Image transport through a disordered optical fibre mediated by transverse Anderson localization.

PubMed

Karbasi, Salman; Frazier, Ryan J; Koch, Karl W; Hawkins, Thomas; Ballato, John; Mafi, Arash

2014-02-25

Transverse Anderson localization of light allows localized optical-beam-transport through a transversely disordered and longitudinally invariant medium. Its successful implementation in disordered optical fibres recently resulted in the propagation of localized beams of radii comparable to that of conventional optical fibres. Here we demonstrate optical image transport using transverse Anderson localization of light. The image transport quality obtained in the polymer disordered optical fibre is comparable to or better than some of the best commercially available multicore image fibres with less pixelation and higher contrast. It is argued that considerable improvement in image transport quality can be obtained in a disordered fibre made from a glass matrix with near wavelength-size randomly distributed air-holes with an air-hole fill-fraction of 50%. Our results open the way to device-level implementation of the transverse Anderson localization of light with potential applications in biological and medical imaging.

GF-3 SAR Image Despeckling Based on the Improved Non-Local Means Using Non-Subsampled Shearlet Transform

NASA Astrophysics Data System (ADS)

Shi, R.; Sun, Z.

2018-04-01

GF-3 synthetic aperture radar (SAR) images are rich in information and have obvious sparse features. However, the speckle appears in the GF-3 SAR images due to the coherent imaging system and it hinders the interpretation of images seriously. Recently, Shearlet is applied to the image processing with its best sparse representation. A new Shearlet-transform-based method is proposed in this paper based on the improved non-local means. Firstly, the logarithmic operation and the non-subsampled Shearlet transformation are applied to the GF-3 SAR image. Secondly, in order to solve the problems that the image details are smoothed overly and the weight distribution is affected by the speckle, a new non-local means is used for the transformed high frequency coefficient. Thirdly, the Shearlet reconstruction is carried out. Finally, the final filtered image is obtained by an exponential operation. Experimental results demonstrate that, compared with other despeckling methods, the proposed method can suppress the speckle effectively in homogeneous regions and has better capability of edge preserving.

Real-Time Multi-Target Localization from Unmanned Aerial Vehicles

PubMed Central

Wang, Xuan; Liu, Jinghong; Zhou, Qianfei

2016-01-01

In order to improve the reconnaissance efficiency of unmanned aerial vehicle (UAV) electro-optical stabilized imaging systems, a real-time multi-target localization scheme based on an UAV electro-optical stabilized imaging system is proposed. First, a target location model is studied. Then, the geodetic coordinates of multi-targets are calculated using the homogeneous coordinate transformation. On the basis of this, two methods which can improve the accuracy of the multi-target localization are proposed: (1) the real-time zoom lens distortion correction method; (2) a recursive least squares (RLS) filtering method based on UAV dead reckoning. The multi-target localization error model is established using Monte Carlo theory. In an actual flight, the UAV flight altitude is 1140 m. The multi-target localization results are within the range of allowable error. After we use a lens distortion correction method in a single image, the circular error probability (CEP) of the multi-target localization is reduced by 7%, and 50 targets can be located at the same time. The RLS algorithm can adaptively estimate the location data based on multiple images. Compared with multi-target localization based on a single image, CEP of the multi-target localization using RLS is reduced by 25%. The proposed method can be implemented on a small circuit board to operate in real time. This research is expected to significantly benefit small UAVs which need multi-target geo-location functions. PMID:28029145

Real-Time Multi-Target Localization from Unmanned Aerial Vehicles.

PubMed

Wang, Xuan; Liu, Jinghong; Zhou, Qianfei

2016-12-25

In order to improve the reconnaissance efficiency of unmanned aerial vehicle (UAV) electro-optical stabilized imaging systems, a real-time multi-target localization scheme based on an UAV electro-optical stabilized imaging system is proposed. First, a target location model is studied. Then, the geodetic coordinates of multi-targets are calculated using the homogeneous coordinate transformation. On the basis of this, two methods which can improve the accuracy of the multi-target localization are proposed: (1) the real-time zoom lens distortion correction method; (2) a recursive least squares (RLS) filtering method based on UAV dead reckoning. The multi-target localization error model is established using Monte Carlo theory. In an actual flight, the UAV flight altitude is 1140 m. The multi-target localization results are within the range of allowable error. After we use a lens distortion correction method in a single image, the circular error probability (CEP) of the multi-target localization is reduced by 7%, and 50 targets can be located at the same time. The RLS algorithm can adaptively estimate the location data based on multiple images. Compared with multi-target localization based on a single image, CEP of the multi-target localization using RLS is reduced by 25%. The proposed method can be implemented on a small circuit board to operate in real time. This research is expected to significantly benefit small UAVs which need multi-target geo-location functions.

“Lucky Averaging”: Quality improvement on Adaptive Optics Scanning Laser Ophthalmoscope Images

PubMed Central

Huang, Gang; Zhong, Zhangyi; Zou, Weiyao; Burns, Stephen A.

2012-01-01

Adaptive optics(AO) has greatly improved retinal image resolution. However, even with AO, temporal and spatial variations in image quality still occur due to wavefront fluctuations, intra-frame focus shifts and other factors. As a result, aligning and averaging images can produce a mean image that has lower resolution or contrast than the best images within a sequence. To address this, we propose an image post-processing scheme called “lucky averaging”, analogous to lucky imaging (Fried, 1978) based on computing the best local contrast over time. Results from eye data demonstrate improvements in image quality. PMID:21964097

A concept-based interactive biomedical image retrieval approach using visualness and spatial information

NASA Astrophysics Data System (ADS)

Rahman, Md M.; Antani, Sameer K.; Demner-Fushman, Dina; Thoma, George R.

2015-03-01

This paper presents a novel approach to biomedical image retrieval by mapping image regions to local concepts and represent images in a weighted entropy-based concept feature space. The term concept refers to perceptually distinguishable visual patches that are identified locally in image regions and can be mapped to a glossary of imaging terms. Further, the visual significance (e.g., visualness) of concepts is measured as Shannon entropy of pixel values in image patches and is used to refine the feature vector. Moreover, the system can assist user in interactively select a Region-Of-Interest (ROI) and search for similar image ROIs. Further, a spatial verification step is used as a post-processing step to improve retrieval results based on location information. The hypothesis that such approaches would improve biomedical image retrieval, is validated through experiments on a data set of 450 lung CT images extracted from journal articles from four different collections.

Visibility enhancement of color images using Type-II fuzzy membership function

NASA Astrophysics Data System (ADS)

Singh, Harmandeep; Khehra, Baljit Singh

2018-04-01

Images taken in poor environmental conditions decrease the visibility and hidden information of digital images. Therefore, image enhancement techniques are necessary for improving the significant details of these images. An extensive review has shown that histogram-based enhancement techniques greatly suffer from over/under enhancement issues. Fuzzy-based enhancement techniques suffer from over/under saturated pixels problems. In this paper, a novel Type-II fuzzy-based image enhancement technique has been proposed for improving the visibility of images. The Type-II fuzzy logic can automatically extract the local atmospheric light and roughly eliminate the atmospheric veil in local detail enhancement. The proposed technique has been evaluated on 10 well-known weather degraded color images and is also compared with four well-known existing image enhancement techniques. The experimental results reveal that the proposed technique outperforms others regarding visible edge ratio, color gradients and number of saturated pixels.

Image-Based Localization Aided Indoor Pedestrian Trajectory Estimation Using Smartphones

PubMed Central

Zhou, Yan; Zheng, Xianwei; Chen, Ruizhi; Xiong, Hanjiang; Guo, Sheng

2018-01-01

Accurately determining pedestrian location in indoor environments using consumer smartphones is a significant step in the development of ubiquitous localization services. Many different map-matching methods have been combined with pedestrian dead reckoning (PDR) to achieve low-cost and bias-free pedestrian tracking. However, this works only in areas with dense map constraints and the error accumulates in open areas. In order to achieve reliable localization without map constraints, an improved image-based localization aided pedestrian trajectory estimation method is proposed in this paper. The image-based localization recovers the pose of the camera from the 2D-3D correspondences between the 2D image positions and the 3D points of the scene model, previously reconstructed by a structure-from-motion (SfM) pipeline. This enables us to determine the initial location and eliminate the accumulative error of PDR when an image is successfully registered. However, the image is not always registered since the traditional 2D-to-3D matching rejects more and more correct matches when the scene becomes large. We thus adopt a robust image registration strategy that recovers initially unregistered images by integrating 3D-to-2D search. In the process, the visibility and co-visibility information is adopted to improve the efficiency when searching for the correspondences from both sides. The performance of the proposed method was evaluated through several experiments and the results demonstrate that it can offer highly acceptable pedestrian localization results in long-term tracking, with an error of only 0.56 m, without the need for dedicated infrastructures. PMID:29342123

Transcranial magnetic stimulation assisted by neuronavigation of magnetic resonance images

NASA Astrophysics Data System (ADS)

Viesca, N. Angeline; Alcauter, S. Sarael; Barrios, A. Fernando; González, O. Jorge J.; Márquez, F. Jorge A.

2012-10-01

Technological advance has improved the way scientists and doctors can learn about the brain and treat different disorders. A non-invasive method used for this is Transcranial Magnetic Stimulation (TMS) based on neuron excitation by electromagnetic induction. Combining this method with functional Magnetic Resonance Images (fMRI), it is intended to improve the localization technique of cortical brain structures by designing an extracranial localization system, based on Alcauter et al. work.

Many local pattern texture features: which is better for image-based multilabel human protein subcellular localization classification?

PubMed

Yang, Fan; Xu, Ying-Ying; Shen, Hong-Bin

2014-01-01

Human protein subcellular location prediction can provide critical knowledge for understanding a protein's function. Since significant progress has been made on digital microscopy, automated image-based protein subcellular location classification is urgently needed. In this paper, we aim to investigate more representative image features that can be effectively used for dealing with the multilabel subcellular image samples. We prepared a large multilabel immunohistochemistry (IHC) image benchmark from the Human Protein Atlas database and tested the performance of different local texture features, including completed local binary pattern, local tetra pattern, and the standard local binary pattern feature. According to our experimental results from binary relevance multilabel machine learning models, the completed local binary pattern, and local tetra pattern are more discriminative for describing IHC images when compared to the traditional local binary pattern descriptor. The combination of these two novel local pattern features and the conventional global texture features is also studied. The enhanced performance of final binary relevance classification model trained on the combined feature space demonstrates that different features are complementary to each other and thus capable of improving the accuracy of classification.

Advances in Lymphatic Imaging and Drug Delivery

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

Nune, Satish K.; Gunda, Padmaja; Majeti, Bharat K.

2011-09-10

Cancer remains the second leading cause of death after heart disease in the US. While metastasized cancers such as breast, prostate, and colon are incurable, before their distant spread, these diseases will have invaded the lymphatic system as a first step in their progression. Hence, proper evaluation of the disease state of the lymphatics which drain a tumor site is crucial to staging and the formation of a treatment plan. Current lymphatic imaging modalities with visible dyes and radionucleotide tracers offer limited sensitivity and poor resolution; however, newer tools using nanocarriers, quantum dots, and magnetic resonance imaging promise to vastlymore » improve the staging of lymphatic spread without needless biopsies. Concurrent with the improvement of lymphatic imaging agents, has been the development of drug carriers that can localize chemotherapy to the lymphatic system, thus improving the treatment of localized disease while minimizing the exposure of healthy organs to cytotoxic drugs. This review will focus on polymeric systems that have been developed for imaging and drug delivery to the lymph system, how these new devices improve upon current technologies, and where further improvement is needed.« less

Hybrid active contour model for inhomogeneous image segmentation with background estimation

NASA Astrophysics Data System (ADS)

Sun, Kaiqiong; Li, Yaqin; Zeng, Shan; Wang, Jun

2018-03-01

This paper proposes a hybrid active contour model for inhomogeneous image segmentation. The data term of the energy function in the active contour consists of a global region fitting term in a difference image and a local region fitting term in the original image. The difference image is obtained by subtracting the background from the original image. The background image is dynamically estimated from a linear filtered result of the original image on the basis of the varying curve locations during the active contour evolution process. As in existing local models, fitting the image to local region information makes the proposed model robust against an inhomogeneous background and maintains the accuracy of the segmentation result. Furthermore, fitting the difference image to the global region information makes the proposed model robust against the initial contour location, unlike existing local models. Experimental results show that the proposed model can obtain improved segmentation results compared with related methods in terms of both segmentation accuracy and initial contour sensitivity.

Multi-clues image retrieval based on improved color invariants

NASA Astrophysics Data System (ADS)

Liu, Liu; Li, Jian-Xun

2012-05-01

At present, image retrieval has a great progress in indexing efficiency and memory usage, which mainly benefits from the utilization of the text retrieval technology, such as the bag-of-features (BOF) model and the inverted-file structure. Meanwhile, because the robust local feature invariants are selected to establish BOF, the retrieval precision of BOF is enhanced, especially when it is applied to a large-scale database. However, these local feature invariants mainly consider the geometric variance of the objects in the images, and thus the color information of the objects fails to be made use of. Because of the development of the information technology and Internet, the majority of our retrieval objects is color images. Therefore, retrieval performance can be further improved through proper utilization of the color information. We propose an improved method through analyzing the flaw of shadow-shading quasi-invariant. The response and performance of shadow-shading quasi-invariant for the object edge with the variance of lighting are enhanced. The color descriptors of the invariant regions are extracted and integrated into BOF based on the local feature. The robustness of the algorithm and the improvement of the performance are verified in the final experiments.

Clinical evaluation of reducing acquisition time on single-photon emission computed tomography image quality using proprietary resolution recovery software.

PubMed

Aldridge, Matthew D; Waddington, Wendy W; Dickson, John C; Prakash, Vineet; Ell, Peter J; Bomanji, Jamshed B

2013-11-01

A three-dimensional model-based resolution recovery (RR) reconstruction algorithm that compensates for collimator-detector response, resulting in an improvement in reconstructed spatial resolution and signal-to-noise ratio of single-photon emission computed tomography (SPECT) images, was tested. The software is said to retain image quality even with reduced acquisition time. Clinically, any improvement in patient throughput without loss of quality is to be welcomed. Furthermore, future restrictions in radiotracer supplies may add value to this type of data analysis. The aims of this study were to assess improvement in image quality using the software and to evaluate the potential of performing reduced time acquisitions for bone and parathyroid SPECT applications. Data acquisition was performed using the local standard SPECT/CT protocols for 99mTc-hydroxymethylene diphosphonate bone and 99mTc-methoxyisobutylisonitrile parathyroid SPECT imaging. The principal modification applied was the acquisition of an eight-frame gated data set acquired using an ECG simulator with a fixed signal as the trigger. This had the effect of partitioning the data such that the effect of reduced time acquisitions could be assessed without conferring additional scanning time on the patient. The set of summed data sets was then independently reconstructed using the RR software to permit a blinded assessment of the effect of acquired counts upon reconstructed image quality as adjudged by three experienced observers. Data sets reconstructed with the RR software were compared with the local standard processing protocols; filtered back-projection and ordered-subset expectation-maximization. Thirty SPECT studies were assessed (20 bone and 10 parathyroid). The images reconstructed with the RR algorithm showed improved image quality for both full-time and half-time acquisitions over local current processing protocols (P<0.05). The RR algorithm improved image quality compared with local processing protocols and has been introduced into routine clinical use. SPECT acquisitions are now acquired at half of the time previously required. The method of binning the data can be applied to any other camera system to evaluate the reduction in acquisition time for similar processes. The potential for dose reduction is also inherent with this approach.

A Novel System for Correction of Relative Angular Displacement between Airborne Platform and UAV in Target Localization

PubMed Central

Liu, Chenglong; Liu, Jinghong; Song, Yueming; Liang, Huaidan

2017-01-01

This paper provides a system and method for correction of relative angular displacements between an Unmanned Aerial Vehicle (UAV) and its onboard strap-down photoelectric platform to improve localization accuracy. Because the angular displacements have an influence on the final accuracy, by attaching a measuring system to the platform, the texture image of platform base bulkhead can be collected in a real-time manner. Through the image registration, the displacement vector of the platform relative to its bulkhead can be calculated to further determine angular displacements. After being decomposed and superposed on the three attitude angles of the UAV, the angular displacements can reduce the coordinate transformation errors and thus improve the localization accuracy. Even a simple kind of method can improve the localization accuracy by 14.3%. PMID:28273845

A Novel System for Correction of Relative Angular Displacement between Airborne Platform and UAV in Target Localization.

PubMed

Liu, Chenglong; Liu, Jinghong; Song, Yueming; Liang, Huaidan

2017-03-04

This paper provides a system and method for correction of relative angular displacements between an Unmanned Aerial Vehicle (UAV) and its onboard strap-down photoelectric platform to improve localization accuracy. Because the angular displacements have an influence on the final accuracy, by attaching a measuring system to the platform, the texture image of platform base bulkhead can be collected in a real-time manner. Through the image registration, the displacement vector of the platform relative to its bulkhead can be calculated to further determine angular displacements. After being decomposed and superposed on the three attitude angles of the UAV, the angular displacements can reduce the coordinate transformation errors and thus improve the localization accuracy. Even a simple kind of method can improve the localization accuracy by 14.3%.

Local scattering property scales flow speed estimation in laser speckle contrast imaging

NASA Astrophysics Data System (ADS)

Miao, Peng; Chao, Zhen; Feng, Shihan; Yu, Hang; Ji, Yuanyuan; Li, Nan; Thakor, Nitish V.

