput protein analysis of immunohistochemically stained tissue. PMID:26150998

A deep learning framework for supporting the classification of breast lesions in ultrasound images.

PubMed

Han, Seokmin; Kang, Ho-Kyung; Jeong, Ja-Yeon; Park, Moon-Ho; Kim, Wonsik; Bang, Won-Chul; Seong, Yeong-Kyeong

2017-09-15

In this research, we exploited the deep learning framework to differentiate the distinctive types of lesions and nodules in breast acquired with ultrasound imaging. A biopsy-proven benchmarking dataset was built from 5151 patients cases containing a total of 7408 ultrasound breast images, representative of semi-automatically segmented lesions associated with masses. The dataset comprised 4254 benign and 3154 malignant lesions. The developed method includes histogram equalization, image cropping and margin augmentation. The GoogLeNet convolutionary neural network was trained to the database to differentiate benign and malignant tumors. The networks were trained on the data with augmentation and the data without augmentation. Both of them showed an area under the curve of over 0.9. The networks showed an accuracy of about 0.9 (90%), a sensitivity of 0.86 and a specificity of 0.96. Although target regions of interest (ROIs) were selected by radiologists, meaning that radiologists still have to point out the location of the ROI, the classification of malignant lesions showed promising results. If this method is used by radiologists in clinical situations it can classify malignant lesions in a short time and support the diagnosis of radiologists in discriminating malignant lesions. Therefore, the proposed method can work in tandem with human radiologists to improve performance, which is a fundamental purpose of computer-aided diagnosis.

A deep learning framework for supporting the classification of breast lesions in ultrasound images

NASA Astrophysics Data System (ADS)

Han, Seokmin; Kang, Ho-Kyung; Jeong, Ja-Yeon; Park, Moon-Ho; Kim, Wonsik; Bang, Won-Chul; Seong, Yeong-Kyeong

2017-10-01

In this research, we exploited the deep learning framework to differentiate the distinctive types of lesions and nodules in breast acquired with ultrasound imaging. A biopsy-proven benchmarking dataset was built from 5151 patients cases containing a total of 7408 ultrasound breast images, representative of semi-automatically segmented lesions associated with masses. The dataset comprised 4254 benign and 3154 malignant lesions. The developed method includes histogram equalization, image cropping and margin augmentation. The GoogLeNet convolutionary neural network was trained to the database to differentiate benign and malignant tumors. The networks were trained on the data with augmentation and the data without augmentation. Both of them showed an area under the curve of over 0.9. The networks showed an accuracy of about 0.9 (90%), a sensitivity of 0.86 and a specificity of 0.96. Although target regions of interest (ROIs) were selected by radiologists, meaning that radiologists still have to point out the location of the ROI, the classification of malignant lesions showed promising results. If this method is used by radiologists in clinical situations it can classify malignant lesions in a short time and support the diagnosis of radiologists in discriminating malignant lesions. Therefore, the proposed method can work in tandem with human radiologists to improve performance, which is a fundamental purpose of computer-aided diagnosis.

Evaluation of the Effect of Hemoglobin or Hematocrit Level on Dural Sinus Density Using Unenhanced Computed Tomography

PubMed Central

Cha, Sang-Hoon; Lee, Sung-Hyun; Shin, Dong-Ick

2013-01-01

Purpose To identify the relationship between hemoglobin (Hgb) or hematocrit (Hct) level and dural sinus density using unenhanced computed tomography (UECT). Materials and Methods Patients who were performed UECT and had records of a complete blood count within 24 hours from UECT were included (n=122). We measured the Hounsfield unit (HU) of the dural sinus at the right sigmoid sinus, left sigmoid sinus and 2 points of the superior sagittal sinus. Quantitative measurement of dural sinus density using the circle regions of interest (ROI) method was calculated as average ROI values at 3 or 4 points. Simple regression analysis was used to evaluate the correlation between mean HU and Hgb or mean HU and Hct. Results The mean densities of the dural sinuses ranged from 24.67 to 53.67 HU (mean, 43.28 HU). There was a strong correlation between mean density and Hgb level (r=0.832) and between mean density and Hct level (r=0.840). Conclusion Dural sinus density on UECT is closely related to Hgb and Hct levels. Therefore, the Hgb or Hct levels can be used to determine whether the dural sinus density is within the normal range or pathological conditions such as venous thrombosis. PMID:23225795

Cross calibration of GF-1 satellite wide field of view sensor with Landsat 8 OLI and HJ-1A HSI

NASA Astrophysics Data System (ADS)

Liu, Li; Gao, Hailiang; Pan, Zhiqiang; Gu, Xingfa; Han, Qijin; Zhang, Xuewen

2018-01-01

This paper focuses on cross calibrating the GaoFen (GF-1) satellite wide field of view (WFV) sensor using the Landsat 8 Operational Land Imager (OLI) and HuanJing-1A (HJ-1A) hyperspectral imager (HSI) as reference sensors. Two methods are proposed to calculate the spectral band adjustment factor (SBAF). One is based on the HJ-1A HSI image and the other is based on ground-measured reflectance. However, the HSI image and ground-measured reflectance were measured at different dates, as the WFV and OLI imagers passed overhead. Three groups of regions of interest (ROIs) were chosen for cross calibration, based on different selection criteria. Cross-calibration gains with nonzero and zero offsets were both calculated. The results confirmed that the gains with zero offset were better, as they were more consistent over different groups of ROIs and SBAF calculation methods. The uncertainty of this cross calibration was analyzed, and the influence of SBAF was calculated based on different HSI images and ground reflectance spectra. The results showed that the uncertainty of SBAF was <3% for bands 1 to 3. Two other large uncertainties in this cross calibration were variation of atmosphere and low ground reflectance.

Numerical study on simultaneous emission and transmission tomography in the MRI framework

NASA Astrophysics Data System (ADS)

Gjesteby, Lars; Cong, Wenxiang; Wang, Ge

2017-09-01

Multi-modality imaging methods are instrumental for advanced diagnosis and therapy. Specifically, a hybrid system that combines computed tomography (CT), nuclear imaging, and magnetic resonance imaging (MRI) will be a Holy Grail of medical imaging, delivering complementary structural/morphological, functional, and molecular information for precision medicine. A novel imaging method was recently demonstrated that takes advantage of radiotracer polarization to combine MRI principles with nuclear imaging. This approach allows the concentration of a polarized Υ-ray emitting radioisotope to be imaged with MRI resolution potentially outperforming the standard nuclear imaging mode at a sensitivity significantly higher than that of MRI. In our work, we propose to acquire MRI-modulated nuclear data for simultaneous image reconstruction of both emission and transmission parameters, suggesting the potential for simultaneous CT-SPECT-MRI. The synchronized diverse datasets allow excellent spatiotemporal registration and unique insight into physiological and pathological features. Here we describe the methodology involving the system design with emphasis on the formulation for tomographic images, even when significant radiotracer signals are limited to a region of interest (ROI). Initial numerical results demonstrate the feasibility of our approach for reconstructing concentration and attenuation images through a head phantom with various radio-labeled ROIs. Additional considerations regarding the radioisotope characteristics are also discussed.

Anatomically-Aided PET Reconstruction Using the Kernel Method

PubMed Central

Hutchcroft, Will; Wang, Guobao; Chen, Kevin T.; Catana, Ciprian; Qi, Jinyi

2016-01-01

This paper extends the kernel method that was proposed previously for dynamic PET reconstruction, to incorporate anatomical side information into the PET reconstruction model. In contrast to existing methods that incorporate anatomical information using a penalized likelihood framework, the proposed method incorporates this information in the simpler maximum likelihood (ML) formulation and is amenable to ordered subsets. The new method also does not require any segmentation of the anatomical image to obtain edge information. We compare the kernel method with the Bowsher method for anatomically-aided PET image reconstruction through a simulated data set. Computer simulations demonstrate that the kernel method offers advantages over the Bowsher method in region of interest (ROI) quantification. Additionally the kernel method is applied to a 3D patient data set. The kernel method results in reduced noise at a matched contrast level compared with the conventional ML expectation maximization (EM) algorithm. PMID:27541810

A brain-region-based meta-analysis method utilizing the Apriori algorithm.

PubMed

Niu, Zhendong; Nie, Yaoxin; Zhou, Qian; Zhu, Linlin; Wei, Jieyao

2016-05-18

Brain network connectivity modeling is a crucial method for studying the brain's cognitive functions. Meta-analyses can unearth reliable results from individual studies. Meta-analytic connectivity modeling is a connectivity analysis method based on regions of interest (ROIs) which showed that meta-analyses could be used to discover brain network connectivity. In this paper, we propose a new meta-analysis method that can be used to find network connectivity models based on the Apriori algorithm, which has the potential to derive brain network connectivity models from activation information in the literature, without requiring ROIs. This method first extracts activation information from experimental studies that use cognitive tasks of the same category, and then maps the activation information to corresponding brain areas by using the automatic anatomical label atlas, after which the activation rate of these brain areas is calculated. Finally, using these brain areas, a potential brain network connectivity model is calculated based on the Apriori algorithm. The present study used this method to conduct a mining analysis on the citations in a language review article by Price (Neuroimage 62(2):816-847, 2012). The results showed that the obtained network connectivity model was consistent with that reported by Price. The proposed method is helpful to find brain network connectivity by mining the co-activation relationships among brain regions. Furthermore, results of the co-activation relationship analysis can be used as a priori knowledge for the corresponding dynamic causal modeling analysis, possibly achieving a significant dimension-reducing effect, thus increasing the efficiency of the dynamic causal modeling analysis.

Physics Model-Based Scatter Correction in Multi-Source Interior Computed Tomography.

PubMed

Gong, Hao; Li, Bin; Jia, Xun; Cao, Guohua

2018-02-01

Multi-source interior computed tomography (CT) has a great potential to provide ultra-fast and organ-oriented imaging at low radiation dose. However, X-ray cross scattering from multiple simultaneously activated X-ray imaging chains compromises imaging quality. Previously, we published two hardware-based scatter correction methods for multi-source interior CT. Here, we propose a software-based scatter correction method, with the benefit of no need for hardware modifications. The new method is based on a physics model and an iterative framework. The physics model was derived analytically, and was used to calculate X-ray scattering signals in both forward direction and cross directions in multi-source interior CT. The physics model was integrated to an iterative scatter correction framework to reduce scatter artifacts. The method was applied to phantom data from both Monte Carlo simulations and physical experimentation that were designed to emulate the image acquisition in a multi-source interior CT architecture recently proposed by our team. The proposed scatter correction method reduced scatter artifacts significantly, even with only one iteration. Within a few iterations, the reconstructed images fast converged toward the "scatter-free" reference images. After applying the scatter correction method, the maximum CT number error at the region-of-interests (ROIs) was reduced to 46 HU in numerical phantom dataset and 48 HU in physical phantom dataset respectively, and the contrast-noise-ratio at those ROIs increased by up to 44.3% and up to 19.7%, respectively. The proposed physics model-based iterative scatter correction method could be useful for scatter correction in dual-source or multi-source CT.

Roi-Orientated Sensor Correction Based on Virtual Steady Reimaging Model for Wide Swath High Resolution Optical Satellite Imagery

NASA Astrophysics Data System (ADS)

Zhu, Y.; Jin, S.; Tian, Y.; Wang, M.

2017-09-01

To meet the requirement of high accuracy and high speed processing for wide swath high resolution optical satellite imagery under emergency situation in both ground processing system and on-board processing system. This paper proposed a ROI-orientated sensor correction algorithm based on virtual steady reimaging model for wide swath high resolution optical satellite imagery. Firstly, the imaging time and spatial window of the ROI is determined by a dynamic search method. Then, the dynamic ROI sensor correction model based on virtual steady reimaging model is constructed. Finally, the corrected image corresponding to the ROI is generated based on the coordinates mapping relationship which is established by the dynamic sensor correction model for corrected image and rigours imaging model for original image. Two experimental results show that the image registration between panchromatic and multispectral images can be well achieved and the image distortion caused by satellite jitter can be also corrected efficiently.

Cerebral Cortex Regions Selectively Vulnerable to Radiation Dose-Dependent Atrophy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Seibert, Tyler M.; Karunamuni, Roshan; Kaifi, Samar

Purpose and Objectives: Neurologic deficits after brain radiation therapy (RT) typically involve decline in higher-order cognitive functions such as attention and memory rather than sensory defects or paralysis. We sought to determine whether areas of the cortex critical to cognition are selectively vulnerable to radiation dose-dependent atrophy. Methods and Materials: We measured change in cortical thickness in 54 primary brain tumor patients who underwent fractionated, partial brain RT. The study patients underwent high-resolution, volumetric magnetic resonance imaging (T1-weighted; T2 fluid-attenuated inversion recovery, FLAIR) before RT and 1 year afterward. Semiautomated software was used to segment anatomic regions of the cerebral cortex formore » each patient. Cortical thickness was measured for each region before RT and 1 year afterward. Two higher-order cortical regions of interest (ROIs) were tested for association between radiation dose and cortical thinning: entorhinal (memory) and inferior parietal (attention/memory). For comparison, 2 primary cortex ROIs were also tested: pericalcarine (vision) and paracentral lobule (somatosensory/motor). Linear mixed-effects analyses were used to test all other cortical regions for significant radiation dose-dependent thickness change. Statistical significance was set at α = 0.05 using 2-tailed tests. Results: Cortical atrophy was significantly associated with radiation dose in the entorhinal (P=.01) and inferior parietal ROIs (P=.02). By contrast, no significant radiation dose-dependent effect was found in the primary cortex ROIs (pericalcarine and paracentral lobule). In the whole-cortex analysis, 9 regions showed significant radiation dose-dependent atrophy, including areas responsible for memory, attention, and executive function (P≤.002). Conclusions: Areas of cerebral cortex important for higher-order cognition may be most vulnerable to radiation-related atrophy. This is consistent with clinical observations that brain radiation patients experience deficits in domains of memory, executive function, and attention. Correlations of regional cortical atrophy with domain-specific cognitive functioning in prospective trials are warranted.« less

SU-C-202-02: A Comprehensive Evaluation of Adaptive Daily Planning for Cervical Cancer HDR Brachytherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Meerschaert, R; Paul, A; Zhuang, L

Purpose: To evaluate adaptive daily planning for cervical cancer patients who underwent high-dose-rate intra-cavitary brachytherapy (HDR-ICBT). Methods: This study included 22 cervical cancer patients who underwent 5 fractions of HDR ICBT. Regions of interest (ROIs) including high-risk clinical tumor volume (HR-CTV) and organs-at-risk (OARs) were manually contoured on daily CT images. All patients were treated with adaptive daily plans, which involved ROI delineation and dose optimization at each treatment fraction. Single treatment plans were retrospectively generated by applying the first treatment fraction’s dwell times adjusted for decay and dwell positions of the applicator to subsequent treatment fractions. Various existing similaritymore » metrics were calculated for the ROIs to quantify interfractional organ variations. A novel similarity score (JRARM) was established, which combined both volumetric overlap metrics (DSC, JSC, and RVD) and distance metrics (ASD, MSD, and RMSD). Linear regression was performed to determine a relationship between inter-fractional organ variations of various similarity metrics and D2cc variations from both plans. Wilcoxon Signed Rank Tests were used to assess adaptive daily plans and single plans by comparing EQD2 D2cc (α/β=3) for OARs. Results: For inter-fractional organ variations, the sigmoid demonstrated the greatest variations based on the JRARM and DSC similarity metrics. Comparisons between paired ROIs showed differences in JRARM scores and DSCs at each treatment fraction. RVD, MSD, and RMSD were found to be significantly correlated to D2cc variations for bladder and sigmoid. The comparison between plans found that adaptive daily planning provided lower EQD2 D2cc of OARs than single planning, specifically for the sigmoid (p=0.015). Conclusion: Substantial inter-fractional organ motion can occur during HDR-BT, which may significantly affect D2cc of OARs. Adaptive daily planning provides improved dose sparing for OARs compared to single planning.« less

Indexing data cubes for content-based searches in radio astronomy

NASA Astrophysics Data System (ADS)

Araya, M.; Candia, G.; Gregorio, R.; Mendoza, M.; Solar, M.

2016-01-01

Methods for observing space have changed profoundly in the past few decades. The methods needed to detect and record astronomical objects have shifted from conventional observations in the optical range to more sophisticated methods which permit the detection of not only the shape of an object but also the velocity and frequency of emissions in the millimeter-scale wavelength range and the chemical substances from which they originate. The consolidation of radio astronomy through a range of global-scale projects such as the Very Long Baseline Array (VLBA) and the Atacama Large Millimeter/submillimeter Array (ALMA) reinforces the need to develop better methods of data processing that can automatically detect regions of interest (ROIs) within data cubes (position-position-velocity), index them and facilitate subsequent searches via methods based on queries using spatial coordinates and/or velocity ranges. In this article, we present the development of an automatic system for indexing ROIs in data cubes that is capable of automatically detecting and recording ROIs while reducing the necessary storage space. The system is able to process data cubes containing megabytes of data in fractions of a second without human supervision, thus allowing it to be incorporated into a production line for displaying objects in a virtual observatory. We conducted a set of comprehensive experiments to illustrate how our system works. As a result, an index of 3% of the input size was stored in a spatial database, representing a compression ratio equal to 33:1 over an input of 20.875 GB, achieving an index of 773 MB approximately. On the other hand, a single query can be evaluated over our system in a fraction of second, showing that the indexing step works as a shock-absorber of the computational time involved in data cube processing. The system forms part of the Chilean Virtual Observatory (ChiVO), an initiative which belongs to the International Virtual Observatory Alliance (IVOA) that seeks to provide the capability of content-based searches on data cubes to the astronomical community.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Leng, Shuai; Yu, Lifeng; Wang, Jia

Purpose: Our purpose was to reduce image noise in spectral CT by exploiting data redundancies in the energy domain to allow flexible selection of the number, width, and location of the energy bins. Methods: Using a variety of spectral CT imaging methods, conventional filtered backprojection (FBP) reconstructions were performed and resulting images were compared to those processed using a Local HighlY constrained backPRojection Reconstruction (HYPR-LR) algorithm. The mean and standard deviation of CT numbers were measured within regions of interest (ROIs), and results were compared between FBP and HYPR-LR. For these comparisons, the following spectral CT imaging methods were used:(i)more » numerical simulations based on a photon-counting, detector-based CT system, (ii) a photon-counting, detector-based micro CT system using rubidium and potassium chloride solutions, (iii) a commercial CT system equipped with integrating detectors utilizing tube potentials of 80, 100, 120, and 140 kV, and (iv) a clinical dual-energy CT examination. The effects of tube energy and energy bin width were evaluated appropriate to each CT system. Results: The mean CT number in each ROI was unchanged between FBP and HYPR-LR images for each of the spectral CT imaging scenarios, irrespective of bin width or tube potential. However, image noise, as represented by the standard deviation of CT numbers in each ROI, was reduced by 36%-76%. In all scenarios, image noise after HYPR-LR algorithm was similar to that of composite images, which used all available photons. No difference in spatial resolution was observed between HYPR-LR processing and FBP. Dual energy patient data processed using HYPR-LR demonstrated reduced noise in the individual, low- and high-energy images, as well as in the material-specific basis images. Conclusions: Noise reduction can be accomplished for spectral CT by exploiting data redundancies in the energy domain. HYPR-LR is a robust method for reducing image noise in a variety of spectral CT imaging systems without losing spatial resolution or CT number accuracy. This method improves the flexibility to select energy bins in the manner that optimizes material identification and separation without paying the penalty of increased image noise or its corollary, increased patient dose.« less

A comparative study of automatic image segmentation algorithms for target tracking in MR-IGRT.