2015-07-01

Laser speckle contrast imaging (LSCI) has been widely used in in vivo blood flow imaging. However, the effect of local scattering property (scattering coefficient µ s ) on blood flow speed estimation has not been well investigated. In this study, such an effect was quantified and involved in relation between speckle autocorrelation time τ c and flow speed v based on simulation flow experiments. For in vivo blood flow imaging, an improved estimation strategy was developed to eliminate the estimation bias due to the inhomogeneous distribution of the scattering property. Compared to traditional LSCI, a new estimation method significantly suppressed the imaging noise and improves the imaging contrast of vasculatures. Furthermore, the new method successfully captured the blood flow changes and vascular constriction patterns in rats’ cerebral cortex from normothermia to mild and moderate hypothermia.

Inferring Biological Structures from Super-Resolution Single Molecule Images Using Generative Models

PubMed Central

Maji, Suvrajit; Bruchez, Marcel P.

2012-01-01

Localization-based super resolution imaging is presently limited by sampling requirements for dynamic measurements of biological structures. Generating an image requires serial acquisition of individual molecular positions at sufficient density to define a biological structure, increasing the acquisition time. Efficient analysis of biological structures from sparse localization data could substantially improve the dynamic imaging capabilities of these methods. Using a feature extraction technique called the Hough Transform simple biological structures are identified from both simulated and real localization data. We demonstrate that these generative models can efficiently infer biological structures in the data from far fewer localizations than are required for complete spatial sampling. Analysis at partial data densities revealed efficient recovery of clathrin vesicle size distributions and microtubule orientation angles with as little as 10% of the localization data. This approach significantly increases the temporal resolution for dynamic imaging and provides quantitatively useful biological information. PMID:22629348

Novel Near-Lossless Compression Algorithm for Medical Sequence Images with Adaptive Block-Based Spatial Prediction.

PubMed

Song, Xiaoying; Huang, Qijun; Chang, Sheng; He, Jin; Wang, Hao

2016-12-01

To address the low compression efficiency of lossless compression and the low image quality of general near-lossless compression, a novel near-lossless compression algorithm based on adaptive spatial prediction is proposed for medical sequence images for possible diagnostic use in this paper. The proposed method employs adaptive block size-based spatial prediction to predict blocks directly in the spatial domain and Lossless Hadamard Transform before quantization to improve the quality of reconstructed images. The block-based prediction breaks the pixel neighborhood constraint and takes full advantage of the local spatial correlations found in medical images. The adaptive block size guarantees a more rational division of images and the improved use of the local structure. The results indicate that the proposed algorithm can efficiently compress medical images and produces a better peak signal-to-noise ratio (PSNR) under the same pre-defined distortion than other near-lossless methods.

Hierarchical Feature Extraction With Local Neural Response for Image Recognition.

PubMed

Li, Hong; Wei, Yantao; Li, Luoqing; Chen, C L P

2013-04-01

In this paper, a hierarchical feature extraction method is proposed for image recognition. The key idea of the proposed method is to extract an effective feature, called local neural response (LNR), of the input image with nontrivial discrimination and invariance properties by alternating between local coding and maximum pooling operation. The local coding, which is carried out on the locally linear manifold, can extract the salient feature of image patches and leads to a sparse measure matrix on which maximum pooling is carried out. The maximum pooling operation builds the translation invariance into the model. We also show that other invariant properties, such as rotation and scaling, can be induced by the proposed model. In addition, a template selection algorithm is presented to reduce computational complexity and to improve the discrimination ability of the LNR. Experimental results show that our method is robust to local distortion and clutter compared with state-of-the-art algorithms.

Biomedical image representation approach using visualness and spatial information in a concept feature space for interactive region-of-interest-based retrieval.

PubMed

Rahman, Md Mahmudur; Antani, Sameer K; Demner-Fushman, Dina; Thoma, George R

2015-10-01

This article presents an approach to biomedical image retrieval by mapping image regions to local concepts where images are represented in a weighted entropy-based concept feature space. The term "concept" refers to perceptually distinguishable visual patches that are identified locally in image regions and can be mapped to a glossary of imaging terms. Further, the visual significance (e.g., visualness) of concepts is measured as the Shannon entropy of pixel values in image patches and is used to refine the feature vector. Moreover, the system can assist the user in interactively selecting a region-of-interest (ROI) and searching for similar image ROIs. Further, a spatial verification step is used as a postprocessing step to improve retrieval results based on location information. The hypothesis that such approaches would improve biomedical image retrieval is validated through experiments on two different data sets, which are collected from open access biomedical literature.

Biomedical image representation approach using visualness and spatial information in a concept feature space for interactive region-of-interest-based retrieval

PubMed Central

Rahman, Md. Mahmudur; Antani, Sameer K.; Demner-Fushman, Dina; Thoma, George R.

2015-01-01

Abstract. This article presents an approach to biomedical image retrieval by mapping image regions to local concepts where images are represented in a weighted entropy-based concept feature space. The term “concept” refers to perceptually distinguishable visual patches that are identified locally in image regions and can be mapped to a glossary of imaging terms. Further, the visual significance (e.g., visualness) of concepts is measured as the Shannon entropy of pixel values in image patches and is used to refine the feature vector. Moreover, the system can assist the user in interactively selecting a region-of-interest (ROI) and searching for similar image ROIs. Further, a spatial verification step is used as a postprocessing step to improve retrieval results based on location information. The hypothesis that such approaches would improve biomedical image retrieval is validated through experiments on two different data sets, which are collected from open access biomedical literature. PMID:26730398

Humans make efficient use of natural image statistics when performing spatial interpolation.

PubMed

D'Antona, Anthony D; Perry, Jeffrey S; Geisler, Wilson S

2013-12-16

Visual systems learn through evolution and experience over the lifespan to exploit the statistical structure of natural images when performing visual tasks. Understanding which aspects of this statistical structure are incorporated into the human nervous system is a fundamental goal in vision science. To address this goal, we measured human ability to estimate the intensity of missing image pixels in natural images. Human estimation accuracy is compared with various simple heuristics (e.g., local mean) and with optimal observers that have nearly complete knowledge of the local statistical structure of natural images. Human estimates are more accurate than those of simple heuristics, and they match the performance of an optimal observer that knows the local statistical structure of relative intensities (contrasts). This optimal observer predicts the detailed pattern of human estimation errors and hence the results place strong constraints on the underlying neural mechanisms. However, humans do not reach the performance of an optimal observer that knows the local statistical structure of the absolute intensities, which reflect both local relative intensities and local mean intensity. As predicted from a statistical analysis of natural images, human estimation accuracy is negligibly improved by expanding the context from a local patch to the whole image. Our results demonstrate that the human visual system exploits efficiently the statistical structure of natural images.

Utilization of targeted near-infrared molecular imaging to improve pulmonary metastasectomy of osteosarcomas

NASA Astrophysics Data System (ADS)

Predina, Jarrod D.; Newton, Andrew; Deshpande, Charuhas; Low, Philip; Singhal, Sunil

2018-01-01

Pulmonary metastasectomy for osteosarcoma provides a select group of patients an opportunity for long-term survival and possible cure. Unfortunately, a complete metastasectomy is challenging due an inability to accurately identify lesions that lay below the threshold of preoperative imaging or intraoperative visual and tactile inspection. Growing evidence suggests that osteosarcomas express a number of unique molecular markers, including the folate receptor alpha. In this case report, we describe the application of a folate receptor-targeted, near-infrared optical contrast agent (OTL38) to improve osteosarcoma localization during minimally invasive pulmonary resection. In addition to localizing preoperatively identified lesions, this technology helped identify additional disease that was undetected on preoperative imaging or with traditional intraoperative techniques. This report marks the first successful utilization of a molecular imaging probe useful for osteosarcomas. This technology may provide a unique approach to improve pulmonary metastasectomy of osteosarcomas.

Usefulness of composite methionine-positron emission tomography/3.0-tesla magnetic resonance imaging to detect the localization and extent of early-stage Cushing adenoma.

PubMed

Ikeda, Hidetoshi; Abe, Takehiko; Watanabe, Kazuo

2010-04-01

Fifty to eighty percent of Cushing disease is diagnosed by typical endocrine responses. Recently, the number of diagnoses of Cushing disease without typical Cushing syndrome has been increasing; therefore, improving ways to determine the localization of the adenoma and making an early diagnosis is important. This study was undertaken to determine the present diagnostic accuracy for Cushing microadenoma and to compare the differences in diagnostic accuracy between MR imaging and PET/MR imaging. During the past 3 years the authors analyzed the diagnostic accuracy in a series of 35 patients with Cushing adenoma that was verified by surgical pituitary exploration. All 35 cases of Cushing disease, including 20 cases of "overt" and 15 cases of "preclinical" Cushing disease, were studied. Superconductive MR images (1.5 or 3.0 T) and composite images from FDG-PET or methionine (MET)-PET and 3.0-T MR imaging were compared with the localization of adenomas verified by surgery. The diagnostic accuracy of superconductive MR imaging for detecting the localization of Cushing microadenoma was only 40%. The causes of unsatisfactory results for superconductive MR imaging were false-negative results (10 cases), false-positive results (6 cases), and instances of double pituitary adenomas (3 cases). In contrast, the accuracy of microadenoma localization using MET-PET/3.0-T MR imaging was 100% and that of FDG-PET/3.0-T MR imaging was 73%. Moreover, the adenoma location was better delineated on MET-PET/MR images than on FDG-PET/MR images. There was no significant difference in maximum standard uptake value of adenomas evaluated by MET-PET between preclinical Cushing disease and overt Cushing disease. Composite MET-PET/3.0-T MR imaging is useful for the improvement of the delineation of Cushing microadenoma and offers high-quality detectability for early-stage Cushing adenoma.

Image Segmentation Method Using Fuzzy C Mean Clustering Based on Multi-Objective Optimization

NASA Astrophysics Data System (ADS)

Chen, Jinlin; Yang, Chunzhi; Xu, Guangkui; Ning, Li

2018-04-01

Image segmentation is not only one of the hottest topics in digital image processing, but also an important part of computer vision applications. As one kind of image segmentation algorithms, fuzzy C-means clustering is an effective and concise segmentation algorithm. However, the drawback of FCM is that it is sensitive to image noise. To solve the problem, this paper designs a novel fuzzy C-mean clustering algorithm based on multi-objective optimization. We add a parameter λ to the fuzzy distance measurement formula to improve the multi-objective optimization. The parameter λ can adjust the weights of the pixel local information. In the algorithm, the local correlation of neighboring pixels is added to the improved multi-objective mathematical model to optimize the clustering cent. Two different experimental results show that the novel fuzzy C-means approach has an efficient performance and computational time while segmenting images by different type of noises.

The power of Kawaii: viewing cute images promotes a careful behavior and narrows attentional focus.

PubMed

Nittono, Hiroshi; Fukushima, Michiko; Yano, Akihiro; Moriya, Hiroki

2012-01-01

Kawaii (a Japanese word meaning "cute") things are popular because they produce positive feelings. However, their effect on behavior remains unclear. In this study, three experiments were conducted to examine the effects of viewing cute images on subsequent task performance. In the first experiment, university students performed a fine motor dexterity task before and after viewing images of baby or adult animals. Performance indexed by the number of successful trials increased after viewing cute images (puppies and kittens; M ± SE=43.9 ± 10.3% improvement) more than after viewing images that were less cute (dogs and cats; 11.9 ± 5.5% improvement). In the second experiment, this finding was replicated by using a non-motor visual search task. Performance improved more after viewing cute images (15.7 ± 2.2% improvement) than after viewing less cute images (1.4 ± 2.1% improvement). Viewing images of pleasant foods was ineffective in improving performance (1.2 ± 2.1%). In the third experiment, participants performed a global-local letter task after viewing images of baby animals, adult animals, and neutral objects. In general, global features were processed faster than local features. However, this global precedence effect was reduced after viewing cute images. Results show that participants performed tasks requiring focused attention more carefully after viewing cute images. This is interpreted as the result of a narrowed attentional focus induced by the cuteness-triggered positive emotion that is associated with approach motivation and the tendency toward systematic processing. For future applications, cute objects may be used as an emotion elicitor to induce careful behavioral tendencies in specific situations, such as driving and office work.

The Power of Kawaii: Viewing Cute Images Promotes a Careful Behavior and Narrows Attentional Focus

PubMed Central

Nittono, Hiroshi; Fukushima, Michiko; Yano, Akihiro; Moriya, Hiroki

2012-01-01

Kawaii (a Japanese word meaning “cute”) things are popular because they produce positive feelings. However, their effect on behavior remains unclear. In this study, three experiments were conducted to examine the effects of viewing cute images on subsequent task performance. In the first experiment, university students performed a fine motor dexterity task before and after viewing images of baby or adult animals. Performance indexed by the number of successful trials increased after viewing cute images (puppies and kittens; M ± SE = 43.9±10.3% improvement) more than after viewing images that were less cute (dogs and cats; 11.9±5.5% improvement). In the second experiment, this finding was replicated by using a non-motor visual search task. Performance improved more after viewing cute images (15.7±2.2% improvement) than after viewing less cute images (1.4±2.1% improvement). Viewing images of pleasant foods was ineffective in improving performance (1.2±2.1%). In the third experiment, participants performed a global–local letter task after viewing images of baby animals, adult animals, and neutral objects. In general, global features were processed faster than local features. However, this global precedence effect was reduced after viewing cute images. Results show that participants performed tasks requiring focused attention more carefully after viewing cute images. This is interpreted as the result of a narrowed attentional focus induced by the cuteness-triggered positive emotion that is associated with approach motivation and the tendency toward systematic processing. For future applications, cute objects may be used as an emotion elicitor to induce careful behavioral tendencies in specific situations, such as driving and office work. PMID:23050022

Fast live cell imaging at nanometer scale using annihilating filter-based low-rank Hankel matrix approach

NASA Astrophysics Data System (ADS)

Min, Junhong; Carlini, Lina; Unser, Michael; Manley, Suliana; Ye, Jong Chul

2015-09-01

Localization microscopy such as STORM/PALM can achieve a nanometer scale spatial resolution by iteratively localizing fluorescence molecules. It was shown that imaging of densely activated molecules can accelerate temporal resolution which was considered as major limitation of localization microscopy. However, this higher density imaging needs to incorporate advanced localization algorithms to deal with overlapping point spread functions (PSFs). In order to address this technical challenges, previously we developed a localization algorithm called FALCON1, 2 using a quasi-continuous localization model with sparsity prior on image space. It was demonstrated in both 2D/3D live cell imaging. However, it has several disadvantages to be further improved. Here, we proposed a new localization algorithm using annihilating filter-based low rank Hankel structured matrix approach (ALOHA). According to ALOHA principle, sparsity in image domain implies the existence of rank-deficient Hankel structured matrix in Fourier space. Thanks to this fundamental duality, our new algorithm can perform data-adaptive PSF estimation and deconvolution of Fourier spectrum, followed by truly grid-free localization using spectral estimation technique. Furthermore, all these optimizations are conducted on Fourier space only. We validated the performance of the new method with numerical experiments and live cell imaging experiment. The results confirmed that it has the higher localization performances in both experiments in terms of accuracy and detection rate.

A microwave imaging-based 3D localization algorithm for an in-body RF source as in wireless capsule endoscopes.

PubMed

Chandra, Rohit; Balasingham, Ilangko

2015-01-01

A microwave imaging-based technique for 3D localization of an in-body RF source is presented. Such a technique can be useful for localization of an RF source as in wireless capsule endoscopes for positioning of any abnormality in the gastrointestinal tract. Microwave imaging is used to determine the dielectric properties (relative permittivity and conductivity) of the tissues that are required for a precise localization. A 2D microwave imaging algorithm is used for determination of the dielectric properties. Calibration method is developed for removing any error due to the used 2D imaging algorithm on the imaging data of a 3D body. The developed method is tested on a simple 3D heterogeneous phantom through finite-difference-time-domain simulations. Additive white Gaussian noise at the signal-to-noise ratio of 30 dB is added to the simulated data to make them more realistic. The developed calibration method improves the imaging and the localization accuracy. Statistics on the localization accuracy are generated by randomly placing the RF source at various positions inside the small intestine of the phantom. The cumulative distribution function of the localization error is plotted. In 90% of the cases, the localization accuracy was found within 1.67 cm, showing the capability of the developed method for 3D localization.

Improved field localization in transcranial magnetic stimulation of the brain with the utilization of a conductive shield plate in the stimulator.

PubMed

Kim, Dong-Hun; Georghiou, George E; Won, Chulho

2006-04-01

In this paper, a carefully designed conductive shield plate is presented, which helps to improve localization of the electric field distribution induced by transcranial magnetic stimulation for neuron stimulation. The shield plate is introduced between a figure-of-eight coil and the head. In order to accurately predict the field distribution inside the brain and to examine the effects of the shield plate, a realistic head model is constructed from magnetic resonance image data with the help of image processing tools and the finite-element method in three dimensions is employed. Finally, to show the improvements obtained, the results are compared with two conventional coil designs. It is found that an incorporation of the shield plate into the coil, effectively improves the induced field localization by more than 50%, and prevents other parts of the brain from exposure to high pulsed magnetic fields.

Deep learning massively accelerates super-resolution localization microscopy.