PubMed

Feng, Yuan; Kawrakow, Iwan; Olsen, Jeff; Parikh, Parag J; Noel, Camille; Wooten, Omar; Du, Dongsu; Mutic, Sasa; Hu, Yanle

2016-03-01

On-board magnetic resonance (MR) image guidance during radiation therapy offers the potential for more accurate treatment delivery. To utilize the real-time image information, a crucial prerequisite is the ability to successfully segment and track regions of interest (ROI). The purpose of this work is to evaluate the performance of different segmentation algorithms using motion images (4 frames per second) acquired using a MR image-guided radiotherapy (MR-IGRT) system. Manual contours of the kidney, bladder, duodenum, and a liver tumor by an experienced radiation oncologist were used as the ground truth for performance evaluation. Besides the manual segmentation, images were automatically segmented using thresholding, fuzzy k-means (FKM), k-harmonic means (KHM), and reaction-diffusion level set evolution (RD-LSE) algorithms, as well as the tissue tracking algorithm provided by the ViewRay treatment planning and delivery system (VR-TPDS). The performance of the five algorithms was evaluated quantitatively by comparing with the manual segmentation using the Dice coefficient and target registration error (TRE) measured as the distance between the centroid of the manual ROI and the centroid of the automatically segmented ROI. All methods were able to successfully segment the bladder and the kidney, but only FKM, KHM, and VR-TPDS were able to segment the liver tumor and the duodenum. The performance of the thresholding, FKM, KHM, and RD-LSE algorithms degraded as the local image contrast decreased, whereas the performance of the VP-TPDS method was nearly independent of local image contrast due to the reference registration algorithm. For segmenting high-contrast images (i.e., kidney), the thresholding method provided the best speed (<1 ms) with a satisfying accuracy (Dice=0.95). When the image contrast was low, the VR-TPDS method had the best automatic contour. Results suggest an image quality determination procedure before segmentation and a combination of different methods for optimal segmentation with the on-board MR-IGRT system. PACS number(s): 87.57.nm, 87.57.N-, 87.61.Tg. © 2016 The Authors.

A comparative study of automatic image segmentation algorithms for target tracking in MR‐IGRT

PubMed Central

Feng, Yuan; Kawrakow, Iwan; Olsen, Jeff; Parikh, Parag J.; Noel, Camille; Wooten, Omar; Du, Dongsu; Mutic, Sasa

2016-01-01

On‐board magnetic resonance (MR) image guidance during radiation therapy offers the potential for more accurate treatment delivery. To utilize the real‐time image information, a crucial prerequisite is the ability to successfully segment and track regions of interest (ROI). The purpose of this work is to evaluate the performance of different segmentation algorithms using motion images (4 frames per second) acquired using a MR image‐guided radiotherapy (MR‐IGRT) system. Manual contours of the kidney, bladder, duodenum, and a liver tumor by an experienced radiation oncologist were used as the ground truth for performance evaluation. Besides the manual segmentation, images were automatically segmented using thresholding, fuzzy k‐means (FKM), k‐harmonic means (KHM), and reaction‐diffusion level set evolution (RD‐LSE) algorithms, as well as the tissue tracking algorithm provided by the ViewRay treatment planning and delivery system (VR‐TPDS). The performance of the five algorithms was evaluated quantitatively by comparing with the manual segmentation using the Dice coefficient and target registration error (TRE) measured as the distance between the centroid of the manual ROI and the centroid of the automatically segmented ROI. All methods were able to successfully segment the bladder and the kidney, but only FKM, KHM, and VR‐TPDS were able to segment the liver tumor and the duodenum. The performance of the thresholding, FKM, KHM, and RD‐LSE algorithms degraded as the local image contrast decreased, whereas the performance of the VP‐TPDS method was nearly independent of local image contrast due to the reference registration algorithm. For segmenting high‐contrast images (i.e., kidney), the thresholding method provided the best speed (<1 ms) with a satisfying accuracy (Dice=0.95). When the image contrast was low, the VR‐TPDS method had the best automatic contour. Results suggest an image quality determination procedure before segmentation and a combination of different methods for optimal segmentation with the on‐board MR‐IGRT system. PACS number(s): 87.57.nm, 87.57.N‐, 87.61.Tg

The MAJORANA DEMONSTRATOR for 0νββ: Current status and future plans

DOE PAGES

Green, M. P.; Abgrall, N.; Aguayo, E.; ...

2015-01-01

The Majorana Demonstrator will search for neutrinoless-double-beta decay (0νββ) in 76Ge, while establishing the feasibility of a future tonne-scale germanium-based 0νββ experiment, and performing searches for new physics beyond the Standard Model. The experiment, currently under construction at the Sanford Underground Research Facility in Lead, SD, will consist of a pair of modular high-purity germanium detector arrays housed inside of a compact copper, lead, and polyethylene shield. Through a combination of strict materials qualifications and assay, low-background design, and powerful background rejection techniques, the Demonstrator aims to achieve a background rate in the 0νββ region of interest (ROI) of nomore » more than 3 counts in the 0νββ-decay ROI per tonne of target isotope per year (cnts/(ROI-t-y)). The current status of the Demonstrator is discussed, as are plans for its completion.« less

Determining composition of micron-scale protein deposits in neurodegenerative disease by spatially targeted optical microproteomics.

PubMed

Hadley, Kevin C; Rakhit, Rishi; Guo, Hongbo; Sun, Yulong; Jonkman, James E N; McLaurin, Joanne; Hazrati, Lili-Naz; Emili, Andrew; Chakrabartty, Avijit

2015-09-29

Spatially targeted optical microproteomics (STOMP) is a novel proteomics technique for interrogating micron-scale regions of interest (ROIs) in mammalian tissue, with no requirement for genetic manipulation. Methanol or formalin-fixed specimens are stained with fluorescent dyes or antibodies to visualize ROIs, then soaked in solutions containing the photo-tag: 4-benzoylbenzyl-glycyl-hexahistidine. Confocal imaging along with two photon excitation are used to covalently couple photo-tags to all proteins within each ROI, to a resolution of 0.67 µm in the xy-plane and 1.48 µm axially. After tissue solubilization, photo-tagged proteins are isolated and identified by mass spectrometry. As a test case, we examined amyloid plaques in an Alzheimer's disease (AD) mouse model and a post-mortem AD case, confirming known plaque constituents and discovering new ones. STOMP can be applied to various biological samples including cell lines, primary cell cultures, ex vivo specimens, biopsy samples, and fixed post-mortem tissue.

Histological analysis of the alterations on cortical bone channels network after radiotherapy: A rabbit study.

PubMed

Rabelo, Gustavo Davi; Beletti, Marcelo Emílio; Dechichi, Paula

2010-10-01

The aim of this study was to evaluate the effects of radiotherapy in cortical bone channels network. Fourteen rabbits were divided in two groups and test group received single dose of 15 Gy cobalt-60 radiation in tibia, bilaterally. The animals were sacrificed and a segment of tibia was removed and histologically processed. Histological images were taken and had their bone channels segmented and called regions of interest (ROI). Images were analyzed through developed algorithms using the SCILAB mathematical environment, getting percentage of bone matrix, ROI areas, ROI perimeters, their standard deviations and Lacunarity. The osteocytes and empty lacunae were also counted. Data were evaluated using Kolmogorov-Smirnov, Mann Whitney, and Student's t test (P < 0.05). Significant differences in bone matrix percentage, area and perimeters of the channels, their respective standard deviations and lacunarity were found between groups. In conclusion, the radiotherapy causes reduction of bone matrix and modifies the morphology of bone channels network. © 2010 Wiley-Liss, Inc.

Low Background Signal Readout Electronics for the MAJORANA DEMONSTRATOR

DOE Office of Scientific and Technical Information (OSTI.GOV)

Guinn, I.; Abgrall, N.; Arnquist, Isaac J.

2015-03-18

The Majorana Demonstrator (MJD)[1] is an array of p-type point contact (PPC) high purity Germanium (HPGe) detectors intended to search for neutrinoless double beta decay (0vBB decay) in 76Ge. MJD will consist of 40 kg of detectors, 30 kg of which will be isotopically enriched to 87% 76Ge. The array will consist of 14 strings of four or ve detectors placed in two separate cryostats. One of the main goals of the experiment is to demonstrate the feasibility of building a tonne-scale array of detectors to search for 0vBB decay with a much higher sensitivity. This involves acheiving backgrounds inmore » the 4 keV region of interest (ROI) around the 2039 keV Q-value of the BB decay of less than 1 count/ROI-t-y. Because many backgrounds will not directly scale with detector mass, the specific background goal of MJD is less than 3 counts/ROI-t-y.« less

Auditory middle latency responses differ in right- and left-handed subjects: an evaluation through topographic brain mapping.

PubMed

Mohebbi, Mehrnaz; Mahmoudian, Saeid; Alborzi, Marzieh Sharifian; Najafi-Koopaie, Mojtaba; Farahani, Ehsan Darestani; Farhadi, Mohammad

2014-09-01

To investigate the association of handedness with auditory middle latency responses (AMLRs) using topographic brain mapping by comparing amplitudes and latencies in frontocentral and hemispheric regions of interest (ROIs). The study included 44 healthy subjects with normal hearing (22 left handed and 22 right handed). AMLRs were recorded from 29 scalp electrodes in response to binaural 4-kHz tone bursts. Frontocentral ROI comparisons revealed that Pa and Pb amplitudes were significantly larger in the left-handed than the right-handed group. Topographic brain maps showed different distributions in AMLR components between the two groups. In hemispheric comparisons, Pa amplitude differed significantly across groups. A left-hemisphere emphasis of Pa was found in the right-handed group but not in the left-handed group. This study provides evidence that handedness is associated with AMLR components in frontocentral and hemispheric ROI. Handedness should be considered an essential factor in the clinical or experimental use of AMLRs.

Region of interest and windowing-based progressive medical image delivery using JPEG2000

NASA Astrophysics Data System (ADS)

Nagaraj, Nithin; Mukhopadhyay, Sudipta; Wheeler, Frederick W.; Avila, Ricardo S.

2003-05-01

An important telemedicine application is the perusal of CT scans (digital format) from a central server housed in a healthcare enterprise across a bandwidth constrained network by radiologists situated at remote locations for medical diagnostic purposes. It is generally expected that a viewing station respond to an image request by displaying the image within 1-2 seconds. Owing to limited bandwidth, it may not be possible to deliver the complete image in such a short period of time with traditional techniques. In this paper, we investigate progressive image delivery solutions by using JPEG 2000. An estimate of the time taken in different network bandwidths is performed to compare their relative merits. We further make use of the fact that most medical images are 12-16 bits, but would ultimately be converted to an 8-bit image via windowing for display on the monitor. We propose a windowing progressive RoI technique to exploit this and investigate JPEG 2000 RoI based compression after applying a favorite or a default window setting on the original image. Subsequent requests for different RoIs and window settings would then be processed at the server. For the windowing progressive RoI mode, we report a 50% reduction in transmission time.

Tractography Verified by Intraoperative Magnetic Resonance Imaging and Subcortical Stimulation During Tumor Resection Near the Corticospinal Tract.

PubMed

Münnich, Timo; Klein, Jan; Hattingen, Elke; Noack, Anika; Herrmann, Eva; Seifert, Volker; Senft, Christian; Forster, Marie-Therese

2018-04-14

Tractography is a popular tool for visualizing the corticospinal tract (CST). However, results may be influenced by numerous variables, eg, the selection of seeding regions of interests (ROIs) or the chosen tracking algorithm. To compare different variable sets by correlating tractography results with intraoperative subcortical stimulation of the CST, correcting intraoperative brain shift by the use of intraoperative MRI. Seeding ROIs were created by means of motor cortex segmentation, functional MRI (fMRI), and navigated transcranial magnetic stimulation (nTMS). Based on these ROIs, tractography was run for each patient using a deterministic and a probabilistic algorithm. Tractographies were processed on pre- and postoperatively acquired data. Using a linear mixed effects statistical model, best correlation between subcortical stimulation intensity and the distance between tractography and stimulation sites was achieved by using the segmented motor cortex as seeding ROI and applying the probabilistic algorithm on preoperatively acquired imaging sequences. Tractographies based on fMRI or nTMS results differed very little, but with enlargement of positive nTMS sites the stimulation-distance correlation of nTMS-based tractography improved. Our results underline that the use of tractography demands for careful interpretation of its virtual results by considering all influencing variables.

Seeking Optimal Region-Of-Interest (ROI) Single-Value Summary Measures for fMRI Studies in Imaging Genetics

PubMed Central

Tong, Yunxia; Chen, Qiang; Nichols, Thomas E.; Rasetti, Roberta; Callicott, Joseph H.; Berman, Karen F.; Weinberger, Daniel R.; Mattay, Venkata S.

2016-01-01

A data-driven hypothesis-free genome-wide association (GWA) approach in imaging genetics studies allows screening the entire genome to discover novel genes that modulate brain structure, chemistry, and function. However, a whole brain voxel-wise analysis approach in such genome-wide based imaging genetic studies can be computationally intense and also likely has low statistical power since a stringent multiple comparisons correction is needed for searching over the entire genome and brain. In imaging genetics with functional magnetic resonance imaging (fMRI) phenotypes, since many experimental paradigms activate focal regions that can be pre-specified based on a priori knowledge, reducing the voxel-wise search to single-value summary measures within a priori ROIs could prove efficient and promising. The goal of this investigation is to evaluate the sensitivity and reliability of different single-value ROI summary measures and provide guidance in future work. Four different fMRI databases were tested and comparisons across different groups (patients with schizophrenia, their siblings, vs. normal control subjects; across genotype groups) were conducted. Our results show that four of these measures, particularly those that represent values from the top most-activated voxels within an ROI are more powerful at reliably detecting group differences and generating greater effect sizes than the others. PMID:26974435

TU-G-BRD-02: Automated Systematic Quality Assurance Program for Radiation Oncology Information System Upgrades

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, B; Yi, B; Eley, J

Purpose: To: (1) describe an independent, automated, systematic software-based protocol for verifying clinical data accuracy/integrity for mitigation of data corruption/loss risks following radiation oncology information system (ROIS) upgrades; and (2) report on application of this approach in an academic/community practice environment. Methods: We propose a robust approach to perform quality assurance on the ROIS after an upgrade, targeting four data sources: (1) ROIS relational database; (2) ROIS DICOM interface; (3) ROIS treatment machine data configuration; and (4) ROIS-generated clinical reports. We investigated the database schema for differences between pre-/post-upgrade states. Paired DICOM data streams for the same object (such asmore » RT-Plan/Treatment Record) were compared between pre-/post-upgrade states for data corruption. We examined machine configuration and related commissioning data files for changes and corruption. ROIS-generated treatment appointment and treatment parameter reports were compared to ensure patient encounter and treatment plan accuracy. This protocol was supplemented by an end-to-end clinical workflow test to verify essential ROI functionality and integrity of components interfaced during patient care chain of activities. We describe the implementation of this protocol during a Varian ARIA system upgrade at our clinic. Results: We verified 1,638 data tables with 2.4 billion data records. For 222 under-treatment patients, 605 DICOM RT plans and 13,480 DICOM treatment records retrieved from the ROIS DICOM interface were compared, with no differences in fractions, doses delivered, or treatment parameters. We identified 82 new data tables and 78 amended/deleted tables consistent with the upgrade. Reports for 5,073 patient encounters over a 2-week horizon were compared and were identical to those before the upgrade. Content in 12,237 xml machine files was compared, with no differences identified. Conclusion: An independent QA/validation approach for ROIS upgrades was developed and implemented at our clinic. The success of this approach ensures a robust QA of ROIS upgrades without manual paper/electronic checks and associated intensive labor.« less

MR-perfusion (MRP) and diffusion-weighted imaging (DWI) in prostate cancer: quantitative and model-based gadobenate dimeglumine MRP parameters in detection of prostate cancer.

PubMed

Scherr, M K; Seitz, M; Müller-Lisse, U G; Ingrisch, M; Reiser, M F; Müller-Lisse, U L

2010-12-01

Various MR methods, including MR-spectroscopy (MRS), dynamic, contrast-enhanced MRI (DCE-MRI), and diffusion-weighted imaging (DWI) have been applied to improve test quality of standard MRI of the prostate. To determine if quantitative, model-based MR-perfusion (MRP) with gadobenate dimeglumine (Gd-BOPTA) discriminates between prostate cancer, benign tissue, and transitional zone (TZ) tissue. 27 patients (age, 65±4 years; PSA 11.0±6.1 ng/ml) with clinical suspicion of prostate cancer underwent standard MRI, 3D MR-spectroscopy (MRS), and MRP with Gd-BOPTA. Based on results of combined MRI/MRS and subsequent guided prostate biopsy alone (17/27), biopsy and radical prostatectomy (9/27), or sufficient negative follow-up (7/27), maps of model-free, deconvolution-based mean transit time (dMTT) were generated for 29 benign regions (bROIs), 14 cancer regions (cROIs), and 18 regions of transitional zone (tzROIs). Applying a 2-compartment exchange model, quantitative perfusion analysis was performed including as parameters: plasma flow (PF), plasma volume (PV), plasma mean transit time (PMTT), extraction flow (EFL), extraction fraction (EFR), interstitial volume (IV) and interstitial mean transit time (IMTT). Two-sided T-tests (significance level p<0.05) discriminated bROIs vs. cROIs and cROIs vs. tzROIs, respectively. PMTT discriminated best between bROIs (11.8±3.0 s) and cROIs (24.3±9.6 s) (p<0.0001), while PF, PV, PS, EFR, IV, IMTT also differed significantly (p 0.00002-0.0136). Discrimination between cROIs and tzROIs was insignificant for all parameters except PV (14.3±2.5 ml vs. 17.6±2.6 ml, p<0.05). Besides MRI, MRS and DWI quantitative, 2-compartment MRP with Gd-BOPTA discriminates between prostate cancer and benign tissue with several parameters. However, distinction of prostate cancer and TZ does not appear to be reliable. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Smoke regions extraction based on two steps segmentation and motion detection in early fire

NASA Astrophysics Data System (ADS)

Jian, Wenlin; Wu, Kaizhi; Yu, Zirong; Chen, Lijuan

2018-03-01

Aiming at the early problems of video-based smoke detection in fire video, this paper proposes a method to extract smoke suspected regions by combining two steps segmentation and motion characteristics. Early smoldering smoke can be seen as gray or gray-white regions. In the first stage, regions of interests (ROIs) with smoke are obtained by using two step segmentation methods. Then, suspected smoke regions are detected by combining the two step segmentation and motion detection. Finally, morphological processing is used for smoke regions extracting. The Otsu algorithm is used as segmentation method and the ViBe algorithm is used to detect the motion of smoke. The proposed method was tested on 6 test videos with smoke. The experimental results show the effectiveness of our proposed method over visual observation.

Parenchymal signal intensity in 3-T body MRI of dogs with hematopoietic neoplasia.

PubMed

Feeney, Daniel A; Sharkey, Leslie C; Steward, Susan M; Bahr, Katherine L; Henson, Michael S; Ito, Daisuke; O'Brien, Timothy D; Jessen, Carl R; Husbands, Brian D; Borgatti, Antonella; Modiano, Jaime F

2013-04-01

We performed a preliminary study involving 10 dogs to assess the applicability of body MRI for staging of canine diffuse hematopoietic neoplasia. T1-weighted (before and after intravenous gadolinium), T2-weighted, in-phase, out-of-phase, and short tau inversion recovery pulse sequences were used. By using digital region of interest (ROI) and visual comparison techniques, relative parenchymal organ (medial iliac lymph nodes, liver, spleen, kidney cortex, and kidney medulla) signal intensity was quantified as less than, equal to, or greater than that of skeletal muscle in 2 clinically normal young adult dogs and 10 dogs affected with either B-cell lymphoma (n = 7) or myelodysplastic syndrome (n = 3). Falciform fat and urinary bladder were evaluated to provide additional perspective regarding signal intensity from the pulse sequences. Dogs with nonfocal disease could be distinguished from normal dogs according to both the visual and ROI signal-intensity relationships. In normal dogs, liver signal intensity on the T2-weighted sequence was greater than that of skeletal muscle by using either the visual or ROI approach. However in affected dogs, T2-weighted liver signal intensity was less than that of skeletal muscle by using either the ROI approach (10 of 10 dogs) or the visual approach (9 of 10 dogs). These findings suggest that the comparison of relative signal intensity among organs may have merit as a research model for infiltrative parenchymal disease (ROI approach) or metabolic effects of disease; this comparison may have practical clinical applicability (visual comparison approach) as well.