PubMed

Ouyang, Wei; Aristov, Andrey; Lelek, Mickaël; Hao, Xian; Zimmer, Christophe

2018-06-01

The speed of super-resolution microscopy methods based on single-molecule localization, for example, PALM and STORM, is limited by the need to record many thousands of frames with a small number of observed molecules in each. Here, we present ANNA-PALM, a computational strategy that uses artificial neural networks to reconstruct super-resolution views from sparse, rapidly acquired localization images and/or widefield images. Simulations and experimental imaging of microtubules, nuclear pores, and mitochondria show that high-quality, super-resolution images can be reconstructed from up to two orders of magnitude fewer frames than usually needed, without compromising spatial resolution. Super-resolution reconstructions are even possible from widefield images alone, though adding localization data improves image quality. We demonstrate super-resolution imaging of >1,000 fields of view containing >1,000 cells in ∼3 h, yielding an image spanning spatial scales from ∼20 nm to ∼2 mm. The drastic reduction in acquisition time and sample irradiation afforded by ANNA-PALM enables faster and gentler high-throughput and live-cell super-resolution imaging.

Cryo-balloon catheter localization in fluoroscopic images

NASA Astrophysics Data System (ADS)

Kurzendorfer, Tanja; Brost, Alexander; Jakob, Carolin; Mewes, Philip W.; Bourier, Felix; Koch, Martin; Kurzidim, Klaus; Hornegger, Joachim; Strobel, Norbert

2013-03-01

Minimally invasive catheter ablation has become the preferred treatment option for atrial fibrillation. Although the standard ablation procedure involves ablation points set by radio-frequency catheters, cryo-balloon catheters have even been reported to be more advantageous in certain cases. As electro-anatomical mapping systems do not support cryo-balloon ablation procedures, X-ray guidance is needed. However, current methods to provide support for cryo-balloon catheters in fluoroscopically guided ablation procedures rely heavily on manual user interaction. To improve this, we propose a first method for automatic cryo-balloon catheter localization in fluoroscopic images based on a blob detection algorithm. Our method is evaluated on 24 clinical images from 17 patients. The method successfully detected the cryoballoon in 22 out of 24 images, yielding a success rate of 91.6 %. The successful localization achieved an accuracy of 1.00 mm +/- 0.44 mm. Even though our methods currently fails in 8.4 % of the images available, it still offers a significant improvement over manual methods. Furthermore, detecting a landmark point along the cryo-balloon catheter can be a very important step for additional post-processing operations.

Automated characterisation of ultrasound images of ovarian tumours: the diagnostic accuracy of a support vector machine and image processing with a local binary pattern operator.

PubMed

Khazendar, S; Sayasneh, A; Al-Assam, H; Du, H; Kaijser, J; Ferrara, L; Timmerman, D; Jassim, S; Bourne, T

2015-01-01

Preoperative characterisation of ovarian masses into benign or malignant is of paramount importance to optimise patient management. In this study, we developed and validated a computerised model to characterise ovarian masses as benign or malignant. Transvaginal 2D B mode static ultrasound images of 187 ovarian masses with known histological diagnosis were included. Images were first pre-processed and enhanced, and Local Binary Pattern Histograms were then extracted from 2 × 2 blocks of each image. A Support Vector Machine (SVM) was trained using stratified cross validation with randomised sampling. The process was repeated 15 times and in each round 100 images were randomly selected. The SVM classified the original non-treated static images as benign or malignant masses with an average accuracy of 0.62 (95% CI: 0.59-0.65). This performance significantly improved to an average accuracy of 0.77 (95% CI: 0.75-0.79) when images were pre-processed, enhanced and treated with a Local Binary Pattern operator (mean difference 0.15: 95% 0.11-0.19, p < 0.0001, two-tailed t test). We have shown that an SVM can classify static 2D B mode ultrasound images of ovarian masses into benign and malignant categories. The accuracy improves if texture related LBP features extracted from the images are considered.

Physics-based elastic image registration using splines and including landmark localization uncertainties.

PubMed

Wörz, Stefan; Rohr, Karl

2006-01-01

We introduce an elastic registration approach which is based on a physical deformation model and uses Gaussian elastic body splines (GEBS). We formulate an extended energy functional related to the Navier equation under Gaussian forces which also includes landmark localization uncertainties. These uncertainties are characterized by weight matrices representing anisotropic errors. Since the approach is based on a physical deformation model, cross-effects in elastic deformations can be taken into account. Moreover, we have a free parameter to control the locality of the transformation for improved registration of local geometric image differences. We demonstrate the applicability of our scheme based on 3D CT images from the Truth Cube experiment, 2D MR images of the brain, as well as 2D gel electrophoresis images. It turns out that the new scheme achieves more accurate results compared to previous approaches.

Fast and efficient molecule detection in localization-based super-resolution microscopy by parallel adaptive histogram equalization.

PubMed

Li, Yiming; Ishitsuka, Yuji; Hedde, Per Niklas; Nienhaus, G Ulrich

2013-06-25

In localization-based super-resolution microscopy, individual fluorescent markers are stochastically photoactivated and subsequently localized within a series of camera frames, yielding a final image with a resolution far beyond the diffraction limit. Yet, before localization can be performed, the subregions within the frames where the individual molecules are present have to be identified-oftentimes in the presence of high background. In this work, we address the importance of reliable molecule identification for the quality of the final reconstructed super-resolution image. We present a fast and robust algorithm (a-livePALM) that vastly improves the molecule detection efficiency while minimizing false assignments that can lead to image artifacts.

Adaptive noise correction of dual-energy computed tomography images.

PubMed

Maia, Rafael Simon; Jacob, Christian; Hara, Amy K; Silva, Alvin C; Pavlicek, William; Mitchell, J Ross

2016-04-01

Noise reduction in material density images is a necessary preprocessing step for the correct interpretation of dual-energy computed tomography (DECT) images. In this paper we describe a new method based on a local adaptive processing to reduce noise in DECT images An adaptive neighborhood Wiener (ANW) filter was implemented and customized to use local characteristics of material density images. The ANW filter employs a three-level wavelet approach, combined with the application of an anisotropic diffusion filter. Material density images and virtual monochromatic images are noise corrected with two resulting noise maps. The algorithm was applied and quantitatively evaluated in a set of 36 images. From that set of images, three are shown here, and nine more are shown in the online supplementary material. Processed images had higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) than the raw material density images. The average improvements in SNR and CNR for the material density images were 56.5 and 54.75%, respectively. We developed a new DECT noise reduction algorithm. We demonstrate throughout a series of quantitative analyses that the algorithm improves the quality of material density images and virtual monochromatic images.

Image enhancement and color constancy for a vehicle-mounted change detection system

NASA Astrophysics Data System (ADS)

Tektonidis, Marco; Monnin, David

2016-10-01

Vehicle-mounted change detection systems allow to improve situational awareness on outdoor itineraries of inter- est. Since the visibility of acquired images is often affected by illumination effects (e.g., shadows) it is important to enhance local contrast. For the analysis and comparison of color images depicting the same scene at different time points it is required to compensate color and lightness inconsistencies caused by the different illumination conditions. We have developed an approach for image enhancement and color constancy based on the center/surround Retinex model and the Gray World hypothesis. The combination of the two methods using a color processing function improves color rendition, compared to both methods. The use of stacked integral images (SII) allows to efficiently perform local image processing. Our combined Retinex/Gray World approach has been successfully applied to image sequences acquired on outdoor itineraries at different time points and a comparison with previous Retinex-based approaches has been carried out.

Example-Based Image Colorization Using Locality Consistent Sparse Representation.

PubMed

Bo Li; Fuchen Zhao; Zhuo Su; Xiangguo Liang; Yu-Kun Lai; Rosin, Paul L

2017-11-01

Image colorization aims to produce a natural looking color image from a given gray-scale image, which remains a challenging problem. In this paper, we propose a novel example-based image colorization method exploiting a new locality consistent sparse representation. Given a single reference color image, our method automatically colorizes the target gray-scale image by sparse pursuit. For efficiency and robustness, our method operates at the superpixel level. We extract low-level intensity features, mid-level texture features, and high-level semantic features for each superpixel, which are then concatenated to form its descriptor. The collection of feature vectors for all the superpixels from the reference image composes the dictionary. We formulate colorization of target superpixels as a dictionary-based sparse reconstruction problem. Inspired by the observation that superpixels with similar spatial location and/or feature representation are likely to match spatially close regions from the reference image, we further introduce a locality promoting regularization term into the energy formulation, which substantially improves the matching consistency and subsequent colorization results. Target superpixels are colorized based on the chrominance information from the dominant reference superpixels. Finally, to further improve coherence while preserving sharpness, we develop a new edge-preserving filter for chrominance channels with the guidance from the target gray-scale image. To the best of our knowledge, this is the first work on sparse pursuit image colorization from single reference images. Experimental results demonstrate that our colorization method outperforms the state-of-the-art methods, both visually and quantitatively using a user study.

Multiresolution image registration in digital x-ray angiography with intensity variation modeling.

PubMed

Nejati, Mansour; Pourghassem, Hossein

2014-02-01

Digital subtraction angiography (DSA) is a widely used technique for visualization of vessel anatomy in diagnosis and treatment. However, due to unavoidable patient motions, both externally and internally, the subtracted angiography images often suffer from motion artifacts that adversely affect the quality of the medical diagnosis. To cope with this problem and improve the quality of DSA images, registration algorithms are often employed before subtraction. In this paper, a novel elastic registration algorithm for registration of digital X-ray angiography images, particularly for the coronary location, is proposed. This algorithm includes a multiresolution search strategy in which a global transformation is calculated iteratively based on local search in coarse and fine sub-image blocks. The local searches are accomplished in a differential multiscale framework which allows us to capture both large and small scale transformations. The local registration transformation also explicitly accounts for local variations in the image intensities which incorporated into our model as a change of local contrast and brightness. These local transformations are then smoothly interpolated using thin-plate spline interpolation function to obtain the global model. Experimental results with several clinical datasets demonstrate the effectiveness of our algorithm in motion artifact reduction.

Adaptive Optics Images of the Galactic Center: Using Empirical Noise-maps to Optimize Image Analysis

NASA Astrophysics Data System (ADS)

Albers, Saundra; Witzel, Gunther; Meyer, Leo; Sitarski, Breann; Boehle, Anna; Ghez, Andrea M.

2015-01-01

Adaptive Optics images are one of the most important tools in studying our Galactic Center. In-depth knowledge of the noise characteristics is crucial to optimally analyze this data. Empirical noise estimates - often represented by a constant value for the entire image - can be greatly improved by computing the local detector properties and photon noise contributions pixel by pixel. To comprehensively determine the noise, we create a noise model for each image using the three main contributors—photon noise of stellar sources, sky noise, and dark noise. We propagate the uncertainties through all reduction steps and analyze the resulting map using Starfinder. The estimation of local noise properties helps to eliminate fake detections while improving the detection limit of fainter sources. We predict that a rigorous understanding of noise allows a more robust investigation of the stellar dynamics in the center of our Galaxy.

Boosting instance prototypes to detect local dermoscopic features.

PubMed

Situ, Ning; Yuan, Xiaojing; Zouridakis, George

2010-01-01

Local dermoscopic features are useful in many dermoscopic criteria for skin cancer detection. We address the problem of detecting local dermoscopic features from epiluminescence (ELM) microscopy skin lesion images. We formulate the recognition of local dermoscopic features as a multi-instance learning (MIL) problem. We employ the method of diverse density (DD) and evidence confidence (EC) function to convert MIL to a single-instance learning (SIL) problem. We apply Adaboost to improve the classification performance with support vector machines (SVMs) as the base classifier. We also propose to boost the selection of instance prototypes through changing the data weights in the DD function. We validate the methods on detecting ten local dermoscopic features from a dataset with 360 images. We compare the performance of the MIL approach, its boosting version, and a baseline method without using MIL. Our results show that boosting can provide performance improvement compared to the other two methods.

Statistical lamb wave localization based on extreme value theory

NASA Astrophysics Data System (ADS)

Harley, Joel B.

2018-04-01

Guided wave localization methods based on delay-and-sum imaging, matched field processing, and other techniques have been designed and researched to create images that locate and describe structural damage. The maximum value of these images typically represent an estimated damage location. Yet, it is often unclear if this maximum value, or any other value in the image, is a statistically significant indicator of damage. Furthermore, there are currently few, if any, approaches to assess the statistical significance of guided wave localization images. As a result, we present statistical delay-and-sum and statistical matched field processing localization methods to create statistically significant images of damage. Our framework uses constant rate of false alarm statistics and extreme value theory to detect damage with little prior information. We demonstrate our methods with in situ guided wave data from an aluminum plate to detect two 0.75 cm diameter holes. Our results show an expected improvement in statistical significance as the number of sensors increase. With seventeen sensors, both methods successfully detect damage with statistical significance.

Improved 2D/3D registration robustness using local spatial information

NASA Astrophysics Data System (ADS)

De Momi, Elena; Eckman, Kort; Jaramaz, Branislav; DiGioia, Anthony, III

2006-03-01

Xalign is a tool designed to measure implant orientation after joint arthroplasty by co-registering a projection of an implant model and a digitally reconstructed radiograph of the patient's anatomy with a post operative x-ray. A mutual information based registration method is used to automate alignment. When using basic mutual information, the presence of local maxima can result in misregistration. To increase robustness of registration, our research is aimed at improving the similarity function by modifying the information measure and incorporating local spatial information. A test dataset with known groundtruth parameters was created to evaluate the performance of this measure. A synthetic radiograph was generated first from a preoperative pelvic CT scan to act as the gold standard. The voxel weights used to generate the image were then modified and new images were generated with the CT rigidly transformed. The roll, pitch and yaw angles span a range of -10/+10 degrees, while x, y and z translations range from -10mm to +10mm. These images were compared with the reference image. The proposed cost function correctly identified the correct pose in all tests and did not exhibit any local maxima which would slow or prevent locating the global maximum.

Evaluation of methods to produce an image library for automatic patient model localization for dose mapping during fluoroscopically guided procedures

NASA Astrophysics Data System (ADS)

Kilian-Meneghin, Josh; Xiong, Z.; Rudin, S.; Oines, A.; Bednarek, D. R.

2017-03-01

The purpose of this work is to evaluate methods for producing a library of 2D-radiographic images to be correlated to clinical images obtained during a fluoroscopically-guided procedure for automated patient-model localization. The localization algorithm will be used to improve the accuracy of the skin-dose map superimposed on the 3D patient- model of the real-time Dose-Tracking-System (DTS). For the library, 2D images were generated from CT datasets of the SK-150 anthropomorphic phantom using two methods: Schmid's 3D-visualization tool and Plastimatch's digitally-reconstructed-radiograph (DRR) code. Those images, as well as a standard 2D-radiographic image, were correlated to a 2D-fluoroscopic image of a phantom, which represented the clinical-fluoroscopic image, using the Corr2 function in Matlab. The Corr2 function takes two images and outputs the relative correlation between them, which is fed into the localization algorithm. Higher correlation means better alignment of the 3D patient-model with the patient image. In this instance, it was determined that the localization algorithm will succeed when Corr2 returns a correlation of at least 50%. The 3D-visualization tool images returned 55-80% correlation relative to the fluoroscopic-image, which was comparable to the correlation for the radiograph. The DRR images returned 61-90% correlation, again comparable to the radiograph. Both methods prove to be sufficient for the localization algorithm and can be produced quickly; however, the DRR method produces more accurate grey-levels. Using the DRR code, a library at varying angles can be produced for the localization algorithm.

Advancements in MR Imaging of the Prostate: From Diagnosis to Interventions

PubMed Central

Bonekamp, David; Jacobs, Michael A.; El-Khouli, Riham; Stoianovici, Dan

2011-01-01

Prostate cancer is the most frequently diagnosed cancer in males and the second leading cause of cancer-related death in men. Assessment of prostate cancer can be divided into detection, localization, and staging; accurate assessment is a prerequisite for optimal clinical management and therapy selection. Magnetic resonance (MR) imaging has been shown to be of particular help in localization and staging of prostate cancer. Traditional prostate MR imaging has been based on morphologic imaging with standard T1-weighted and T2-weighted sequences, which has limited accuracy. Recent advances include additional functional and physiologic MR imaging techniques (diffusion-weighted imaging, MR spectroscopy, and perfusion imaging), which allow extension of the obtainable information beyond anatomic assessment. Multiparametric MR imaging provides the highest accuracy in diagnosis and staging of prostate cancer. In addition, improvements in MR imaging hardware and software (3-T vs 1.5-T imaging) continue to improve spatial and temporal resolution and the signal-to-noise ratio of MR imaging examinations. Another recent advancement in the field is MR imaging guidance for targeted prostate biopsy, which is an alternative to the current standard of transrectal ultrasonography–guided systematic biopsy. © RSNA, 2011 PMID:21571651

Ultrasound guided fluorescence molecular tomography with improved quantification by an attenuation compensated born-normalization and in vivo preclinical study of cancer

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

Li, Baoqiang; Berti, Romain; Abran, Maxime

2014-05-15

Ultrasound imaging, having the advantages of low-cost and non-invasiveness over MRI and X-ray CT, was reported by several studies as an adequate complement to fluorescence molecular tomography with the perspective of improving localization and quantification of fluorescent molecular targets in vivo. Based on the previous work, an improved dual-modality Fluorescence-Ultrasound imaging system was developed and then validated in imaging study with preclinical tumor model. Ultrasound imaging and a profilometer were used to obtain the anatomical prior information and 3D surface, separately, to precisely extract the tissue boundary on both sides of sample in order to achieve improved fluorescence reconstruction. Furthermore,more » a pattern-based fluorescence reconstruction on the detection side was incorporated to enable dimensional reduction of the dataset while keeping the useful information for reconstruction. Due to its putative role in the current imaging geometry and the chosen reconstruction technique, we developed an attenuation compensated Born-normalization method to reduce the attenuation effects and cancel off experimental factors when collecting quantitative fluorescence datasets over large area. Results of both simulation and phantom study demonstrated that fluorescent targets could be recovered accurately and quantitatively using this reconstruction mechanism. Finally, in vivo experiment confirms that the imaging system associated with the proposed image reconstruction approach was able to extract both functional and anatomical information, thereby improving quantification and localization of molecular targets.« less

Contrast-Enhanced Ultrasound Angiogenesis Imaging by Mutual Information Analysis for Prostate Cancer Localization.

PubMed

Schalk, Stefan G; Demi, Libertario; Bouhouch, Nabil; Kuenen, Maarten P J; Postema, Arnoud W; de la Rosette, Jean J M C H; Wijkstra, Hessel; Tjalkens, Tjalling J; Mischi, Massimo

2017-03-01

The role of angiogenesis in cancer growth has stimulated research aimed at noninvasive cancer detection by blood perfusion imaging. Recently, contrast ultrasound dispersion imaging was proposed as an alternative method for angiogenesis imaging. After the intravenous injection of an ultrasound-contrast-agent bolus, dispersion can be indirectly estimated from the local similarity between neighboring time-intensity curves (TICs) measured by ultrasound imaging. Up until now, only linear similarity measures have been investigated. Motivated by the promising results of this approach in prostate cancer (PCa), we developed a novel dispersion estimation method based on mutual information, thus including nonlinear similarity, to further improve its ability to localize PCa. First, a simulation study was performed to establish the theoretical link between dispersion and mutual information. Next, the method's ability to localize PCa was validated in vivo in 23 patients (58 datasets) referred for radical prostatectomy by comparison with histology. A monotonic relationship between dispersion and mutual information was demonstrated. The in vivo study resulted in a receiver operating characteristic (ROC) curve area equal to 0.77, which was superior (p = 0.21-0.24) to that obtained by linear similarity measures (0.74-0.75) and (p <; 0.05) to that by conventional perfusion parameters (≤0.70). Mutual information between neighboring time-intensity curves can be used to indirectly estimate contrast dispersion and can lead to more accurate PCa localization. An improved PCa localization method can possibly lead to better grading and staging of tumors, and support focal-treatment guidance. Moreover, future employment of the method in other types of angiogenic cancer can be considered.

Automatic medical image annotation and keyword-based image retrieval using relevance feedback.

PubMed

Ko, Byoung Chul; Lee, JiHyeon; Nam, Jae-Yeal

2012-08-01

This paper presents novel multiple keywords annotation for medical images, keyword-based medical image retrieval, and relevance feedback method for image retrieval for enhancing image retrieval performance. For semantic keyword annotation, this study proposes a novel medical image classification method combining local wavelet-based center symmetric-local binary patterns with random forests. For keyword-based image retrieval, our retrieval system use the confidence score that is assigned to each annotated keyword by combining probabilities of random forests with predefined body relation graph. To overcome the limitation of keyword-based image retrieval, we combine our image retrieval system with relevance feedback mechanism based on visual feature and pattern classifier. Compared with other annotation and relevance feedback algorithms, the proposed method shows both improved annotation performance and accurate retrieval results.

Use of local noise power spectrum and wavelet analysis in quantitative image quality assurance for EPIDs

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

Lee, Soyoung

Purpose: To investigate the use of local noise power spectrum (NPS) to characterize image noise and wavelet analysis to isolate defective pixels and inter-subpanel flat-fielding artifacts for quantitative quality assurance (QA) of electronic portal imaging devices (EPIDs). Methods: A total of 93 image sets including custom-made bar-pattern images and open exposure images were collected from four iViewGT a-Si EPID systems over three years. Global quantitative metrics such as modulation transform function (MTF), NPS, and detective quantum efficiency (DQE) were computed for each image set. Local NPS was also calculated for individual subpanels by sampling region of interests within each subpanelmore » of the EPID. The 1D NPS, obtained by radially averaging the 2D NPS, was fitted to a power-law function. The r-square value of the linear regression analysis was used as a singular metric to characterize the noise properties of individual subpanels of the EPID. The sensitivity of the local NPS was first compared with the global quantitative metrics using historical image sets. It was then compared with two commonly used commercial QA systems with images collected after applying two different EPID calibration methods (single-level gain and multilevel gain). To detect isolated defective pixels and inter-subpanel flat-fielding artifacts, Haar wavelet transform was applied on the images. Results: Global quantitative metrics including MTF, NPS, and DQE showed little change over the period of data collection. On the contrary, a strong correlation between the local NPS (r-square values) and the variation of the EPID noise condition was observed. The local NPS analysis indicated image quality improvement with the r-square values increased from 0.80 ± 0.03 (before calibration) to 0.85 ± 0.03 (after single-level gain calibration) and to 0.96 ± 0.03 (after multilevel gain calibration), while the commercial QA systems failed to distinguish the image quality improvement between the two calibration methods. With wavelet analysis, defective pixels and inter-subpanel flat-fielding artifacts were clearly identified as spikes after thresholding the inversely transformed images. Conclusions: The proposed local NPS (r-square values) showed superior sensitivity to the noise level variations of individual subpanels compared with global quantitative metrics such as MTF, NPS, and DQE. Wavelet analysis was effective in detecting isolated defective pixels and inter-subpanel flat-fielding artifacts. The proposed methods are promising for the early detection of imaging artifacts of EPIDs.« less

Automated characterisation of ultrasound images of ovarian tumours: the diagnostic accuracy of a support vector machine and image processing with a local binary pattern operator

PubMed Central

Khazendar, S.; Sayasneh, A.; Al-Assam, H.; Du, H.; Kaijser, J.; Ferrara, L.; Timmerman, D.; Jassim, S.; Bourne, T.

2015-01-01

Introduction: Preoperative characterisation of ovarian masses into benign or malignant is of paramount importance to optimise patient management. Objectives: In this study, we developed and validated a computerised model to characterise ovarian masses as benign or malignant. Materials and methods: Transvaginal 2D B mode static ultrasound images of 187 ovarian masses with known histological diagnosis were included. Images were first pre-processed and enhanced, and Local Binary Pattern Histograms were then extracted from 2 × 2 blocks of each image. A Support Vector Machine (SVM) was trained using stratified cross validation with randomised sampling. The process was repeated 15 times and in each round 100 images were randomly selected. Results: The SVM classified the original non-treated static images as benign or malignant masses with an average accuracy of 0.62 (95% CI: 0.59-0.65). This performance significantly improved to an average accuracy of 0.77 (95% CI: 0.75-0.79) when images were pre-processed, enhanced and treated with a Local Binary Pattern operator (mean difference 0.15: 95% 0.11-0.19, p < 0.0001, two-tailed t test). Conclusion: We have shown that an SVM can classify static 2D B mode ultrasound images of ovarian masses into benign and malignant categories. The accuracy improves if texture related LBP features extracted from the images are considered. PMID:25897367

Improving iris recognition performance using segmentation, quality enhancement, match score fusion, and indexing.

PubMed

Vatsa, Mayank; Singh, Richa; Noore, Afzel

2008-08-01

This paper proposes algorithms for iris segmentation, quality enhancement, match score fusion, and indexing to improve both the accuracy and the speed of iris recognition. A curve evolution approach is proposed to effectively segment a nonideal iris image using the modified Mumford-Shah functional. Different enhancement algorithms are concurrently applied on the segmented iris image to produce multiple enhanced versions of the iris image. A support-vector-machine-based learning algorithm selects locally enhanced regions from each globally enhanced image and combines these good-quality regions to create a single high-quality iris image. Two distinct features are extracted from the high-quality iris image. The global textural feature is extracted using the 1-D log polar Gabor transform, and the local topological feature is extracted using Euler numbers. An intelligent fusion algorithm combines the textural and topological matching scores to further improve the iris recognition performance and reduce the false rejection rate, whereas an indexing algorithm enables fast and accurate iris identification. The verification and identification performance of the proposed algorithms is validated and compared with other algorithms using the CASIA Version 3, ICE 2005, and UBIRIS iris databases.

Small Imaging Depth LIDAR and DCNN-Based Localization for Automated Guided Vehicle †

PubMed Central

Ito, Seigo; Hiratsuka, Shigeyoshi; Ohta, Mitsuhiko; Matsubara, Hiroyuki; Ogawa, Masaru

2018-01-01

We present our third prototype sensor and a localization method for Automated Guided Vehicles (AGVs), for which small imaging LIght Detection and Ranging (LIDAR) and fusion-based localization are fundamentally important. Our small imaging LIDAR, named the Single-Photon Avalanche Diode (SPAD) LIDAR, uses a time-of-flight method and SPAD arrays. A SPAD is a highly sensitive photodetector capable of detecting at the single-photon level, and the SPAD LIDAR has two SPAD arrays on the same chip for detection of laser light and environmental light. Therefore, the SPAD LIDAR simultaneously outputs range image data and monocular image data with the same coordinate system and does not require external calibration among outputs. As AGVs travel both indoors and outdoors with vibration, this calibration-less structure is particularly useful for AGV applications. We also introduce a fusion-based localization method, named SPAD DCNN, which uses the SPAD LIDAR and employs a Deep Convolutional Neural Network (DCNN). SPAD DCNN can fuse the outputs of the SPAD LIDAR: range image data, monocular image data and peak intensity image data. The SPAD DCNN has two outputs: the regression result of the position of the SPAD LIDAR and the classification result of the existence of a target to be approached. Our third prototype sensor and the localization method are evaluated in an indoor environment by assuming various AGV trajectories. The results show that the sensor and localization method improve the localization accuracy. PMID:29320434

Small Imaging Depth LIDAR and DCNN-Based Localization for Automated Guided Vehicle.

PubMed

Ito, Seigo; Hiratsuka, Shigeyoshi; Ohta, Mitsuhiko; Matsubara, Hiroyuki; Ogawa, Masaru

2018-01-10

We present our third prototype sensor and a localization method for Automated Guided Vehicles (AGVs), for which small imaging LIght Detection and Ranging (LIDAR) and fusion-based localization are fundamentally important. Our small imaging LIDAR, named the Single-Photon Avalanche Diode (SPAD) LIDAR, uses a time-of-flight method and SPAD arrays. A SPAD is a highly sensitive photodetector capable of detecting at the single-photon level, and the SPAD LIDAR has two SPAD arrays on the same chip for detection of laser light and environmental light. Therefore, the SPAD LIDAR simultaneously outputs range image data and monocular image data with the same coordinate system and does not require external calibration among outputs. As AGVs travel both indoors and outdoors with vibration, this calibration-less structure is particularly useful for AGV applications. We also introduce a fusion-based localization method, named SPAD DCNN, which uses the SPAD LIDAR and employs a Deep Convolutional Neural Network (DCNN). SPAD DCNN can fuse the outputs of the SPAD LIDAR: range image data, monocular image data and peak intensity image data. The SPAD DCNN has two outputs: the regression result of the position of the SPAD LIDAR and the classification result of the existence of a target to be approached. Our third prototype sensor and the localization method are evaluated in an indoor environment by assuming various AGV trajectories. The results show that the sensor and localization method improve the localization accuracy.

Development of horn antenna mixer array with internal local oscillator module for microwave imaging diagnostics

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

Kuwahara, D., E-mail: dkuwahar@cc.tuat.ac.jp; Ito, N.; Nagayama, Y.

A new antenna array is proposed in order to improve the sensitivity and complexity of microwave imaging diagnostics systems such as a microwave imaging reflectometry, a microwave imaging interferometer, and an electron cyclotron emission imaging. The antenna array consists of five elements: a horn antenna, a waveguide-to-microstrip line transition, a mixer, a local oscillation (LO) module, and an intermediate frequency amplifier. By using an LO module, the LO optics can be removed, and the supplied LO power to each element can be equalized. We report details of the antenna array and characteristics of a prototype antenna array.

Development of horn antenna mixer array with internal local oscillator module for microwave imaging diagnostics.

PubMed

Kuwahara, D; Ito, N; Nagayama, Y; Yoshinaga, T; Yamaguchi, S; Yoshikawa, M; Kohagura, J; Sugito, S; Kogi, Y; Mase, A

2014-11-01

A new antenna array is proposed in order to improve the sensitivity and complexity of microwave imaging diagnostics systems such as a microwave imaging reflectometry, a microwave imaging interferometer, and an electron cyclotron emission imaging. The antenna array consists of five elements: a horn antenna, a waveguide-to-microstrip line transition, a mixer, a local oscillation (LO) module, and an intermediate frequency amplifier. By using an LO module, the LO optics can be removed, and the supplied LO power to each element can be equalized. We report details of the antenna array and characteristics of a prototype antenna array.

Anisotropic-Scale Junction Detection and Matching for Indoor Images.

PubMed

Xue, Nan; Xia, Gui-Song; Bai, Xiang; Zhang, Liangpei; Shen, Weiming

Junctions play an important role in characterizing local geometrical structures of images, and the detection of which is a longstanding but challenging task. Existing junction detectors usually focus on identifying the location and orientations of junction branches while ignoring their scales, which, however, contain rich geometries of images. This paper presents a novel approach for junction detection and characterization, which especially exploits the locally anisotropic geometries of a junction and estimates its scales by relying on an a-contrario model. The output junctions are with anisotropic scales, saying that a scale parameter is associated with each branch of a junction and are thus named as anisotropic-scale junctions (ASJs). We then apply the new detected ASJs for matching indoor images, where there are dramatic changes of viewpoints and the detected local visual features, e.g., key-points, are usually insufficient and lack distinctive ability. We propose to use the anisotropic geometries of our junctions to improve the matching precision of indoor images. The matching results on sets of indoor images demonstrate that our approach achieves the state-of-the-art performance on indoor image matching.Junctions play an important role in characterizing local geometrical structures of images, and the detection of which is a longstanding but challenging task. Existing junction detectors usually focus on identifying the location and orientations of junction branches while ignoring their scales, which, however, contain rich geometries of images. This paper presents a novel approach for junction detection and characterization, which especially exploits the locally anisotropic geometries of a junction and estimates its scales by relying on an a-contrario model. The output junctions are with anisotropic scales, saying that a scale parameter is associated with each branch of a junction and are thus named as anisotropic-scale junctions (ASJs). We then apply the new detected ASJs for matching indoor images, where there are dramatic changes of viewpoints and the detected local visual features, e.g., key-points, are usually insufficient and lack distinctive ability. We propose to use the anisotropic geometries of our junctions to improve the matching precision of indoor images. The matching results on sets of indoor images demonstrate that our approach achieves the state-of-the-art performance on indoor image matching.

Global and Local Features Based Classification for Bleed-Through Removal

NASA Astrophysics Data System (ADS)

Hu, Xiangyu; Lin, Hui; Li, Shutao; Sun, Bin

2016-12-01

The text on one side of historical documents often seeps through and appears on the other side, so the bleed-through is a common problem in historical document images. It makes the document images hard to read and the text difficult to recognize. To improve the image quality and readability, the bleed-through has to be removed. This paper proposes a global and local features extraction based bleed-through removal method. The Gaussian mixture model is used to get the global features of the images. Local features are extracted by the patch around each pixel. Then, the extreme learning machine classifier is utilized to classify the scanned images into the foreground text and the bleed-through component. Experimental results on real document image datasets show that the proposed method outperforms the state-of-the-art bleed-through removal methods and preserves the text strokes well.

High Dynamic Range Imaging Using Multiple Exposures

NASA Astrophysics Data System (ADS)

Hou, Xinglin; Luo, Haibo; Zhou, Peipei; Zhou, Wei

2017-06-01

It is challenging to capture a high-dynamic range (HDR) scene using a low-dynamic range (LDR) camera. This paper presents an approach for improving the dynamic range of cameras by using multiple exposure images of same scene taken under different exposure times. First, the camera response function (CRF) is recovered by solving a high-order polynomial in which only the ratios of the exposures are used. Then, the HDR radiance image is reconstructed by weighted summation of the each radiance maps. After that, a novel local tone mapping (TM) operator is proposed for the display of the HDR radiance image. By solving the high-order polynomial, the CRF can be recovered quickly and easily. Taken the local image feature and characteristic of histogram statics into consideration, the proposed TM operator could preserve the local details efficiently. Experimental result demonstrates the effectiveness of our method. By comparison, the method outperforms other methods in terms of imaging quality.

Clinical Study of Orthogonal-View Phase-Matched Digital Tomosynthesis for Lung Tumor Localization.