Parenchymal Signal Intensity in 3-T Body MRI of Dogs with Hematopoietic Neoplasia

PubMed Central

Feeney, Daniel A; Sharkey, Leslie C; Steward, Susan M; Bahr, Katherine L; Henson, Michael S; Ito, Daisuke; O'Brien, Timothy D; Jessen, Carl R; Husbands, Brian D; Borgatti, Antonella; Modiano, Jaime F

2013-01-01

We performed a preliminary study involving 10 dogs to assess the applicability of body MRI for staging of canine diffuse hematopoietic neoplasia. T1-weighted (before and after intravenous gadolinium), T2-weighted, in-phase, out-of-phase, and short tau inversion recovery pulse sequences were used. By using digital region of interest (ROI) and visual comparison techniques, relative parenchymal organ (medial iliac lymph nodes, liver, spleen, kidney cortex, and kidney medulla) signal intensity was quantified as less than, equal to, or greater than that of skeletal muscle in 2 clinically normal young adult dogs and 10 dogs affected with either B-cell lymphoma (n = 7) or myelodysplastic syndrome (n = 3). Falciform fat and urinary bladder were evaluated to provide additional perspective regarding signal intensity from the pulse sequences. Dogs with nonfocal disease could be distinguished from normal dogs according to both the visual and ROI signal-intensity relationships. In normal dogs, liver signal intensity on the T2-weighted sequence was greater than that of skeletal muscle by using either the visual or ROI approach. However in affected dogs, T2-weighted liver signal intensity was less than that of skeletal muscle by using either the ROI approach (10 of 10 dogs) or the visual approach (9 of 10 dogs). These findings suggest that the comparison of relative signal intensity among organs may have merit as a research model for infiltrative parenchymal disease (ROI approach) or metabolic effects of disease; this comparison may have practical clinical applicability (visual comparison approach) as well. PMID:23582424

A new Fourier transform based CBIR scheme for mammographic mass classification: a preliminary invariance assessment

NASA Astrophysics Data System (ADS)

Gundreddy, Rohith Reddy; Tan, Maxine; Qui, Yuchen; Zheng, Bin

2015-03-01

The purpose of this study is to develop and test a new content-based image retrieval (CBIR) scheme that enables to achieve higher reproducibility when it is implemented in an interactive computer-aided diagnosis (CAD) system without significantly reducing lesion classification performance. This is a new Fourier transform based CBIR algorithm that determines image similarity of two regions of interest (ROI) based on the difference of average regional image pixel value distribution in two Fourier transform mapped images under comparison. A reference image database involving 227 ROIs depicting the verified soft-tissue breast lesions was used. For each testing ROI, the queried lesion center was systematically shifted from 10 to 50 pixels to simulate inter-user variation of querying suspicious lesion center when using an interactive CAD system. The lesion classification performance and reproducibility as the queried lesion center shift were assessed and compared among the three CBIR schemes based on Fourier transform, mutual information and Pearson correlation. Each CBIR scheme retrieved 10 most similar reference ROIs and computed a likelihood score of the queried ROI depicting a malignant lesion. The experimental results shown that three CBIR schemes yielded very comparable lesion classification performance as measured by the areas under ROC curves with the p-value greater than 0.498. However, the CBIR scheme using Fourier transform yielded the highest invariance to both queried lesion center shift and lesion size change. This study demonstrated the feasibility of improving robustness of the interactive CAD systems by adding a new Fourier transform based image feature to CBIR schemes.

What Is Professional Development Worth? Calculating the Value of Onboarding Programs in Extension

ERIC Educational Resources Information Center

Harder, Amy; Hodges, Alan; Zelaya, Priscilla

2017-01-01

Return on investment (ROI) is a commonly used metric for organizations concerned with demonstrating the value of their investments; it can be used to determine whether funds spent providing professional development programs for Extension professionals are good investments. This article presents a method for calculating ROI for an onboarding…

Quantifying sex, race, and age specific differences in bone microstructure requires measurement of anatomically equivalent regions.

PubMed

Ghasem-Zadeh, Ali; Burghardt, Andrew; Wang, Xiao-Fang; Iuliano, Sandra; Bonaretti, Serena; Bui, Minh; Zebaze, Roger; Seeman, Ego

2017-08-01

Individuals differ in forearm length. As microstructure differs along the radius, we hypothesized that errors may occur when sexual and racial dimorphisms are quantified at a fixed distance from the radio-carpal joint. Microstructure was quantified ex vivo in 18 cadaveric radii using high resolution peripheral quantitative computed tomography and in vivo in 158 Asian and Caucasian women and men at a fixed region of interest (ROI), a corrected ROI positioned at 4.5-6% of forearm length and using the fixed ROI adjusted for cross sectional area (CSA), forearm length or height. Secular effects of age were assessed by comparing 38 younger and 33 older women. Ex vivo, similar amounts of bone mass fashioned adjacent cross sections. Larger distal cross sections had thinner porous cortices of lower matrix mineral density (MMD), a larger medullary CSA and higher trabecular density. Smaller proximal cross-sections had thicker less porous cortices of higher MMD, a small medullary canal with little trabecular bone. Taller persons had more distally positioned fixed ROIs which moved proximally when corrected. Shorter persons had more proximally positioned fixed ROIs which moved distally when corrected, so dimorphisms lessened. In the corrected ROIs, in Caucasians, women had 0.6 SD higher porosity and 0.6 SD lower trabecular density than men (p<0.01). In Asians, women had 0.25 SD higher porosity (NS) and 0.5 SD lower trabecular density than men (p<0.05). In women, Asians had 0.8 SD lower porosity and 0.3 SD higher trabecular density than Caucasians (p<0.01). In men, Asians and Caucasians had similar porosity and trabecular density. Results were similar using an adjusted fixed ROI. Adjusting for secular effects of age on forearm length resulted in the age-related increment in porosity increasing from 2.08 SD to 2.48 SD (p<0.05). Assessment of sex, race and age related differences in microstructure requires measurement of anatomically equivalent regions. Copyright © 2017 Elsevier Inc. All rights reserved.

Local Functional Connectivity as a Pre-Surgical Tool for Seizure Focus Identification in Non-Lesion, Focal Epilepsy

PubMed Central

Weaver, K. E.; Chaovalitwongse, W. A.; Novotny, E. J.; Poliakov, A.; Grabowski, T. G.; Ojemann, J. G.

2013-01-01

Successful resection of cortical tissue engendering seizure activity is efficacious for the treatment of refractory, focal epilepsy. The pre-operative localization of the seizure focus is therefore critical to yielding positive, post-operative outcomes. In a small proportion of focal epilepsy patients presenting with normal MRI, identification of the seizure focus is significantly more challenging. We examined the capacity of resting state functional MRI (rsfMRI) to identify the seizure focus in a group of four non-lesion, focal (NLF) epilepsy individuals. We predicted that computing patterns of local functional connectivity in and around the epileptogenic zone combined with a specific reference to the corresponding region within the contralateral hemisphere would reliably predict the location of the seizure focus. We first averaged voxel-wise regional homogeneity (ReHo) across regions of interest (ROIs) from a standardized, probabilistic atlas for each NLF subject as well as 16 age- and gender-matched controls. To examine contralateral effects, we computed a ratio of the mean pair-wise correlations of all voxels within a ROI with the corresponding contralateral region (IntraRegional Connectivity – IRC). For each subject, ROIs were ranked (from lowest to highest) on ReHo, IRC, and the mean of the two values. At the group level, we observed a significant decrease in the rank for ROI harboring the seizure focus for the ReHo rankings as well as for the mean rank. At the individual level, the seizure focus ReHo rank was within bottom 10% lowest ranked ROIs for all four NLF epilepsy patients and three out of the four for the IRC rankings. However, when the two ranks were combined (averaging across ReHo and IRC ranks and scalars), the seizure focus ROI was either the lowest or second lowest ranked ROI for three out of the four epilepsy subjects. This suggests that rsfMRI may serve as an adjunct pre-surgical tool, facilitating the identification of the seizure focus in focal epilepsy. PMID:23641233

Quantitative Lesion-to-Fat Elasticity Ratio Measured by Shear-Wave Elastography for Breast Mass: Which Area Should Be Selected as the Fat Reference?

PubMed

Youk, Ji Hyun; Son, Eun Ju; Gweon, Hye Mi; Han, Kyung Hwa; Kim, Jeong-Ah

2015-01-01

To investigate whether the diagnostic performance of lesion-to-fat elasticity ratio (Eratio) was affected by the location of the reference fat. For 257 breast masses in 250 women who underwent shear-wave elastography before biopsy or surgery, multiple Eratios were measured with a fixed region-of-interest (ROI) in the mass along with multiple ROIs over the surrounding fat in different locations. Logistic regression analysis was used to determine that Eratio was independently associated with malignancy adjusted for the location of fat ROI (depth, laterality, and distance from lesion or skin). Mean (Emean) and maximum (Emax) elasticity values of fat were divided into four groups according to their interquartile ranges. Diagnostic performance of each group was evaluated using the area under the ROC curve (AUC). False diagnoses of Eratio were reviewed for ROIs on areas showing artifactual high or low stiffness and analyzed by logistic regression analysis to determine variables (associated palpable abnormality, lesion size, the vertical distance from fat ROI to skin, and elasticity values of lesion or fat) independently associated with false results. Eratio was independently associated with malignancy adjusted for the location of fat ROI (P<0.0001). Among four groups of fat elasticity values, the AUC showed no significant difference (<25th percentile, 25th percentile~median, median~75th percentile, and ≥75th percentile; 0.973, 0.982, 0.967, and 0.954 for Emean; 0.977, 0.967, 0.966, and 0.957 for Emax). Fat elasticity values were independently associated with false results of Eratio with the cut-off of 3.18 from ROC curve (P<0.0001). ROIs were set on fat showing artifactual high stiffness in 90% of 10 false negatives and on lesion showing vertical striped artifact or fat showing artifactual low stiffness in 77.5% of 71 false positives. Eratio shows good diagnostic performance regardless of the location of reference fat, except when it is placed in areas of artifacts.

Application of neuroanatomical features to tractography clustering.