PubMed

Zhang, You; Ren, Lei; Vergalasova, Irina; Yin, Fang-Fang

2017-01-01

Compared to cone-beam computed tomography, digital tomosynthesis imaging has the benefits of shorter scanning time, less imaging dose, and better mechanical clearance for tumor localization in radiation therapy. However, for lung tumors, the localization accuracy of the conventional digital tomosynthesis technique is affected by the lack of depth information and the existence of lung tumor motion. This study investigates the clinical feasibility of using an orthogonal-view phase-matched digital tomosynthesis technique to improve the accuracy of lung tumor localization. The proposed orthogonal-view phase-matched digital tomosynthesis technique benefits from 2 major features: (1) it acquires orthogonal-view projections to improve the depth information in reconstructed digital tomosynthesis images and (2) it applies respiratory phase-matching to incorporate patient motion information into the synthesized reference digital tomosynthesis sets, which helps to improve the localization accuracy of moving lung tumors. A retrospective study enrolling 14 patients was performed to evaluate the accuracy of the orthogonal-view phase-matched digital tomosynthesis technique. Phantom studies were also performed using an anthropomorphic phantom to investigate the feasibility of using intratreatment aggregated kV and beams' eye view cine MV projections for orthogonal-view phase-matched digital tomosynthesis imaging. The localization accuracy of the orthogonal-view phase-matched digital tomosynthesis technique was compared to that of the single-view digital tomosynthesis techniques and the digital tomosynthesis techniques without phase-matching. The orthogonal-view phase-matched digital tomosynthesis technique outperforms the other digital tomosynthesis techniques in tumor localization accuracy for both the patient study and the phantom study. For the patient study, the orthogonal-view phase-matched digital tomosynthesis technique localizes the tumor to an average (± standard deviation) error of 1.8 (0.7) mm for a 30° total scan angle. For the phantom study using aggregated kV-MV projections, the orthogonal-view phase-matched digital tomosynthesis localizes the tumor to an average error within 1 mm for varying magnitudes of scan angles. The pilot clinical study shows that the orthogonal-view phase-matched digital tomosynthesis technique enables fast and accurate localization of moving lung tumors.

Local Subspace Classifier with Transform-Invariance for Image Classification

NASA Astrophysics Data System (ADS)

Hotta, Seiji

A family of linear subspace classifiers called local subspace classifier (LSC) outperforms the k-nearest neighbor rule (kNN) and conventional subspace classifiers in handwritten digit classification. However, LSC suffers very high sensitivity to image transformations because it uses projection and the Euclidean distances for classification. In this paper, I present a combination of a local subspace classifier (LSC) and a tangent distance (TD) for improving accuracy of handwritten digit recognition. In this classification rule, we can deal with transform-invariance easily because we are able to use tangent vectors for approximation of transformations. However, we cannot use tangent vectors in other type of images such as color images. Hence, kernel LSC (KLSC) is proposed for incorporating transform-invariance into LSC via kernel mapping. The performance of the proposed methods is verified with the experiments on handwritten digit and color image classification.

Optimizing Imaging Conditions for Demanding Multi-Color Super Resolution Localization Microscopy

PubMed Central

Nahidiazar, Leila; Agronskaia, Alexandra V.; Broertjes, Jorrit; van den Broek, Bram; Jalink, Kees

2016-01-01

Single Molecule Localization super-resolution Microscopy (SMLM) has become a powerful tool to study cellular architecture at the nanometer scale. In SMLM, single fluorophore labels are made to repeatedly switch on and off (“blink”), and their exact locations are determined by mathematically finding the centers of individual blinks. The image quality obtainable by SMLM critically depends on efficacy of blinking (brightness, fraction of molecules in the on-state) and on preparation longevity and labeling density. Recent work has identified several combinations of bright dyes and imaging buffers that work well together. Unfortunately, different dyes blink optimally in different imaging buffers, and acquisition of good quality 2- and 3-color images has therefore remained challenging. In this study we describe a new imaging buffer, OxEA, that supports 3-color imaging of the popular Alexa dyes. We also describe incremental improvements in preparation technique that significantly decrease lateral- and axial drift, as well as increase preparation longevity. We show that these improvements allow us to collect very large series of images from the same cell, enabling image stitching, extended 3D imaging as well as multi-color recording. PMID:27391487

Remote sensing image segmentation using local sparse structure constrained latent low rank representation

NASA Astrophysics Data System (ADS)

Tian, Shu; Zhang, Ye; Yan, Yimin; Su, Nan; Zhang, Junping

2016-09-01

Latent low-rank representation (LatLRR) has been attached considerable attention in the field of remote sensing image segmentation, due to its effectiveness in exploring the multiple subspace structures of data. However, the increasingly heterogeneous texture information in the high spatial resolution remote sensing images, leads to more severe interference of pixels in local neighborhood, and the LatLRR fails to capture the local complex structure information. Therefore, we present a local sparse structure constrainted latent low-rank representation (LSSLatLRR) segmentation method, which explicitly imposes the local sparse structure constraint on LatLRR to capture the intrinsic local structure in manifold structure feature subspaces. The whole segmentation framework can be viewed as two stages in cascade. In the first stage, we use the local histogram transform to extract the texture local histogram features (LHOG) at each pixel, which can efficiently capture the complex and micro-texture pattern. In the second stage, a local sparse structure (LSS) formulation is established on LHOG, which aims to preserve the local intrinsic structure and enhance the relationship between pixels having similar local characteristics. Meanwhile, by integrating the LSS and the LatLRR, we can efficiently capture the local sparse and low-rank structure in the mixture of feature subspace, and we adopt the subspace segmentation method to improve the segmentation accuracy. Experimental results on the remote sensing images with different spatial resolution show that, compared with three state-of-the-art image segmentation methods, the proposed method achieves more accurate segmentation results.

Efficient local representations for three-dimensional palmprint recognition

NASA Astrophysics Data System (ADS)

Yang, Bing; Wang, Xiaohua; Yao, Jinliang; Yang, Xin; Zhu, Wenhua

2013-10-01

Palmprints have been broadly used for personal authentication because they are highly accurate and incur low cost. Most previous works have focused on two-dimensional (2-D) palmprint recognition in the past decade. Unfortunately, 2-D palmprint recognition systems lose the shape information when capturing palmprint images. Moreover, such 2-D palmprint images can be easily forged or affected by noise. Hence, three-dimensional (3-D) palmprint recognition has been regarded as a promising way to further improve the performance of palmprint recognition systems. We have developed a simple, but efficient method for 3-D palmprint recognition by using local features. We first utilize shape index representation to describe the geometry of local regions in 3-D palmprint data. Then, we extract local binary pattern and Gabor wavelet features from the shape index image. The two types of complementary features are finally fused at a score level for further improvements. The experimental results on the Hong Kong Polytechnic 3-D palmprint database, which contains 8000 samples from 400 palms, illustrate the effectiveness of the proposed method.

Improved localization of cellular membrane receptors using combined fluorescence microscopy and simultaneous topography and recognition imaging

NASA Astrophysics Data System (ADS)

Duman, M.; Pfleger, M.; Zhu, R.; Rankl, C.; Chtcheglova, L. A.; Neundlinger, I.; Bozna, B. L.; Mayer, B.; Salio, M.; Shepherd, D.; Polzella, P.; Moertelmaier, M.; Kada, G.; Ebner, A.; Dieudonne, M.; Schütz, G. J.; Cerundolo, V.; Kienberger, F.; Hinterdorfer, P.

2010-03-01

The combination of fluorescence microscopy and atomic force microscopy has a great potential in single-molecule-detection applications, overcoming many of the limitations coming from each individual technique. Here we present a new platform of combined fluorescence and simultaneous topography and recognition imaging (TREC) for improved localization of cellular receptors. Green fluorescent protein (GFP) labeled human sodium-glucose cotransporter (hSGLT1) expressed Chinese Hamster Ovary (CHO) cells and endothelial cells (MyEnd) from mouse myocardium stained with phalloidin-rhodamine were used as cell systems to study AFM topography and fluorescence microscopy on the same surface area. Topographical AFM images revealed membrane features such as lamellipodia, cytoskeleton fibers, F-actin filaments and small globular structures with heights ranging from 20 to 30 nm. Combined fluorescence and TREC imaging was applied to detect density, distribution and localization of YFP-labeled CD1d molecules on α-galactosylceramide (αGalCer)-loaded THP1 cells. While the expression level, distribution and localization of CD1d molecules on THP1 cells were detected with fluorescence microscopy, the nanoscale distribution of binding sites was investigated with molecular recognition imaging by using a chemically modified AFM tip. Using TREC on the inverted light microscope, the recognition sites of cell receptors were detected in recognition images with domain sizes ranging from ~ 25 to ~ 160 nm, with the smaller domains corresponding to a single CD1d molecule.

Improved localization of cellular membrane receptors using combined fluorescence microscopy and simultaneous topography and recognition imaging.

PubMed

Duman, M; Pfleger, M; Zhu, R; Rankl, C; Chtcheglova, L A; Neundlinger, I; Bozna, B L; Mayer, B; Salio, M; Shepherd, D; Polzella, P; Moertelmaier, M; Kada, G; Ebner, A; Dieudonne, M; Schütz, G J; Cerundolo, V; Kienberger, F; Hinterdorfer, P

2010-03-19

The combination of fluorescence microscopy and atomic force microscopy has a great potential in single-molecule-detection applications, overcoming many of the limitations coming from each individual technique. Here we present a new platform of combined fluorescence and simultaneous topography and recognition imaging (TREC) for improved localization of cellular receptors. Green fluorescent protein (GFP) labeled human sodium-glucose cotransporter (hSGLT1) expressed Chinese Hamster Ovary (CHO) cells and endothelial cells (MyEnd) from mouse myocardium stained with phalloidin-rhodamine were used as cell systems to study AFM topography and fluorescence microscopy on the same surface area. Topographical AFM images revealed membrane features such as lamellipodia, cytoskeleton fibers, F-actin filaments and small globular structures with heights ranging from 20 to 30 nm. Combined fluorescence and TREC imaging was applied to detect density, distribution and localization of YFP-labeled CD1d molecules on alpha-galactosylceramide (alphaGalCer)-loaded THP1 cells. While the expression level, distribution and localization of CD1d molecules on THP1 cells were detected with fluorescence microscopy, the nanoscale distribution of binding sites was investigated with molecular recognition imaging by using a chemically modified AFM tip. Using TREC on the inverted light microscope, the recognition sites of cell receptors were detected in recognition images with domain sizes ranging from approximately 25 to approximately 160 nm, with the smaller domains corresponding to a single CD1d molecule.

Geometry-aware multiscale image registration via OBBTree-based polyaffine log-demons.

PubMed

Seiler, Christof; Pennec, Xavier; Reyes, Mauricio

2011-01-01

Non-linear image registration is an important tool in many areas of image analysis. For instance, in morphometric studies of a population of brains, free-form deformations between images are analyzed to describe the structural anatomical variability. Such a simple deformation model is justified by the absence of an easy expressible prior about the shape changes. Applying the same algorithms used in brain imaging to orthopedic images might not be optimal due to the difference in the underlying prior on the inter-subject deformations. In particular, using an un-informed deformation prior often leads to local minima far from the expected solution. To improve robustness and promote anatomically meaningful deformations, we propose a locally affine and geometry-aware registration algorithm that automatically adapts to the data. We build upon the log-domain demons algorithm and introduce a new type of OBBTree-based regularization in the registration with a natural multiscale structure. The regularization model is composed of a hierarchy of locally affine transformations via their logarithms. Experiments on mandibles show improved accuracy and robustness when used to initialize the demons, and even similar performance by direct comparison to the demons, with a significantly lower degree of freedom. This closes the gap between polyaffine and non-rigid registration and opens new ways to statistically analyze the registration results.

Automated metastatic brain lesion detection: a computer aided diagnostic and clinical research tool

NASA Astrophysics Data System (ADS)

Devine, Jeremy; Sahgal, Arjun; Karam, Irene; Martel, Anne L.

2016-03-01

The accurate localization of brain metastases in magnetic resonance (MR) images is crucial for patients undergoing stereotactic radiosurgery (SRS) to ensure that all neoplastic foci are targeted. Computer automated tumor localization and analysis can improve both of these tasks by eliminating inter and intra-observer variations during the MR image reading process. Lesion localization is accomplished using adaptive thresholding to extract enhancing objects. Each enhancing object is represented as a vector of features which includes information on object size, symmetry, position, shape, and context. These vectors are then used to train a random forest classifier. We trained and tested the image analysis pipeline on 3D axial contrast-enhanced MR images with the intention of localizing the brain metastases. In our cross validation study and at the most effective algorithm operating point, we were able to identify 90% of the lesions at a precision rate of 60%.

Particle swarm optimization-based local entropy weighted histogram equalization for infrared image enhancement

NASA Astrophysics Data System (ADS)

Wan, Minjie; Gu, Guohua; Qian, Weixian; Ren, Kan; Chen, Qian; Maldague, Xavier

2018-06-01

Infrared image enhancement plays a significant role in intelligent urban surveillance systems for smart city applications. Unlike existing methods only exaggerating the global contrast, we propose a particle swam optimization-based local entropy weighted histogram equalization which involves the enhancement of both local details and fore-and background contrast. First of all, a novel local entropy weighted histogram depicting the distribution of detail information is calculated based on a modified hyperbolic tangent function. Then, the histogram is divided into two parts via a threshold maximizing the inter-class variance in order to improve the contrasts of foreground and background, respectively. To avoid over-enhancement and noise amplification, double plateau thresholds of the presented histogram are formulated by means of particle swarm optimization algorithm. Lastly, each sub-image is equalized independently according to the constrained sub-local entropy weighted histogram. Comparative experiments implemented on real infrared images prove that our algorithm outperforms other state-of-the-art methods in terms of both visual and quantized evaluations.

Robust Dehaze Algorithm for Degraded Image of CMOS Image Sensors.

PubMed

Qu, Chen; Bi, Du-Yan; Sui, Ping; Chao, Ai-Nong; Wang, Yun-Fei

2017-09-22

The CMOS (Complementary Metal-Oxide-Semiconductor) is a new type of solid image sensor device widely used in object tracking, object recognition, intelligent navigation fields, and so on. However, images captured by outdoor CMOS sensor devices are usually affected by suspended atmospheric particles (such as haze), causing a reduction in image contrast, color distortion problems, and so on. In view of this, we propose a novel dehazing approach based on a local consistent Markov random field (MRF) framework. The neighboring clique in traditional MRF is extended to the non-neighboring clique, which is defined on local consistent blocks based on two clues, where both the atmospheric light and transmission map satisfy the character of local consistency. In this framework, our model can strengthen the restriction of the whole image while incorporating more sophisticated statistical priors, resulting in more expressive power of modeling, thus, solving inadequate detail recovery effectively and alleviating color distortion. Moreover, the local consistent MRF framework can obtain details while maintaining better results for dehazing, which effectively improves the image quality captured by the CMOS image sensor. Experimental results verified that the method proposed has the combined advantages of detail recovery and color preservation.

Sub-diffraction Limit Localization of Proteins in Volumetric Space Using Bayesian Restoration of Fluorescence Images from Ultrathin Specimens

PubMed Central

Wang, Gordon; Smith, Stephen J.

2012-01-01

Photon diffraction limits the resolution of conventional light microscopy at the lateral focal plane to 0.61λ/NA (λ = wavelength of light, NA = numerical aperture of the objective) and at the axial plane to 1.4nλ/NA2 (n = refractive index of the imaging medium, 1.51 for oil immersion), which with visible wavelengths and a 1.4NA oil immersion objective is ∼220 nm and ∼600 nm in the lateral plane and axial plane respectively. This volumetric resolution is too large for the proper localization of protein clustering in subcellular structures. Here we combine the newly developed proteomic imaging technique, Array Tomography (AT), with its native 50–100 nm axial resolution achieved by physical sectioning of resin embedded tissue, and a 2D maximum likelihood deconvolution method, based on Bayes' rule, which significantly improves the resolution of protein puncta in the lateral plane to allow accurate and fast computational segmentation and analysis of labeled proteins. The physical sectioning of AT allows tissue specimens to be imaged at the physical optimum of modern high NA plan-apochormatic objectives. This translates to images that have little out of focus light, minimal aberrations and wave-front distortions. Thus, AT is able to provide images with truly invariant point spread functions (PSF), a property critical for accurate deconvolution. We show that AT with deconvolution increases the volumetric analytical fidelity of protein localization by significantly improving the modulation of high spatial frequencies up to and potentially beyond the spatial frequency cut-off of the objective. Moreover, we are able to achieve this improvement with no noticeable introduction of noise or artifacts and arrive at object segmentation and localization accuracies on par with image volumes captured using commercial implementations of super-resolution microscopes. PMID:22956902

Sub-diffraction limit localization of proteins in volumetric space using Bayesian restoration of fluorescence images from ultrathin specimens.

PubMed

Wang, Gordon; Smith, Stephen J

2012-01-01

Photon diffraction limits the resolution of conventional light microscopy at the lateral focal plane to 0.61λ/NA (λ = wavelength of light, NA = numerical aperture of the objective) and at the axial plane to 1.4nλ/NA(2) (n = refractive index of the imaging medium, 1.51 for oil immersion), which with visible wavelengths and a 1.4NA oil immersion objective is -220 nm and -600 nm in the lateral plane and axial plane respectively. This volumetric resolution is too large for the proper localization of protein clustering in subcellular structures. Here we combine the newly developed proteomic imaging technique, Array Tomography (AT), with its native 50-100 nm axial resolution achieved by physical sectioning of resin embedded tissue, and a 2D maximum likelihood deconvolution method, based on Bayes' rule, which significantly improves the resolution of protein puncta in the lateral plane to allow accurate and fast computational segmentation and analysis of labeled proteins. The physical sectioning of AT allows tissue specimens to be imaged at the physical optimum of modern high NA plan-apochormatic objectives. This translates to images that have little out of focus light, minimal aberrations and wave-front distortions. Thus, AT is able to provide images with truly invariant point spread functions (PSF), a property critical for accurate deconvolution. We show that AT with deconvolution increases the volumetric analytical fidelity of protein localization by significantly improving the modulation of high spatial frequencies up to and potentially beyond the spatial frequency cut-off of the objective. Moreover, we are able to achieve this improvement with no noticeable introduction of noise or artifacts and arrive at object segmentation and localization accuracies on par with image volumes captured using commercial implementations of super-resolution microscopes.

Multispectral image sharpening using wavelet transform techniques and spatial correlation of edges

USGS Publications Warehouse

Lemeshewsky, George P.; Schowengerdt, Robert A.

2000-01-01

Several reported image fusion or sharpening techniques are based on the discrete wavelet transform (DWT). The technique described here uses a pixel-based maximum selection rule to combine respective transform coefficients of lower spatial resolution near-infrared (NIR) and higher spatial resolution panchromatic (pan) imagery to produce a sharpened NIR image. Sharpening assumes a radiometric correlation between the spectral band images. However, there can be poor correlation, including edge contrast reversals (e.g., at soil-vegetation boundaries), between the fused images and, consequently, degraded performance. To improve sharpening, a local area-based correlation technique originally reported for edge comparison with image pyramid fusion is modified for application with the DWT process. Further improvements are obtained by using redundant, shift-invariant implementation of the DWT. Example images demonstrate the improvements in NIR image sharpening with higher resolution pan imagery.

Evaluation of intensified image enhancement through conspicuity and triangle orientation discrimination measures

NASA Astrophysics Data System (ADS)

Dijk, Judith; van Eekeren, Adam W. M.; Toet, Alexander; den Hollander, Richard J. M.; Schutte, Klamer; van Heijningen, Ad W. P.; Bijl, Piet

2013-04-01

For many military operations, situational awareness is of great importance. During night conditions, this situational awareness can be improved using both analog and digital image-intensified cameras. The quality of image intensifiers is a topic of interest. One of the differences between a digital and analog system is noise behavior. For digital image intensifiers, the noise behavior is not as good as for analog image intensifiers, but it can be improved using noise-reduction techniques. In this paper, the improvement using temporal noise reduction and local adaptive contrast enhancement is shown and quantitatively evaluated by subjective measurement of the conspicuity and triangle orientation discrimination (TOD). The results of the conspicuity and TOD experiments are consistent with each other. The highest improvement is found for a low-clutter environment; for medium- and high-clutter environments, the improvement is less. This can be explained by the fact that image enhancement increases contrast of all image details, irrespective of whether they are targets or clutter. For low-clutter image enhancement, target conspicuity and target detection improvement will be largest, since there are not many distracting elements.

Infrared image background modeling based on improved Susan filtering

NASA Astrophysics Data System (ADS)

Yuehua, Xia

2018-02-01

When SUSAN filter is used to model the infrared image, the Gaussian filter lacks the ability of direction filtering. After filtering, the edge information of the image cannot be preserved well, so that there are a lot of edge singular points in the difference graph, increase the difficulties of target detection. To solve the above problems, the anisotropy algorithm is introduced in this paper, and the anisotropic Gauss filter is used instead of the Gauss filter in the SUSAN filter operator. Firstly, using anisotropic gradient operator to calculate a point of image's horizontal and vertical gradient, to determine the long axis direction of the filter; Secondly, use the local area of the point and the neighborhood smoothness to calculate the filter length and short axis variance; And then calculate the first-order norm of the difference between the local area of the point's gray-scale and mean, to determine the threshold of the SUSAN filter; Finally, the built SUSAN filter is used to convolution the image to obtain the background image, at the same time, the difference between the background image and the original image is obtained. The experimental results show that the background modeling effect of infrared image is evaluated by Mean Squared Error (MSE), Structural Similarity (SSIM) and local Signal-to-noise Ratio Gain (GSNR). Compared with the traditional filtering algorithm, the improved SUSAN filter has achieved better background modeling effect, which can effectively preserve the edge information in the image, and the dim small target is effectively enhanced in the difference graph, which greatly reduces the false alarm rate of the image.

MRI-Guided Focused Ultrasound as a New Method of Drug Delivery

PubMed Central

Thanou, M.; Gedroyc, W.

2013-01-01

Ultrasound-mediated drug delivery under the guidance of an imaging modality can improve drug disposition and achieve site-specific drug delivery. The term focal drug delivery has been introduced to describe the focal targeting of drugs in tissues with the help of imaging and focused ultrasound. Focal drug delivery aims to improve the therapeutic profile of drugs by improving their specificity and their permeation in defined areas. Focused-ultrasound- (FUS-) mediated drug delivery has been applied with various molecules to improve their local distribution in tissues. FUS is applied with the aid of microbubbles to enhance the permeability of bioactive molecules across BBB and improve drug distribution in the brain. Recently, FUS has been utilised in combination with MRI-labelled liposomes that respond to temperature increase. This strategy aims to “activate” nanoparticles to release their cargo locally when triggered by hyperthermia induced by FUS. MRI-guided FUS drug delivery provides the opportunity to improve drug bioavailability locally and therefore improve the therapeutic profiles of drugs. This drug delivery strategy can be directly translated to clinic as MRg FUS is a promising clinically therapeutic approach. However, more basic research is required to understand the physiological mechanism of FUS-enhanced drug delivery. PMID:23738076

Highly undersampled MR image reconstruction using an improved dual-dictionary learning method with self-adaptive dictionaries.

PubMed

Li, Jiansen; Song, Ying; Zhu, Zhen; Zhao, Jun

2017-05-01

Dual-dictionary learning (Dual-DL) method utilizes both a low-resolution dictionary and a high-resolution dictionary, which are co-trained for sparse coding and image updating, respectively. It can effectively exploit a priori knowledge regarding the typical structures, specific features, and local details of training sets images. The prior knowledge helps to improve the reconstruction quality greatly. This method has been successfully applied in magnetic resonance (MR) image reconstruction. However, it relies heavily on the training sets, and dictionaries are fixed and nonadaptive. In this research, we improve Dual-DL by using self-adaptive dictionaries. The low- and high-resolution dictionaries are updated correspondingly along with the image updating stage to ensure their self-adaptivity. The updated dictionaries incorporate both the prior information of the training sets and the test image directly. Both dictionaries feature improved adaptability. Experimental results demonstrate that the proposed method can efficiently and significantly improve the quality and robustness of MR image reconstruction.

Tensor scale-based fuzzy connectedness image segmentation

NASA Astrophysics Data System (ADS)

Saha, Punam K.; Udupa, Jayaram K.

2003-05-01

Tangible solutions to image segmentation are vital in many medical imaging applications. Toward this goal, a framework based on fuzzy connectedness was developed in our laboratory. A fundamental notion called "affinity" - a local fuzzy hanging togetherness relation on voxels - determines the effectiveness of this segmentation framework in real applications. In this paper, we introduce the notion of "tensor scale" - a recently developed local morphometric parameter - in affinity definition and study its effectiveness. Although, our previous notion of "local scale" using the spherical model successfully incorporated local structure size into affinity and resulted in measureable improvements in segmentation results, a major limitation of the previous approach was that it ignored local structural orientation and anisotropy. The current approach of using tensor scale in affinity computation allows an effective utilization of local size, orientation, and ansiotropy in a unified manner. Tensor scale is used for computing both the homogeneity- and object-feature-based components of affinity. Preliminary results of the proposed method on several medical images and computer generated phantoms of realistic shapes are presented. Further extensions of this work are discussed.

An ITK framework for deterministic global optimization for medical image registration

NASA Astrophysics Data System (ADS)

Dru, Florence; Wachowiak, Mark P.; Peters, Terry M.

2006-03-01

Similarity metric optimization is an essential step in intensity-based rigid and nonrigid medical image registration. For clinical applications, such as image guidance of minimally invasive procedures, registration accuracy and efficiency are prime considerations. In addition, clinical utility is enhanced when registration is integrated into image analysis and visualization frameworks, such as the popular Insight Toolkit (ITK). ITK is an open source software environment increasingly used to aid the development, testing, and integration of new imaging algorithms. In this paper, we present a new ITK-based implementation of the DIRECT (Dividing Rectangles) deterministic global optimization algorithm for medical image registration. Previously, it has been shown that DIRECT improves the capture range and accuracy for rigid registration. Our ITK class also contains enhancements over the original DIRECT algorithm by improving stopping criteria, adaptively adjusting a locality parameter, and by incorporating Powell's method for local refinement. 3D-3D registration experiments with ground-truth brain volumes and clinical cardiac volumes show that combining DIRECT with Powell's method improves registration accuracy over Powell's method used alone, is less sensitive to initial misorientation errors, and, with the new stopping criteria, facilitates adequate exploration of the search space without expending expensive iterations on non-improving function evaluations. Finally, in this framework, a new parallel implementation for computing mutual information is presented, resulting in near-linear speedup with two processors.

Region-Based Prediction for Image Compression in the Cloud.

PubMed

Begaint, Jean; Thoreau, Dominique; Guillotel, Philippe; Guillemot, Christine

2018-04-01

Thanks to the increasing number of images stored in the cloud, external image similarities can be leveraged to efficiently compress images by exploiting inter-images correlations. In this paper, we propose a novel image prediction scheme for cloud storage. Unlike current state-of-the-art methods, we use a semi-local approach to exploit inter-image correlation. The reference image is first segmented into multiple planar regions determined from matched local features and super-pixels. The geometric and photometric disparities between the matched regions of the reference image and the current image are then compensated. Finally, multiple references are generated from the estimated compensation models and organized in a pseudo-sequence to differentially encode the input image using classical video coding tools. Experimental results demonstrate that the proposed approach yields significant rate-distortion performance improvements compared with the current image inter-coding solutions such as high efficiency video coding.

Image guidance improves localization of sonographically occult colorectal liver metastases

NASA Astrophysics Data System (ADS)

Leung, Universe; Simpson, Amber L.; Adams, Lauryn B.; Jarnagin, William R.; Miga, Michael I.; Kingham, T. Peter

2015-03-01

Assessing the therapeutic benefit of surgical navigation systems is a challenging problem in image-guided surgery. The exact clinical indications for patients that may benefit from these systems is not always clear, particularly for abdominal surgery where image-guidance systems have failed to take hold in the same way as orthopedic and neurosurgical applications. We report interim analysis of a prospective clinical trial for localizing small colorectal liver metastases using the Explorer system (Path Finder Technologies, Nashville, TN). Colorectal liver metastases are small lesions that can be difficult to identify with conventional intraoperative ultrasound due to echogeneity changes in the liver as a result of chemotherapy and other preoperative treatments. Interim analysis with eighteen patients shows that 9 of 15 (60%) of these occult lesions could be detected with image guidance. Image guidance changed intraoperative management in 3 (17%) cases. These results suggest that image guidance is a promising tool for localization of small occult liver metastases and that the indications for image-guided surgery are expanding.

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

Prostate cancer localization with multispectral MRI using cost-sensitive support vector machines and conditional random fields.

PubMed

Artan, Yusuf; Haider, Masoom A; Langer, Deanna L; van der Kwast, Theodorus H; Evans, Andrew J; Yang, Yongyi; Wernick, Miles N; Trachtenberg, John; Yetik, Imam Samil

2010-09-01

Prostate cancer is a leading cause of cancer death for men in the United States. Fortunately, the survival rate for early diagnosed patients is relatively high. Therefore, in vivo imaging plays an important role for the detection and treatment of the disease. Accurate prostate cancer localization with noninvasive imaging can be used to guide biopsy, radiotherapy, and surgery as well as to monitor disease progression. Magnetic resonance imaging (MRI) performed with an endorectal coil provides higher prostate cancer localization accuracy, when compared to transrectal ultrasound (TRUS). However, in general, a single type of MRI is not sufficient for reliable tumor localization. As an alternative, multispectral MRI, i.e., the use of multiple MRI-derived datasets, has emerged as a promising noninvasive imaging technique for the localization of prostate cancer; however almost all studies are with human readers. There is a significant inter and intraobserver variability for human readers, and it is substantially difficult for humans to analyze the large dataset of multispectral MRI. To solve these problems, this study presents an automated localization method using cost-sensitive support vector machines (SVMs) and shows that this method results in improved localization accuracy than classical SVM. Additionally, we develop a new segmentation method by combining conditional random fields (CRF) with a cost-sensitive framework and show that our method further improves cost-sensitive SVM results by incorporating spatial information. We test SVM, cost-sensitive SVM, and the proposed cost-sensitive CRF on multispectral MRI datasets acquired from 21 biopsy-confirmed cancer patients. Our results show that multispectral MRI helps to increase the accuracy of prostate cancer localization when compared to single MR images; and that using advanced methods such as cost-sensitive SVM as well as the proposed cost-sensitive CRF can boost the performance significantly when compared to SVM.

Edge detection and localization with edge pattern analysis and inflection characterization

NASA Astrophysics Data System (ADS)

Jiang, Bo

2012-05-01

In general edges are considered to be abrupt changes or discontinuities in two dimensional image signal intensity distributions. The accuracy of front-end edge detection methods in image processing impacts the eventual success of higher level pattern analysis downstream. To generalize edge detectors designed from a simple ideal step function model to real distortions in natural images, research on one dimensional edge pattern analysis to improve the accuracy of edge detection and localization proposes an edge detection algorithm, which is composed by three basic edge patterns, such as ramp, impulse, and step. After mathematical analysis, general rules for edge representation based upon the classification of edge types into three categories-ramp, impulse, and step (RIS) are developed to reduce detection and localization errors, especially reducing "double edge" effect that is one important drawback to the derivative method. But, when applying one dimensional edge pattern in two dimensional image processing, a new issue is naturally raised that the edge detector should correct marking inflections or junctions of edges. Research on human visual perception of objects and information theory pointed out that a pattern lexicon of "inflection micro-patterns" has larger information than a straight line. Also, research on scene perception gave an idea that contours have larger information are more important factor to determine the success of scene categorization. Therefore, inflections or junctions are extremely useful features, whose accurate description and reconstruction are significant in solving correspondence problems in computer vision. Therefore, aside from adoption of edge pattern analysis, inflection or junction characterization is also utilized to extend traditional derivative edge detection algorithm. Experiments were conducted to test my propositions about edge detection and localization accuracy improvements. The results support the idea that these edge detection method improvements are effective in enhancing the accuracy of edge detection and localization.

Resolving phase information of the optical local density of state with scattering near-field probes

NASA Astrophysics Data System (ADS)

Prasad, R.; Vincent, R.

2016-10-01

We theoretically discuss the link between the phase measured using a scattering optical scanning near-field microscopy (s-SNOM) and the local density of optical states (LDOS). A remarkable result is that the LDOS information is directly included in the phase of the probe. Therefore by monitoring the spatial variation of the trans-scattering phase, we locally measure the phase modulation associated with the probe and the optical paths. We demonstrate numerically that a technique involving two-phase imaging of a sample with two different sized tips should allow to obtain the image the pLDOS. For this imaging method, numerical comparison with extinction probe measurement shows crucial qualitative and quantitative improvement.

Radar Imaging of Spheres in 3D using MUSIC

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

Chambers, D H; Berryman, J G

2003-01-21

We have shown that multiple spheres can be imaged by linear and planar EM arrays using only one component of polarization. The imaging approach involves calculating the SVD of the scattering response matrix, selecting a subset of singular values that represents noise, and evaluating the MUSIC functional. The noise threshold applied to the spectrum of singular values for optimal performance is typically around 1%. The resulting signal subspace includes more than one singular value per sphere. The presence of reflections from the ground improves height localization, even for a linear array parallel to the ground. However, the interference between directmore » and reflected energy modulates the field, creating periodic nulls that can obscure targets in typical images. These nulls are largely eliminated by normalizing the MUSIC functional with the broadside beam pattern of the array. The resulting images show excellent localization for 1 and 2 spheres. The performance for the 3 sphere configurations are complicated by shadowing effects and the greater range of the 3rd sphere in case 2. Two of the three spheres are easily located by MUSIC but the third is difficult to distinguish from other local maxima of the complex imaging functional. Improvement is seen when the linear array is replace with a planar array, which increases the effective aperture height. Further analysis of the singular values and their relationship to modes of scattering from the spheres, as well as better ways to exploit polarization, should improve performance. Work along these lines is currently being pursued by the authors.« less

Task-based statistical image reconstruction for high-quality cone-beam CT

NASA Astrophysics Data System (ADS)

Dang, Hao; Webster Stayman, J.; Xu, Jennifer; Zbijewski, Wojciech; Sisniega, Alejandro; Mow, Michael; Wang, Xiaohui; Foos, David H.; Aygun, Nafi; Koliatsos, Vassilis E.; Siewerdsen, Jeffrey H.