PubMed

Wang, Qian; Yap, Pew-Thian; Wu, Guorong; Shen, Dinggang

2013-09-01

Diffusion tensor imaging allows unprecedented insight into brain neural connectivity in vivo by allowing reconstruction of neuronal tracts via captured patterns of water diffusion in white matter microstructures. However, tractography algorithms often output hundreds of thousands of fibers, rendering subsequent data analysis intractable. As a remedy, fiber clustering techniques are able to group fibers into dozens of bundles and thus facilitate analyses. Most existing fiber clustering methods rely on geometrical information of fibers, by viewing them as curves in 3D Euclidean space. The important neuroanatomical aspect of fibers, however, is ignored. In this article, the neuroanatomical information of each fiber is encapsulated in the associativity vector, which functions as the unique "fingerprint" of the fiber. Specifically, each entry in the associativity vector describes the relationship between the fiber and a certain anatomical ROI in a fuzzy manner. The value of the entry approaches 1 if the fiber is spatially related to the ROI at high confidence; on the contrary, the value drops closer to 0. The confidence of the ROI is calculated by diffusing the ROI according to the underlying fibers from tractography. In particular, we have adopted the fast marching method for simulation of ROI diffusion. Using the associativity vectors of fibers, we further model fibers as observations sampled from multivariate Gaussian mixtures in the feature space. To group all fibers into relevant major bundles, an expectation-maximization clustering approach is employed. Experimental results indicate that our method results in anatomically meaningful bundles that are highly consistent across subjects. Copyright © 2012 Wiley Periodicals, Inc., a Wiley company.

Intravoxel Incoherent Motion–derived Histogram Metrics for Assessment of Response after Combined Chemotherapy and Radiation Therapy in Rectal Cancer: Initial Experience and Comparison between Single-Section and Volumetric Analyses

PubMed Central

Vargas, Hebert Alberto; Lakhman, Yulia; Sudre, Romain; Do, Richard K. G.; Bibeau, Frederic; Azria, David; Assenat, Eric; Molinari, Nicolas; Pierredon, Marie-Ange; Rouanet, Philippe; Guiu, Boris

2016-01-01

Purpose To determine the diagnostic performance of intravoxel incoherent motion (IVIM) parameters and apparent diffusion coefficient (ADC) to assess response to combined chemotherapy and radiation therapy (CRT) in patients with rectal cancer by using histogram analysis derived from whole-tumor volumes and single-section regions of interest (ROIs). Materials and Methods The institutional review board approved this retrospective study of 31 patients with rectal cancer who underwent magnetic resonance (MR) imaging before and after CRT, including diffusion-weighted imaging with 34 b values prior to surgery. Patient consent was not required. ADC, perfusion-related diffusion fraction (f), slow diffusion coefficient (D), and fast diffusion coefficient (D*) were calculated on MR images acquired before and after CRT by using biexponential fitting. ADC and IVIM histogram metrics and median values were obtained by using whole-tumor volume and single-section ROI analyses. All ADC and IVIM parameters obtained before and after CRT were compared with histopathologic findings by using t tests with Holm-Sidak correction. Receiver operating characteristic curves were generated to evaluate the diagnostic performance of IVIM parameters derived from whole-tumor volume and single-section ROIs for prediction of histopathologic response. Results Extreme values aside, results of histogram analysis of ADC and IVIM were equivalent to median values for tumor response assessment (P > .06). Prior to CRT, none of the median ADC and IVIM diffusion metrics correlated with subsequent tumor response (P > .36). Median D and ADC values derived from either whole-volume or single-section analysis increased significantly after CRT (P ≤ .01) and were significantly higher in good versus poor responders (P ≤ .02). Median IVIM f and D* values did not significantly change after CRT and were not associated with tumor response to CRT (P > .36). Interobserver agreement was excellent for whole-tumor volume analysis (range, 0.91–0.95) but was only moderate for single-section ROI analysis (range, 0.50–0.63). Conclusion Median D and ADC values obtained after CRT were useful for discrimination between good and poor responders. Histogram metrics did not add to the median values for assessment of tumor response. Volumetric analysis demonstrated better interobserver reproducibility when compared with single-section ROI analysis. © RSNA, 2016 Online supplemental material is available for this article. PMID:26919562

Short- and long-term quantitation reproducibility of brain metabolites in the medial wall using proton echo planar spectroscopic imaging.

PubMed

Tsai, Shang-Yueh; Lin, Yi-Ru; Wang, Woan-Chyi; Niddam, David M

2012-11-15

Proton echo planar spectroscopic imaging (PEPSI) is a fast magnetic resonance spectroscopic imaging (MRSI) technique that allows mapping spatial metabolite distributions in the brain. Although the medial wall of the cortex is involved in a wide range of pathological conditions, previous MRSI studies have not focused on this region. To decide the magnitude of metabolic changes to be considered significant in this region, the reproducibility of the method needs to be established. The study aims were to establish the short- and long-term reproducibility of metabolites in the right medial wall and to compare regional differences using a constant short-echo time (TE30) and TE averaging (TEavg) optimized to yield glutamatergic information. 2D sagittal PEPSI was implemented at 3T using a 32 channel head coil. Acquisitions were repeated immediately and after approximately 2 weeks to assess the coefficients of variation (COV). COVs were obtained from eight regions-of-interest (ROIs) of varying size and location. TE30 resulted in better spectral quality and similar or lower quantitation uncertainty for all metabolites except glutamate (Glu). When Glu and glutamine (Gln) were quantified together (Glx) reduced quantitation uncertainty and increased reproducibility was observed for TE30. TEavg resulted in lowered quantitation uncertainty for Glu but in less reliable quantification of several other metabolites. TEavg did not result in a systematically improved short- or long-term reproducibility for Glu. The ROI volume was a major factor influencing reproducibility. For both short- and long-term repetitions, the Glu COVs obtained with TEavg were 5-8% for the large ROIs, 12-17% for the medium sized ROIs and 16-26% for the smaller cingulate ROIs. COVs obtained with TE30 for the less specific Glx were 3-5%, 8-10% and 10-15%. COVs for N-acetyl aspartate, creatine and choline using TE30 with long-term repetition were between 2-10%. Our results show that the cost of more specific glutamatergic information (Glu versus Glx) is the requirement of an increased effect size especially with increasing anatomical specificity. This comes in addition to the loss of sensitivity for other metabolites. Encouraging results were obtained with TE30 compared to other previously reported MRSI studies. The protocols implemented here are reliable and may be used to study disease progression and intervention mechanisms. Copyright © 2012 Elsevier Inc. All rights reserved.

Modelling probabilities of heavy precipitation by regional approaches

NASA Astrophysics Data System (ADS)

Gaal, L.; Kysely, J.

2009-09-01

Extreme precipitation events are associated with large negative consequences for human society, mainly as they may trigger floods and landslides. The recent series of flash floods in central Europe (affecting several isolated areas) on June 24-28, 2009, the worst one over several decades in the Czech Republic as to the number of persons killed and the extent of damage to buildings and infrastructure, is an example. Estimates of growth curves and design values (corresponding e.g. to 50-yr and 100-yr return periods) of precipitation amounts, together with their uncertainty, are important in hydrological modelling and other applications. The interest in high quantiles of precipitation distributions is also related to possible climate change effects, as climate model simulations tend to project increased severity of precipitation extremes in a warmer climate. The present study compares - in terms of Monte Carlo simulation experiments - several methods to modelling probabilities of precipitation extremes that make use of ‘regional approaches’: the estimation of distributions of extremes takes into account data in a ‘region’ (‘pooling group’), in which one may assume that the distributions at individual sites are identical apart from a site-specific scaling factor (the condition is referred to as ‘regional homogeneity’). In other words, all data in a region - often weighted in some way - are taken into account when estimating the probability distribution of extremes at a given site. The advantage is that sampling variations in the estimates of model parameters and high quantiles are to a large extent reduced compared to the single-site analysis. We focus on the ‘region-of-influence’ (ROI) method which is based on the identification of unique pooling groups (forming the database for the estimation) for each site under study. The similarity of sites is evaluated in terms of a set of site attributes related to the distributions of extremes. The issue of the size of the region is linked with a built-in test on regional homogeneity of data. Once a pooling group is delineated, weights based on a dissimilarity measure are assigned to individual sites involved in a pooling group, and all (weighted) data are employed in the estimation of model parameters and high quantiles at a given location. The ROI method is compared with the Hosking-Wallis (HW) regional frequency analysis, which is based on delineating fixed regions (instead of flexible pooling groups) and assigning unit weights to all sites in a region. The comparison of the performance of the individual regional models makes use of data on annual maxima of 1-day precipitation amounts at 209 stations covering the Czech Republic, with altitudes ranging from 150 to 1490 m a.s.l. We conclude that the ROI methodology is superior to the HW analysis, particularly for very high quantiles (100-yr return values). Another advantage of the ROI approach is that subjective decisions - unavoidable when fixed regions in the HW analysis are formed - may efficiently be suppressed, and almost all settings of the ROI method may be justified by results of the simulation experiments. The differences between (any) regional method and single-site analysis are very pronounced and suggest that the at-site estimation is highly unreliable. The ROI method is then applied to estimate high quantiles of precipitation amounts at individual sites. The estimates and their uncertainty are compared with those from a single-site analysis. We focus on the eastern part of the Czech Republic, i.e. an area with complex orography and a particularly pronounced role of Mediterranean cyclones in producing precipitation extremes. The design values are compared with precipitation amounts recorded during the recent heavy precipitation events, including the one associated with the flash flood on June 24, 2009. We also show that the ROI methodology may easily be transferred to the analysis of precipitation extremes in climate model outputs. It efficiently reduces (random) variations in the estimates of parameters of the extreme value distributions in individual gridboxes that result from large spatial variability of heavy precipitation, and represents a straightforward tool for ‘weighting’ data from neighbouring gridboxes within the estimation procedure. The study is supported by the Grant Agency of AS CR under project B300420801.

Effectiveness of regional DTI measures in distinguishing Alzheimer's disease, MCI, and normal aging☆

PubMed Central

Nir, Talia M.; Jahanshad, Neda; Villalon-Reina, Julio E.; Toga, Arthur W.; Jack, Clifford R.; Weiner, Michael W.; Thompson, Paul M.

2013-01-01

The Alzheimer's Disease Neuroimaging Initiative (ADNI) recently added diffusion tensor imaging (DTI), among several other new imaging modalities, in an effort to identify sensitive biomarkers of Alzheimer's disease (AD). While anatomical MRI is the main structural neuroimaging method used in most AD studies and clinical trials, DTI is sensitive to microscopic white matter (WM) changes not detectable with standard MRI, offering additional markers of neurodegeneration. Prior DTI studies of AD report lower fractional anisotropy (FA), and increased mean, axial, and radial diffusivity (MD, AxD, RD) throughout WM. Here we assessed which DTI measures may best identify differences among AD, mild cognitive impairment (MCI), and cognitively healthy elderly control (NC) groups, in region of interest (ROI) and voxel-based analyses of 155 ADNI participants (mean age: 73.5 ± 7.4; 90 M/65 F; 44 NC, 88 MCI, 23 AD). Both VBA and ROI analyses revealed widespread group differences in FA and all diffusivity measures. DTI maps were strongly correlated with widely-used clinical ratings (MMSE, CDR-sob, and ADAS-cog). When effect sizes were ranked, FA analyses were least sensitive for picking up group differences. Diffusivity measures could detect more subtle MCI differences, where FA could not. ROIs showing strongest group differentiation (lowest p-values) included tracts that pass through the temporal lobe, and posterior brain regions. The left hippocampal component of the cingulum showed consistently high effect sizes for distinguishing groups, across all diffusivity and anisotropy measures, and in correlations with cognitive scores. PMID:24179862

Probabilistic atlases of default mode, executive control and salience network white matter tracts: an fMRI-guided diffusion tensor imaging and tractography study

PubMed Central

Figley, Teresa D.; Bhullar, Navdeep; Courtney, Susan M.; Figley, Chase R.

2015-01-01

Diffusion tensor imaging (DTI) is a powerful MRI technique that can be used to estimate both the microstructural integrity and the trajectories of white matter pathways throughout the central nervous system. This fiber tracking (aka, “tractography”) approach is often carried out using anatomically-defined seed points to identify white matter tracts that pass through one or more structures, but can also be performed using functionally-defined regions of interest (ROIs) that have been determined using functional MRI (fMRI) or other methods. In this study, we performed fMRI-guided DTI tractography between all of the previously defined nodes within each of six common resting-state brain networks, including the: dorsal Default Mode Network (dDMN), ventral Default Mode Network (vDMN), left Executive Control Network (lECN), right Executive Control Network (rECN), anterior Salience Network (aSN), and posterior Salience Network (pSN). By normalizing the data from 32 healthy control subjects to a standard template—using high-dimensional, non-linear warping methods—we were able to create probabilistic white matter atlases for each tract in stereotaxic coordinates. By investigating all 198 ROI-to-ROI combinations within the aforementioned resting-state networks (for a total of 6336 independent DTI tractography analyses), the resulting probabilistic atlases represent a comprehensive cohort of functionally-defined white matter regions that can be used in future brain imaging studies to: (1) ascribe DTI or other white matter changes to particular functional brain networks, and (2) compliment resting state fMRI or other functional connectivity analyses. PMID:26578930

Statistical assessment of bi-exponential diffusion weighted imaging signal characteristics induced by intravoxel incoherent motion in malignant breast tumors

PubMed Central

Wong, Oi Lei; Lo, Gladys G.; Chan, Helen H. L.; Wong, Ting Ting; Cheung, Polly S. Y.

2016-01-01

Background The purpose of this study is to statistically assess whether bi-exponential intravoxel incoherent motion (IVIM) model better characterizes diffusion weighted imaging (DWI) signal of malignant breast tumor than mono-exponential Gaussian diffusion model. Methods 3 T DWI data of 29 malignant breast tumors were retrospectively included. Linear least-square mono-exponential fitting and segmented least-square bi-exponential fitting were used for apparent diffusion coefficient (ADC) and IVIM parameter quantification, respectively. F-test and Akaike Information Criterion (AIC) were used to statistically assess the preference of mono-exponential and bi-exponential model using region-of-interests (ROI)-averaged and voxel-wise analysis. Results For ROI-averaged analysis, 15 tumors were significantly better fitted by bi-exponential function and 14 tumors exhibited mono-exponential behavior. The calculated ADC, D (true diffusion coefficient) and f (pseudo-diffusion fraction) showed no significant differences between mono-exponential and bi-exponential preferable tumors. Voxel-wise analysis revealed that 27 tumors contained more voxels exhibiting mono-exponential DWI decay while only 2 tumors presented more bi-exponential decay voxels. ADC was consistently and significantly larger than D for both ROI-averaged and voxel-wise analysis. Conclusions Although the presence of IVIM effect in malignant breast tumors could be suggested, statistical assessment shows that bi-exponential fitting does not necessarily better represent the DWI signal decay in breast cancer under clinically typical acquisition protocol and signal-to-noise ratio (SNR). Our study indicates the importance to statistically examine the breast cancer DWI signal characteristics in practice. PMID:27709078

Image Processing Strategies Based on a Visual Saliency Model for Object Recognition Under Simulated Prosthetic Vision.

PubMed

Wang, Jing; Li, Heng; Fu, Weizhen; Chen, Yao; Li, Liming; Lyu, Qing; Han, Tingting; Chai, Xinyu

2016-01-01

Retinal prostheses have the potential to restore partial vision. Object recognition in scenes of daily life is one of the essential tasks for implant wearers. Still limited by the low-resolution visual percepts provided by retinal prostheses, it is important to investigate and apply image processing methods to convey more useful visual information to the wearers. We proposed two image processing strategies based on Itti's visual saliency map, region of interest (ROI) extraction, and image segmentation. Itti's saliency model generated a saliency map from the original image, in which salient regions were grouped into ROI by the fuzzy c-means clustering. Then Grabcut generated a proto-object from the ROI labeled image which was recombined with background and enhanced in two ways--8-4 separated pixelization (8-4 SP) and background edge extraction (BEE). Results showed that both 8-4 SP and BEE had significantly higher recognition accuracy in comparison with direct pixelization (DP). Each saliency-based image processing strategy was subject to the performance of image segmentation. Under good and perfect segmentation conditions, BEE and 8-4 SP obtained noticeably higher recognition accuracy than DP, and under bad segmentation condition, only BEE boosted the performance. The application of saliency-based image processing strategies was verified to be beneficial to object recognition in daily scenes under simulated prosthetic vision. They are hoped to help the development of the image processing module for future retinal prostheses, and thus provide more benefit for the patients. Copyright © 2015 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

Shear wave velocity is a useful marker for managing nonalcoholic steatohepatitis

PubMed Central

Osaki, Akihiko; Kubota, Tomoyuki; Suda, Takeshi; Igarashi, Masato; Nagasaki, Keisuke; Tsuchiya, Atsunori; Yano, Masahiko; Tamura, Yasushi; Takamura, Masaaki; Kawai, Hirokazu; Yamagiwa, Satoshi; Kikuchi, Toru; Nomoto, Minoru; Aoyagi, Yutaka

2010-01-01

AIM: To investigate whether a noninvasive measurement of tissue strain has a potential usefulness for management of nonalcoholic steatohepatitis (NASH). METHODS: In total 26 patients, 23 NASHs and 3 normal controls were enrolled in this study. NASH was staged based on Brunt criterion. At a region of interest (ROI), a shear wave was evoked by implementing an acoustic radiation force impulse (ARFI), and the propagation velocity was quantified. RESULTS: Shear wave velocity (SWV) could be reproducibly quantified at all ROIs in all subjects except for 4 NASH cases, in which a reliable SWV value was not calculated at several ROIs. An average SWV of 1.34 ± 0.26 m/s in fibrous stage 0-1 was significantly slower than 2.20 ± 0.74 m/s and 2.90 ± 1.01 m/s in stages 3 and 4, respectively, but was not significantly different from 1.79 ± 0.78 m/s in stage 2. When a cutoff value was set at 1.47 m/s, receiver operating characteristic analysis showed significance to dissociate stages 3 and 4 from stage 0-1 (P = 0.0092) with sensitivity, specificity and area under curve of 100%, 75% and 94.2%, respectively. In addition, the correlation between SWV and hyaluronic acid was significant (P < 0.0001), while a tendency toward negative correlation was observed with serum albumin (P = 0.053). CONCLUSION: The clinical implementation of ARFI provides noninvasive repeated evaluations of liver stiffness at an arbitrary position, which has the potential to shed new light on NASH management. PMID:20556839

Automatic classification of pathological myopia in retinal fundus images using PAMELA

NASA Astrophysics Data System (ADS)

Liu, Jiang; Wong, Damon W. K.; Tan, Ngan Meng; Zhang, Zhuo; Lu, Shijian; Lim, Joo Hwee; Li, Huiqi; Saw, Seang Mei; Tong, Louis; Wong, Tien Yin

2010-03-01

Pathological myopia is the seventh leading cause of blindness. We introduce a framework based on PAMELA (PAthological Myopia dEtection through peripapilLary Atrophy) for the detection of pathological myopia from fundus images. The framework consists of a pre-processing stage which extracts a region of interest centered on the optic disc. Subsequently, three analysis modules focus on detecting specific visual indicators. The optic disc tilt ratio module gives a measure of the axial elongation of the eye through inference from the deformation of the optic disc. In the texturebased ROI assessment module, contextual knowledge is used to demarcate the ROI into four distinct, clinically-relevant zones in which information from an entropy transform of the ROI is analyzed and metrics generated. In particular, the preferential appearance of peripapillary atrophy (PPA) in the temporal zone compared to the nasal zone is utilized by calculating ratios of the metrics. The PPA detection module obtains an outer boundary through a level-set method, and subtracts this region against the optic disc boundary. Temporal and nasal zones are obtained from the remnants to generate associated hue and color values. The outputs of the three modules are used as in a SVM model to determine the presence of pathological myopia in a retinal fundus image. Using images from the Singapore Eye Research Institute, the proposed framework reported an optimized accuracy of 90% and a sensitivity and specificity of 0.85 and 0.95 respectively, indicating promise for the use of the proposed system as a screening tool for pathological myopia.

Magnetic Resonance Imaging Profile of Blood–Brain Barrier Injury in Patients With Acute Intracerebral Hemorrhage

PubMed Central

Aksoy, Didem; Bammer, Roland; Mlynash, Michael; Venkatasubramanian, Chitra; Eyngorn, Irina; Snider, Ryan W.; Gupta, Sandeep N.; Narayana, Rashmi; Fischbein, Nancy; Wijman, Christine A. C.