2017-11-01

Task-based analysis of medical imaging performance underlies many ongoing efforts in the development of new imaging systems. In statistical image reconstruction, regularization is often formulated in terms to encourage smoothness and/or sharpness (e.g. a linear, quadratic, or Huber penalty) but without explicit formulation of the task. We propose an alternative regularization approach in which a spatially varying penalty is determined that maximizes task-based imaging performance at every location in a 3D image. We apply the method to model-based image reconstruction (MBIR—viz., penalized weighted least-squares, PWLS) in cone-beam CT (CBCT) of the head, focusing on the task of detecting a small, low-contrast intracranial hemorrhage (ICH), and we test the performance of the algorithm in the context of a recently developed CBCT prototype for point-of-care imaging of brain injury. Theoretical predictions of local spatial resolution and noise are computed via an optimization by which regularization (specifically, the quadratic penalty strength) is allowed to vary throughout the image to maximize local task-based detectability index ({{d}\\prime} ). Simulation studies and test-bench experiments were performed using an anthropomorphic head phantom. Three PWLS implementations were tested: conventional (constant) penalty; a certainty-based penalty derived to enforce constant point-spread function, PSF; and the task-based penalty derived to maximize local detectability at each location. Conventional (constant) regularization exhibited a fairly strong degree of spatial variation in {{d}\\prime} , and the certainty-based method achieved uniform PSF, but each exhibited a reduction in detectability compared to the task-based method, which improved detectability up to ~15%. The improvement was strongest in areas of high attenuation (skull base), where the conventional and certainty-based methods tended to over-smooth the data. The task-driven reconstruction method presents a promising regularization method in MBIR by explicitly incorporating task-based imaging performance as the objective. The results demonstrate improved ICH conspicuity and support the development of high-quality CBCT systems.

Percutaneous Image-Guided Screw Fixation of Bone Lesions in Cancer Patients: Double-Centre Analysis of Outcomes including Local Evolution of the Treated Focus

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

Cazzato, Roberto Luigi, E-mail: gigicazzato@hotmail.it; Koch, Guillaume, E-mail: guillaume.koch@chru-strasbourg.fr; Buy, Xavier, E-mail: x.buy@bordeaux.unicancer.fr

AimTo review outcomes and local evolution of treated lesions following percutaneous image-guided screw fixation (PIGSF) of pathological/insufficiency fractures (PF/InF) and impeding fractures (ImF) in cancer patients at two tertiary centres.Materials and methodsThirty-two consecutive patients (mean age 67.5 years; range 33–86 years) with a range of tumours and prognoses underwent PIGSF for non/minimally displaced PF/InF and ImF. Screws were placed under CT/fluoroscopy or cone-beam CT guidance, with or without cementoplasty. Clinical outcomes were assessed using a simple 4-point scale (1 = worse; 2 = stable; 3 = improved; 4 = significantly improved). Local evolution was reviewed on most recent follow-up imaging. Technical success, complications, and overall survival were evaluated.ResultsThirty-six lesions weremore » treated with 74 screws mainly in the pelvis and femoral neck (58.2 %); including 47.2 % PF, 13.9 % InF, and 38.9 % ImF. Cementoplasty was performed in 63.9 % of the cases. Technical success was 91.6 %. Hospital stay was ≤3 days; 87.1 % of lesions were improved at 1-month follow-up; three major complications (early screw-impingement radiculopathy; accelerated coxarthrosis; late coxofemoral septic arthritis) and one minor complication were observed. Unfavourable local evolution at imaging occurred in 3/24 lesions (12.5 %) at mean 8.7-month follow-up, including poor consolidation (one case) and screw loosening (two cases, at least 1 symptomatic). There were no cases of secondary fractures.ConclusionsPIGSF is feasible for a wide range of oncologic patients, offering good short-term efficacy, acceptable complication rates, and rapid recovery. Unfavourable local evolution at imaging may be relatively frequent, and requires close clinico-radiological surveillance.« less

An improved feature extraction algorithm based on KAZE for multi-spectral image

NASA Astrophysics Data System (ADS)

Yang, Jianping; Li, Jun

2018-02-01

Multi-spectral image contains abundant spectral information, which is widely used in all fields like resource exploration, meteorological observation and modern military. Image preprocessing, such as image feature extraction and matching, is indispensable while dealing with multi-spectral remote sensing image. Although the feature matching algorithm based on linear scale such as SIFT and SURF performs strong on robustness, the local accuracy cannot be guaranteed. Therefore, this paper proposes an improved KAZE algorithm, which is based on nonlinear scale, to raise the number of feature and to enhance the matching rate by using the adjusted-cosine vector. The experiment result shows that the number of feature and the matching rate of the improved KAZE are remarkably than the original KAZE algorithm.

Image-guided interventional therapy for cancer with radiotherapeutic nanoparticles✩

PubMed Central

Phillips, William T.; Bao, Ande; Brenner, Andrew J.; Goins, Beth A.

2015-01-01

One of the major limitations of current cancer therapy is the inability to deliver tumoricidal agents throughout the entire tumor mass using traditional intravenous administration. Nanoparticles carrying beta-emitting therapeutic radionuclides that are delivered using advanced image-guidance have significant potential to improve solid tumor therapy. The use of image-guidance in combination with nanoparticle carriers can improve the delivery of localized radiation to tumors. Nanoparticles labeled with certain beta-emitting radionuclides are intrinsically theranostic agents that can provide information regarding distribution and regional dosimetry within the tumor and the body. Image-guided thermal therapy results in increased uptake of intravenous nanoparticles within tumors, improving therapy. In addition, nanoparticles are ideal carriers for direct intratumoral infusion of beta-emitting radionuclides by convection enhanced delivery, permitting the delivery of localized therapeutic radiation without the requirement of the radionuclide exiting from the nanoparticle. With this approach, very high doses of radiation can be delivered to solid tumors while sparing normal organs. Recent technological developments in image-guidance, convection enhanced delivery and newly developed nanoparticles carrying beta-emitting radionuclides will be reviewed. Examples will be shown describing how this new approach has promise for the treatment of brain, head and neck, and other types of solid tumors. PMID:25016083

An approach of point cloud denoising based on improved bilateral filtering

NASA Astrophysics Data System (ADS)

Zheng, Zeling; Jia, Songmin; Zhang, Guoliang; Li, Xiuzhi; Zhang, Xiangyin

2018-04-01

An omnidirectional mobile platform is designed for building point cloud based on an improved filtering algorithm which is employed to handle the depth image. First, the mobile platform can move flexibly and the control interface is convenient to control. Then, because the traditional bilateral filtering algorithm is time-consuming and inefficient, a novel method is proposed which called local bilateral filtering (LBF). LBF is applied to process depth image obtained by the Kinect sensor. The results show that the effect of removing noise is improved comparing with the bilateral filtering. In the condition of off-line, the color images and processed images are used to build point clouds. Finally, experimental results demonstrate that our method improves the speed of processing time of depth image and the effect of point cloud which has been built.

Visualizing and improving the robustness of phase retrieval algorithms

DOE PAGES

Tripathi, Ashish; Leyffer, Sven; Munson, Todd; ...

2015-06-01

Coherent x-ray diffractive imaging is a novel imaging technique that utilizes phase retrieval and nonlinear optimization methods to image matter at nanometer scales. We explore how the convergence properties of a popular phase retrieval algorithm, Fienup's HIO, behave by introducing a reduced dimensionality problem allowing us to visualize and quantify convergence to local minima and the globally optimal solution. We then introduce generalizations of HIO that improve upon the original algorithm's ability to converge to the globally optimal solution.

Visualizing and improving the robustness of phase retrieval algorithms

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

Tripathi, Ashish; Leyffer, Sven; Munson, Todd

Coherent x-ray diffractive imaging is a novel imaging technique that utilizes phase retrieval and nonlinear optimization methods to image matter at nanometer scales. We explore how the convergence properties of a popular phase retrieval algorithm, Fienup's HIO, behave by introducing a reduced dimensionality problem allowing us to visualize and quantify convergence to local minima and the globally optimal solution. We then introduce generalizations of HIO that improve upon the original algorithm's ability to converge to the globally optimal solution.

Dictionary-based image reconstruction for superresolution in integrated circuit imaging.

PubMed

Cilingiroglu, T Berkin; Uyar, Aydan; Tuysuzoglu, Ahmet; Karl, W Clem; Konrad, Janusz; Goldberg, Bennett B; Ünlü, M Selim

2015-06-01

Resolution improvement through signal processing techniques for integrated circuit imaging is becoming more crucial as the rapid decrease in integrated circuit dimensions continues. Although there is a significant effort to push the limits of optical resolution for backside fault analysis through the use of solid immersion lenses, higher order laser beams, and beam apodization, signal processing techniques are required for additional improvement. In this work, we propose a sparse image reconstruction framework which couples overcomplete dictionary-based representation with a physics-based forward model to improve resolution and localization accuracy in high numerical aperture confocal microscopy systems for backside optical integrated circuit analysis. The effectiveness of the framework is demonstrated on experimental data.

Learning without labeling: domain adaptation for ultrasound transducer localization.

PubMed

Heimann, Tobias; Mountney, Peter; John, Matthias; Ionasec, Razvan

2013-01-01

The fusion of image data from trans-esophageal echography (TEE) and X-ray fluoroscopy is attracting increasing interest in minimally-invasive treatment of structural heart disease. In order to calculate the needed transform between both imaging systems, we employ a discriminative learning based approach to localize the TEE transducer in X-ray images. Instead of time-consuming manual labeling, we generate the required training data automatically from a single volumetric image of the transducer. In order to adapt this system to real X-ray data, we use unlabeled fluoroscopy images to estimate differences in feature space density and correct covariate shift by instance weighting. An evaluation on more than 1900 images reveals that our approach reduces detection failures by 95% compared to cross validation on the test set and improves the localization error from 1.5 to 0.8 mm. Due to the automatic generation of training data, the proposed system is highly flexible and can be adapted to any medical device with minimal efforts.

Bag-of-features approach for improvement of lung tissue classification in diffuse lung disease

NASA Astrophysics Data System (ADS)

Kato, Noriji; Fukui, Motofumi; Isozaki, Takashi

2009-02-01

Many automated techniques have been proposed to classify diffuse lung disease patterns. Most of the techniques utilize texture analysis approaches with second and higher order statistics, and show successful classification result among various lung tissue patterns. However, the approaches do not work well for the patterns with inhomogeneous texture distribution within a region of interest (ROI), such as reticular and honeycombing patterns, because the statistics can only capture averaged feature over the ROI. In this work, we have introduced the bag-of-features approach to overcome this difficulty. In the approach, texture images are represented as histograms or distributions of a few basic primitives, which are obtained by clustering local image features. The intensity descriptor and the Scale Invariant Feature Transformation (SIFT) descriptor are utilized to extract the local features, which have significant discriminatory power due to their specificity to a particular image class. In contrast, the drawback of the local features is lack of invariance under translation and rotation. We improved the invariance by sampling many local regions so that the distribution of the local features is unchanged. We evaluated the performance of our system in the classification task with 5 image classes (ground glass, reticular, honeycombing, emphysema, and normal) using 1109 ROIs from 211 patients. Our system achieved high classification accuracy of 92.8%, which is superior to that of the conventional system with the gray level co-occurrence matrix (GLCM) feature especially for inhomogeneous texture patterns.

Multiview Locally Linear Embedding for Effective Medical Image Retrieval

PubMed Central

Shen, Hualei; Tao, Dacheng; Ma, Dianfu

2013-01-01

Content-based medical image retrieval continues to gain attention for its potential to assist radiological image interpretation and decision making. Many approaches have been proposed to improve the performance of medical image retrieval system, among which visual features such as SIFT, LBP, and intensity histogram play a critical role. Typically, these features are concatenated into a long vector to represent medical images, and thus traditional dimension reduction techniques such as locally linear embedding (LLE), principal component analysis (PCA), or laplacian eigenmaps (LE) can be employed to reduce the “curse of dimensionality”. Though these approaches show promising performance for medical image retrieval, the feature-concatenating method ignores the fact that different features have distinct physical meanings. In this paper, we propose a new method called multiview locally linear embedding (MLLE) for medical image retrieval. Following the patch alignment framework, MLLE preserves the geometric structure of the local patch in each feature space according to the LLE criterion. To explore complementary properties among a range of features, MLLE assigns different weights to local patches from different feature spaces. Finally, MLLE employs global coordinate alignment and alternating optimization techniques to learn a smooth low-dimensional embedding from different features. To justify the effectiveness of MLLE for medical image retrieval, we compare it with conventional spectral embedding methods. We conduct experiments on a subset of the IRMA medical image data set. Evaluation results show that MLLE outperforms state-of-the-art dimension reduction methods. PMID:24349277

Impact of Time-of-Flight on PET Tumor Detection

PubMed Central

Kadrmas, Dan J.; Casey, Michael E.; Conti, Maurizio; Jakoby, Bjoern W.; Lois, Cristina; Townsend, David W.

2009-01-01

Time-of-flight (TOF) PET uses very fast detectors to improve localization of events along coincidence lines-of-response. This information is then utilized to improve the tomographic reconstruction. This work evaluates the effect of TOF upon an observer's performance for detecting and localizing focal warm lesions in noisy PET images. Methods An advanced anthropomorphic lesion-detection phantom was scanned 12 times over 3 days on a prototype TOF PET/CT scanner (Siemens Medical Solutions). The phantom was devised to mimic whole-body oncologic 18F-FDG PET imaging, and a number of spheric lesions (diameters 6–16 mm) were distributed throughout the phantom. The data were reconstructed with the baseline line-of-response ordered-subsets expectation-maximization algorithm, with the baseline algorithm plus point spread function model (PSF), baseline plus TOF, and with both PSF+TOF. The lesion-detection performance of each reconstruction was compared and ranked using localization receiver operating characteristics (LROC) analysis with both human and numeric observers. The phantom results were then subjectively compared to 2 illustrative patient scans reconstructed with PSF and with PSF+TOF. Results Inclusion of TOF information provides a significant improvement in the area under the LROC curve compared to the baseline algorithm without TOF data (P = 0.002), providing a degree of improvement similar to that obtained with the PSF model. Use of both PSF+TOF together provided a cumulative benefit in lesion-detection performance, significantly outperforming either PSF or TOF alone (P < 0.002). Example patient images reflected the same image characteristics that gave rise to improved performance in the phantom data. Conclusion Time-of-flight PET provides a significant improvement in observer performance for detecting focal warm lesions in a noisy background. These improvements in image quality can be expected to improve performance for the clinical tasks of detecting lesions and staging disease. Further study in a large clinical population is warranted to assess the benefit of TOF for various patient sizes and count levels, and to demonstrate effective performance in the clinical environment. PMID:19617317

Robust image region descriptor using local derivative ordinal binary pattern

NASA Astrophysics Data System (ADS)

Shang, Jun; Chen, Chuanbo; Pei, Xiaobing; Liang, Hu; Tang, He; Sarem, Mudar

2015-05-01

Binary image descriptors have received a lot of attention in recent years, since they provide numerous advantages, such as low memory footprint and efficient matching strategy. However, they utilize intermediate representations and are generally less discriminative than floating-point descriptors. We propose an image region descriptor, namely local derivative ordinal binary pattern, for object recognition and image categorization. In order to preserve more local contrast and edge information, we quantize the intensity differences between the central pixels and their neighbors of the detected local affine covariant regions in an adaptive way. These differences are then sorted and mapped into binary codes and histogrammed with a weight of the sum of the absolute value of the differences. Furthermore, the gray level of the central pixel is quantized to further improve the discriminative ability. Finally, we combine them to form a joint histogram to represent the features of the image. We observe that our descriptor preserves more local brightness and edge information than traditional binary descriptors. Also, our descriptor is robust to rotation, illumination variations, and other geometric transformations. We conduct extensive experiments on the standard ETHZ and Kentucky datasets for object recognition and PASCAL for image classification. The experimental results show that our descriptor outperforms existing state-of-the-art methods.

Local structure preserving sparse coding for infrared target recognition

PubMed Central

Han, Jing; Yue, Jiang; Zhang, Yi; Bai, Lianfa

2017-01-01

Sparse coding performs well in image classification. However, robust target recognition requires a lot of comprehensive template images and the sparse learning process is complex. We incorporate sparsity into a template matching concept to construct a local sparse structure matching (LSSM) model for general infrared target recognition. A local structure preserving sparse coding (LSPSc) formulation is proposed to simultaneously preserve the local sparse and structural information of objects. By adding a spatial local structure constraint into the classical sparse coding algorithm, LSPSc can improve the stability of sparse representation for targets and inhibit background interference in infrared images. Furthermore, a kernel LSPSc (K-LSPSc) formulation is proposed, which extends LSPSc to the kernel space to weaken the influence of the linear structure constraint in nonlinear natural data. Because of the anti-interference and fault-tolerant capabilities, both LSPSc- and K-LSPSc-based LSSM can implement target identification based on a simple template set, which just needs several images containing enough local sparse structures to learn a sufficient sparse structure dictionary of a target class. Specifically, this LSSM approach has stable performance in the target detection with scene, shape and occlusions variations. High performance is demonstrated on several datasets, indicating robust infrared target recognition in diverse environments and imaging conditions. PMID:28323824

Novel Approaches to Improve Iris Recognition System Performance Based on Local Quality Evaluation and Feature Fusion

PubMed Central

2014-01-01

For building a new iris template, this paper proposes a strategy to fuse different portions of iris based on machine learning method to evaluate local quality of iris. There are three novelties compared to previous work. Firstly, the normalized segmented iris is divided into multitracks and then each track is estimated individually to analyze the recognition accuracy rate (RAR). Secondly, six local quality evaluation parameters are adopted to analyze texture information of each track. Besides, particle swarm optimization (PSO) is employed to get the weights of these evaluation parameters and corresponding weighted coefficients of different tracks. Finally, all tracks' information is fused according to the weights of different tracks. The experimental results based on subsets of three public and one private iris image databases demonstrate three contributions of this paper. (1) Our experimental results prove that partial iris image cannot completely replace the entire iris image for iris recognition system in several ways. (2) The proposed quality evaluation algorithm is a self-adaptive algorithm, and it can automatically optimize the parameters according to iris image samples' own characteristics. (3) Our feature information fusion strategy can effectively improve the performance of iris recognition system. PMID:24693243

Novel approaches to improve iris recognition system performance based on local quality evaluation and feature fusion.