2013-01-01

Background Spontaneous intracerebral hemorrhage (ICH) is associated with blood–brain barrier (BBB) injury, which is a poorly understood factor in ICH pathogenesis, potentially contributing to edema formation and perihematomal tissue injury. We aimed to assess and quantify BBB permeability following human spontaneous ICH using dynamic contrast‐enhanced magnetic resonance imaging (DCE MRI). We also investigated whether hematoma size or location affected the amount of BBB leakage. Methods and Results Twenty‐five prospectively enrolled patients from the Diagnostic Accuracy of MRI in Spontaneous intracerebral Hemorrhage (DASH) study were examined using DCE MRI at 1 week after symptom onset. Contrast agent dynamics in the brain tissue and general tracer kinetic modeling were used to estimate the forward leakage rate (Ktrans) in regions of interest (ROI) in and surrounding the hematoma and in contralateral mirror–image locations (control ROI). In all patients BBB permeability was significantly increased in the brain tissue immediately adjacent to the hematoma, that is, the hematoma rim, compared to the contralateral mirror ROI (P<0.0001). Large hematomas (>30 mL) had higher Ktrans values than small hematomas (P<0.005). Ktrans values of lobar hemorrhages were significantly higher than the Ktrans values of deep hemorrhages (P<0.005), independent of hematoma volume. Higher Ktrans values were associated with larger edema volumes. Conclusions BBB leakage in the brain tissue immediately bordering the hematoma can be measured and quantified by DCE MRI in human ICH. BBB leakage at 1 week is greater in larger hematomas as well as in hematomas in lobar locations and is associated with larger edema volumes. PMID:23709564

Comparison of breast DCE-MRI contrast time points for predicting response to neoadjuvant chemotherapy using deep convolutional neural network features with transfer learning

NASA Astrophysics Data System (ADS)

Huynh, Benjamin Q.; Antropova, Natasha; Giger, Maryellen L.

2017-03-01

DCE-MRI datasets have a temporal aspect to them, resulting in multiple regions of interest (ROIs) per subject, based on contrast time points. It is unclear how the different contrast time points vary in terms of usefulness for computer-aided diagnosis tasks in conjunction with deep learning methods. We thus sought to compare the different DCE-MRI contrast time points with regard to how well their extracted features predict response to neoadjuvant chemotherapy within a deep convolutional neural network. Our dataset consisted of 561 ROIs from 64 subjects. Each subject was categorized as a non-responder or responder, determined by recurrence-free survival. First, features were extracted from each ROI using a convolutional neural network (CNN) pre-trained on non-medical images. Linear discriminant analysis classifiers were then trained on varying subsets of these features, based on their contrast time points of origin. Leave-one-out cross validation (by subject) was used to assess performance in the task of estimating probability of response to therapy, with area under the ROC curve (AUC) as the metric. The classifier trained on features from strictly the pre-contrast time point performed the best, with an AUC of 0.85 (SD = 0.033). The remaining classifiers resulted in AUCs ranging from 0.71 (SD = 0.028) to 0.82 (SD = 0.027). Overall, we found the pre-contrast time point to be the most effective at predicting response to therapy and that including additional contrast time points moderately reduces variance.

Brain cortical thickness in male adolescents with serious substance use and conduct problems

PubMed Central

Chumachenko, Serhiy Y.; Sakai, Joseph T.; Dalwani, Manish S.; Mikulich-Gilbertson, Susan K.; Dunn, Robin; Tanabe, Jody; Young, Susan; McWilliams, Shannon K.; Banich, Marie T.; Crowley, Thomas J.

2016-01-01

Background Adolescents with substance use disorder (SUD) and conduct problems exhibit high levels of impulsivity and poor self-control. Limited work to date tests for brain cortical thickness differences in these youths. Objectives To investigate differences in cortical thickness between adolescents with substance use and conduct problems and controls. Methods We recruited 25 male adolescents with SUD, and 19 male adolescent controls, and completed structural 3T magnetic resonance brain imaging. Using the surface-based morphometry software FreeSurfer, we completed region-of-interest (ROI) analyses for group cortical thickness differences in left, and separately right, inferior frontal gyrus (IFG), orbitofrontal cortex (OFC) and insula. Using FreeSurfer, we completed whole-cerebrum analyses of group differences in cortical thickness. Results Versus controls, the SUD group showed no cortical thickness differences in ROI analyses. Controlling for age and IQ, no regions with cortical thickness differences were found using whole-cerebrum analyses (though secondary analyses co-varying IQ and whole-cerebrum cortical thickness yielded a between-group cortical thickness difference in the left posterior cingulate/precuneus). Secondary findings showed that the SUD group, relative to controls, demonstrated significantly less right>left asymmetry in IFG, had weaker insular-to-whole-cerebrum cortical thickness correlations, and showed a positive association between conduct disorder symptom count and cortical thickness in a superior temporal gyrus cluster. Conclusion Functional group differences may reflect a more nuanced cortical morphometric difference than ROI cortical thickness. Further investigation of morphometric differences is needed. If replicable findings can be established, they may aid in developing improved diagnostic or more targeted treatment approaches. PMID:26337200

PET/CT detectability and classification of simulated pulmonary lesions using an SUV correction scheme

NASA Astrophysics Data System (ADS)

Morrow, Andrew N.; Matthews, Kenneth L., II; Bujenovic, Steven

2008-03-01

Positron emission tomography (PET) and computed tomography (CT) together are a powerful diagnostic tool, but imperfect image quality allows false positive and false negative diagnoses to be made by any observer despite experience and training. This work investigates PET acquisition mode, reconstruction method and a standard uptake value (SUV) correction scheme on the classification of lesions as benign or malignant in PET/CT images, in an anthropomorphic phantom. The scheme accounts for partial volume effect (PVE) and PET resolution. The observer draws a region of interest (ROI) around the lesion using the CT dataset. A simulated homogenous PET lesion of the same shape as the drawn ROI is blurred with the point spread function (PSF) of the PET scanner to estimate the PVE, providing a scaling factor to produce a corrected SUV. Computer simulations showed that the accuracy of the corrected PET values depends on variations in the CT-drawn boundary and the position of the lesion with respect to the PET image matrix, especially for smaller lesions. Correction accuracy was affected slightly by mismatch of the simulation PSF and the actual scanner PSF. The receiver operating characteristic (ROC) study resulted in several observations. Using observer drawn ROIs, scaled tumor-background ratios (TBRs) more accurately represented actual TBRs than unscaled TBRs. For the PET images, 3D OSEM outperformed 2D OSEM, 3D OSEM outperformed 3D FBP, and 2D OSEM outperformed 2D FBP. The correction scheme significantly increased sensitivity and slightly increased accuracy for all acquisition and reconstruction modes at the cost of a small decrease in specificity.

Influence of image registration on ADC images computed from free-breathing diffusion MRIs of the abdomen

NASA Astrophysics Data System (ADS)

Guyader, Jean-Marie; Bernardin, Livia; Douglas, Naomi H. M.; Poot, Dirk H. J.; Niessen, Wiro J.; Klein, Stefan

2014-03-01

The apparent diffusion coefficient (ADC) is an imaging biomarker providing quantitative information on the diffusion of water in biological tissues. This measurement could be of relevance in oncology drug development, but it suffers from a lack of reliability. ADC images are computed by applying a voxelwise exponential fitting to multiple diffusion-weighted MR images (DW-MRIs) acquired with different diffusion gradients. In the abdomen, respiratory motion induces misalignments in the datasets, creating visible artefacts and inducing errors in the ADC maps. We propose a multistep post-acquisition motion compensation pipeline based on 3D non-rigid registrations. It corrects for motion within each image and brings all DW-MRIs to a common image space. The method is evaluated on 10 datasets of free-breathing abdominal DW-MRIs acquired from healthy volunteers. Regions of interest (ROIs) are segmented in the right part of the abdomen and measurements are compared in the three following cases: no image processing, Gaussian blurring of the raw DW-MRIs and registration. Results show that both blurring and registration improve the visual quality of ADC images, but compared to blurring, registration yields visually sharper images. Measurement uncertainty is reduced both by registration and blurring. For homogeneous ROIs, blurring and registration result in similar median ADCs, which are lower than without processing. In a ROI at the interface between liver and kidney, registration and blurring yield different median ADCs, suggesting that uncorrected motion introduces a bias. Our work indicates that averaging procedures on the scanner should be avoided, as they remove the opportunity to perform motion correction.

eeDAP: An Evaluation Environment for Digital and Analog Pathology

PubMed Central

Gallas, Brandon D.; Cheng, Wei-Chung; Gavrielides, Marios A.; Ivansky, Adam; Keay, Tyler; Wunderlich, Adam; Hipp, Jason; Hewitt, Stephen M.

2017-01-01

Purpose The purpose of this work is to present a platform for designing and executing studies that compare pathologists interpreting histopathology of whole slide images (WSI) on a computer display to pathologists interpreting glass slides on an optical microscope. Methods Here we present eeDAP, an evaluation environment for digital and analog pathology. The key element in eeDAP is the registration of the WSI to the glass slide. Registration is accomplished through computer control of the microscope stage and a camera mounted on the microscope that acquires images of the real time microscope view. Registration allows for the evaluation of the same regions of interest (ROIs) in both domains. This can reduce or eliminate disagreements that arise from pathologists interpreting different areas and focuses the comparison on image quality. Results We reduced the pathologist interpretation area from an entire glass slide (≈10–30 mm)2 to small ROIs <(50 um)2. We also made possible the evaluation of individual cells. Conclusions We summarize eeDAP’s software and hardware and provide calculations and corresponding images of the microscope field of view and the ROIs extracted from the WSIs. These calculations help provide a sense of eeDAP’s functionality and operating principles, while the images provide a sense of the look and feel of studies that can be conducted in the digital and analog domains. The eeDAP software can be downloaded from code.google.com (project: eeDAP) as Matlab source or as a precompiled stand-alone license-free application. PMID:28845079

Robust traffic sign detection using fuzzy shape recognizer

NASA Astrophysics Data System (ADS)

Li, Lunbo; Li, Jun; Sun, Jianhong

2009-10-01

A novel fuzzy approach for the detection of traffic signs in natural environments is presented. More than 3000 road images were collected under different weather conditions by a digital camera, and used for testing this approach. Every RGB image was converted into HSV colour space, and segmented by the hue and saturation thresholds. A symmetrical detector was used to extract the local features of the regions of interest (ROI), and the shape of ROI was determined by a fuzzy shape recognizer which invoked a set of fuzzy rules. The experimental results show that the proposed algorithm is translation, rotation and scaling invariant, and gives reliable shape recognition in complex traffic scenes where clustering and partial occlusion normally occur.