PubMed

Chen, Ying; Liu, Yuanning; Zhu, Xiaodong; Chen, Huiling; He, Fei; Pang, Yutong

2014-01-01

For building a new iris template, this paper proposes a strategy to fuse different portions of iris based on machine learning method to evaluate local quality of iris. There are three novelties compared to previous work. Firstly, the normalized segmented iris is divided into multitracks and then each track is estimated individually to analyze the recognition accuracy rate (RAR). Secondly, six local quality evaluation parameters are adopted to analyze texture information of each track. Besides, particle swarm optimization (PSO) is employed to get the weights of these evaluation parameters and corresponding weighted coefficients of different tracks. Finally, all tracks' information is fused according to the weights of different tracks. The experimental results based on subsets of three public and one private iris image databases demonstrate three contributions of this paper. (1) Our experimental results prove that partial iris image cannot completely replace the entire iris image for iris recognition system in several ways. (2) The proposed quality evaluation algorithm is a self-adaptive algorithm, and it can automatically optimize the parameters according to iris image samples' own characteristics. (3) Our feature information fusion strategy can effectively improve the performance of iris recognition system.

Detecting Surgical Tools by Modelling Local Appearance and Global Shape.

PubMed

Bouget, David; Benenson, Rodrigo; Omran, Mohamed; Riffaud, Laurent; Schiele, Bernt; Jannin, Pierre

2015-12-01

Detecting tools in surgical videos is an important ingredient for context-aware computer-assisted surgical systems. To this end, we present a new surgical tool detection dataset and a method for joint tool detection and pose estimation in 2d images. Our two-stage pipeline is data-driven and relaxes strong assumptions made by previous works regarding the geometry, number, and position of tools in the image. The first stage classifies each pixel based on local appearance only, while the second stage evaluates a tool-specific shape template to enforce global shape. Both local appearance and global shape are learned from training data. Our method is validated on a new surgical tool dataset of 2 476 images from neurosurgical microscopes, which is made freely available. It improves over existing datasets in size, diversity and detail of annotation. We show that our method significantly improves over competitive baselines from the computer vision field. We achieve 15% detection miss-rate at 10(-1) false positives per image (for the suction tube) over our surgical tool dataset. Results indicate that performing semantic labelling as an intermediate task is key for high quality detection.

Knowledge-Based Topic Model for Unsupervised Object Discovery and Localization.

PubMed

Niu, Zhenxing; Hua, Gang; Wang, Le; Gao, Xinbo

Unsupervised object discovery and localization is to discover some dominant object classes and localize all of object instances from a given image collection without any supervision. Previous work has attempted to tackle this problem with vanilla topic models, such as latent Dirichlet allocation (LDA). However, in those methods no prior knowledge for the given image collection is exploited to facilitate object discovery. On the other hand, the topic models used in those methods suffer from the topic coherence issue-some inferred topics do not have clear meaning, which limits the final performance of object discovery. In this paper, prior knowledge in terms of the so-called must-links are exploited from Web images on the Internet. Furthermore, a novel knowledge-based topic model, called LDA with mixture of Dirichlet trees, is proposed to incorporate the must-links into topic modeling for object discovery. In particular, to better deal with the polysemy phenomenon of visual words, the must-link is re-defined as that one must-link only constrains one or some topic(s) instead of all topics, which leads to significantly improved topic coherence. Moreover, the must-links are built and grouped with respect to specific object classes, thus the must-links in our approach are semantic-specific , which allows to more efficiently exploit discriminative prior knowledge from Web images. Extensive experiments validated the efficiency of our proposed approach on several data sets. It is shown that our method significantly improves topic coherence and outperforms the unsupervised methods for object discovery and localization. In addition, compared with discriminative methods, the naturally existing object classes in the given image collection can be subtly discovered, which makes our approach well suited for realistic applications of unsupervised object discovery.Unsupervised object discovery and localization is to discover some dominant object classes and localize all of object instances from a given image collection without any supervision. Previous work has attempted to tackle this problem with vanilla topic models, such as latent Dirichlet allocation (LDA). However, in those methods no prior knowledge for the given image collection is exploited to facilitate object discovery. On the other hand, the topic models used in those methods suffer from the topic coherence issue-some inferred topics do not have clear meaning, which limits the final performance of object discovery. In this paper, prior knowledge in terms of the so-called must-links are exploited from Web images on the Internet. Furthermore, a novel knowledge-based topic model, called LDA with mixture of Dirichlet trees, is proposed to incorporate the must-links into topic modeling for object discovery. In particular, to better deal with the polysemy phenomenon of visual words, the must-link is re-defined as that one must-link only constrains one or some topic(s) instead of all topics, which leads to significantly improved topic coherence. Moreover, the must-links are built and grouped with respect to specific object classes, thus the must-links in our approach are semantic-specific , which allows to more efficiently exploit discriminative prior knowledge from Web images. Extensive experiments validated the efficiency of our proposed approach on several data sets. It is shown that our method significantly improves topic coherence and outperforms the unsupervised methods for object discovery and localization. In addition, compared with discriminative methods, the naturally existing object classes in the given image collection can be subtly discovered, which makes our approach well suited for realistic applications of unsupervised object discovery.

[Importance of parathyroid SPECT and 99mTc scintigraphy, and of clinical, laboratorial, ultrasonographic and citologic correlation in the pre-operative localization of the parathyroid adenoma - pictorial assay].

PubMed

Oliveira, Marco Antônio Condé de; Maeda, Sérgio Setsuo; Dreyer, Patrícia; Lobo, Alberto; Andrade, Victor Piana de; Hoff, Ana O; Biscolla, Rosa Paula Mello; Smanio, Paola; Brandão, Cynthia M A; Vieira, José G

2010-06-01

In patients with primary hyperparathyroidism, candidates for surgical intervention, the parathyroid pre-operative localization is of fundamental importance in planning the appropriate surgical approach. The additional acquisition of SPECT and Technetium-99m images, during parathyroid scintigraphy with Sestamibi, is not common practice. Usually, only planar image acquisition, 15 minutes prior and 2 hours after radiopharmaceutical administration, is performed. In our experience, the complete protocol in parathyroid scintigraphy increases the accuracy of pre-operative parathyroid localization. The complete utilization of all available nuclear medicine methods (SPECT e 99mTc) and image interpretation in a multidisciplinary context can improve the accuracy of parathyroid scintigraphy.

OCT despeckling via weighted nuclear norm constrained non-local low-rank representation

NASA Astrophysics Data System (ADS)

Tang, Chang; Zheng, Xiao; Cao, Lijuan

2017-10-01

As a non-invasive imaging modality, optical coherence tomography (OCT) plays an important role in medical sciences. However, OCT images are always corrupted by speckle noise, which can mask image features and pose significant challenges for medical analysis. In this work, we propose an OCT despeckling method by using non-local, low-rank representation with weighted nuclear norm constraint. Unlike previous non-local low-rank representation based OCT despeckling methods, we first generate a guidance image to improve the non-local group patches selection quality, then a low-rank optimization model with a weighted nuclear norm constraint is formulated to process the selected group patches. The corrupted probability of each pixel is also integrated into the model as a weight to regularize the representation error term. Note that each single patch might belong to several groups, hence different estimates of each patch are aggregated to obtain its final despeckled result. Both qualitative and quantitative experimental results on real OCT images show the superior performance of the proposed method compared with other state-of-the-art speckle removal techniques.

TU-D-207B-03: Early Assessment of Response to Chemoradiotherapy Based On Textural Analysis of Pre and Mid-Treatment FDG-PET Image in Locally Advanced Head and Neck Cancer

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

Cui, Y; Pollom, E; Loo, B

Purpose: To evaluate whether tumor textural features extracted from both pre- and mid-treatment FDG-PET images predict early response to chemoradiotherapy in locally advanced head and neck cancer, and investigate whether they provide complementary value to conventional volume-based measurements. Methods: Ninety-four patients with locally advanced head and neck cancers were retrospectively studied. All patients received definitive chemoradiotherapy and underwent FDG-PET planning scans both before and during treatment. Within the primary tumor we extracted 6 textural features based on gray-level co-occurrence matrices (GLCM): entropy, dissimilarity, contrast, correlation, energy, and homogeneity. These image features were evaluated for their predictive power of treatment responsemore » to chemoradiotherapy in terms of local recurrence free survival (LRFS) and progression free survival (PFS). Logrank test were used to assess the statistical significance of the stratification between low- and high-risk groups. P-values were adjusted for multiple comparisons by the false discovery rate (FDR) method. Results: All six textural features extracted from pre-treatment PET images significantly differentiated low- and high-risk patient groups for LRFS (P=0.011–0.038) and PFS (P=0.029–0.034). On the other hand, none of the textural features on mid-treatment PET images was statistically significant in stratifying LRFS (P=0.212–0.445) or PFS (P=0.168–0.299). An imaging signature that combines textural feature (GLCM homogeneity) and metabolic tumor volume showed an improved performance for predicting LRFS (hazard ratio: 22.8, P<0.0001) and PFS (hazard ratio: 13.9, P=0.0005) in leave-one-out cross validation. Intra-tumor heterogeneity measured by textural features was significantly lower in mid-treatment PET images than in pre-treatment PET images (T-test: P<1.4e-6). Conclusion: Tumor textural features on pretreatment FDG-PET images are predictive for response to chemoradiotherapy in locally advanced head and neck cancer. The complementary information offered by textural features improves patient stratification and may potentially aid in personalized risk-adaptive therapy.« less

Improving limited-projection-angle fluorescence molecular tomography using a co-registered x-ray computed tomography scan.

PubMed

Radrich, Karin; Ale, Angelique; Ermolayev, Vladimir; Ntziachristos, Vasilis

2012-12-01

We examine the improvement in imaging performance, such as axial resolution and signal localization, when employing limited-projection-angle fluorescence molecular tomography (FMT) together with x-ray computed tomography (XCT) measurements versus stand-alone FMT. For this purpose, we employed living mice, bearing a spontaneous lung tumor model, and imaged them with FMT and XCT under identical geometrical conditions using fluorescent probes for cancer targeting. The XCT data was employed, herein, as structural prior information to guide the FMT reconstruction. Gold standard images were provided by fluorescence images of mouse cryoslices, providing the ground truth in fluorescence bio-distribution. Upon comparison of FMT images versus images reconstructed using hybrid FMT and XCT data, we demonstrate marked improvements in image accuracy. This work relates to currently disseminated FMT systems, using limited projection scans, and can be employed to enhance their performance.

lop-DWI: A Novel Scheme for Pre-Processing of Diffusion-Weighted Images in the Gradient Direction Domain.

PubMed

Sepehrband, Farshid; Choupan, Jeiran; Caruyer, Emmanuel; Kurniawan, Nyoman D; Gal, Yaniv; Tieng, Quang M; McMahon, Katie L; Vegh, Viktor; Reutens, David C; Yang, Zhengyi

2014-01-01

We describe and evaluate a pre-processing method based on a periodic spiral sampling of diffusion-gradient directions for high angular resolution diffusion magnetic resonance imaging. Our pre-processing method incorporates prior knowledge about the acquired diffusion-weighted signal, facilitating noise reduction. Periodic spiral sampling of gradient direction encodings results in an acquired signal in each voxel that is pseudo-periodic with characteristics that allow separation of low-frequency signal from high frequency noise. Consequently, it enhances local reconstruction of the orientation distribution function used to define fiber tracks in the brain. Denoising with periodic spiral sampling was tested using synthetic data and in vivo human brain images. The level of improvement in signal-to-noise ratio and in the accuracy of local reconstruction of fiber tracks was significantly improved using our method.

Detection of mitotic nuclei in breast histopathology images using localized ACM and Random Kitchen Sink based classifier.

PubMed

Beevi, K Sabeena; Nair, Madhu S; Bindu, G R

2016-08-01

The exact measure of mitotic nuclei is a crucial parameter in breast cancer grading and prognosis. This can be achieved by improving the mitotic detection accuracy by careful design of segmentation and classification techniques. In this paper, segmentation of nuclei from breast histopathology images are carried out by Localized Active Contour Model (LACM) utilizing bio-inspired optimization techniques in the detection stage, in order to handle diffused intensities present along object boundaries. Further, the application of a new optimal machine learning algorithm capable of classifying strong non-linear data such as Random Kitchen Sink (RKS), shows improved classification performance. The proposed method has been tested on Mitosis detection in breast cancer histological images (MITOS) dataset provided for MITOS-ATYPIA CONTEST 2014. The proposed framework achieved 95% recall, 98% precision and 96% F-score.

Image guided radiation therapy may result in improved local control in locally advanced lung cancer patients.

PubMed

Kilburn, Jeremy M; Soike, Michael H; Lucas, John T; Ayala-Peacock, Diandra; Blackstock, William; Isom, Scott; Kearns, William T; Hinson, William H; Miller, Antonius A; Petty, William J; Munley, Michael T; Urbanic, James J

2016-01-01

Image guided radiation therapy (IGRT) is designed to ensure accurate and precise targeting, but whether improved clinical outcomes result is unknown. A retrospective comparison of locally advanced lung cancer patients treated with and without IGRT from 2001 to 2012 was conducted. Median local failure-free survival (LFFS), regional, locoregional failure-free survival (LRFFS), distant failure-free survival, progression-free survival, and overall survival (OS) were estimated. Univariate and multivariate models assessed the association between patient- and treatment-related covariates and local failure. A total of 169 patients were treated with definitive radiation therapy and concurrent chemotherapy with a median follow-up of 48 months in the IGRT cohort and 96 months in the non-IGRT cohort. IGRT was used in 36% (62 patients) of patients. OS was similar between cohorts (2-year OS, 47% vs 49%, P = .63). The IGRT cohort had improved 2-year LFFS (80% vs 64%, P = .013) and LRFFS (75% and 62%, P = .04). Univariate analysis revealed IGRT and treatment year improved LFFS, whereas group stage, dose, and positron emission tomography/computed tomography planning had no impact. IGRT remained significant in the multivariate model with an adjusted hazard ratio of 0.40 (P = .01). Distant failure-free survival (58% vs 59%, P = .67) did not differ significantly. IGRT with daily cone beam computed tomography confers an improvement in the therapeutic ratio relative to patients treated without this technology. Copyright © 2015 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.

Uniform competency-based local feature extraction for remote sensing images

NASA Astrophysics Data System (ADS)

Sedaghat, Amin; Mohammadi, Nazila

2018-01-01

Local feature detectors are widely used in many photogrammetry and remote sensing applications. The quantity and distribution of the local features play a critical role in the quality of the image matching process, particularly for multi-sensor high resolution remote sensing image registration. However, conventional local feature detectors cannot extract desirable matched features either in terms of the number of correct matches or the spatial and scale distribution in multi-sensor remote sensing images. To address this problem, this paper proposes a novel method for uniform and robust local feature extraction for remote sensing images, which is based on a novel competency criterion and scale and location distribution constraints. The proposed method, called uniform competency (UC) local feature extraction, can be easily applied to any local feature detector for various kinds of applications. The proposed competency criterion is based on a weighted ranking process using three quality measures, including robustness, spatial saliency and scale parameters, which is performed in a multi-layer gridding schema. For evaluation, five state-of-the-art local feature detector approaches, namely, scale-invariant feature transform (SIFT), speeded up robust features (SURF), scale-invariant feature operator (SFOP), maximally stable extremal region (MSER) and hessian-affine, are used. The proposed UC-based feature extraction algorithms were successfully applied to match various synthetic and real satellite image pairs, and the results demonstrate its capability to increase matching performance and to improve the spatial distribution. The code to carry out the UC feature extraction is available from href="https://www.researchgate.net/publication/317956777_UC-Feature_Extraction.

Improved wheal detection from skin prick test images

NASA Astrophysics Data System (ADS)

Bulan, Orhan

2014-03-01

Skin prick test is a commonly used method for diagnosis of allergic diseases (e.g., pollen allergy, food allergy, etc.) in allergy clinics. The results of this test are erythema and wheal provoked on the skin where the test is applied. The sensitivity of the patient against a specific allergen is determined by the physical size of the wheal, which can be estimated from images captured by digital cameras. Accurate wheal detection from these images is an important step for precise estimation of wheal size. In this paper, we propose a method for improved wheal detection on prick test images captured by digital cameras. Our method operates by first localizing the test region by detecting calibration marks drawn on the skin. The luminance variation across the localized region is eliminated by applying a color transformation from RGB to YCbCr and discarding the luminance channel. We enhance the contrast of the captured images for the purpose of wheal detection by performing principal component analysis on the blue-difference (Cb) and red-difference (Cr) color channels. We finally, perform morphological operations on the contrast enhanced image to detect the wheal on the image plane. Our experiments performed on images acquired from 36 different patients show the efficiency of the proposed method for wheal detection from skin prick test images captured in an uncontrolled environment.

Localized Spatio-Temporal Constraints for Accelerated CMR Perfusion

PubMed Central

Akçakaya, Mehmet; Basha, Tamer A.; Pflugi, Silvio; Foppa, Murilo; Kissinger, Kraig V.; Hauser, Thomas H.; Nezafat, Reza

2013-01-01

Purpose To develop and evaluate an image reconstruction technique for cardiac MRI (CMR)perfusion that utilizes localized spatio-temporal constraints. Methods CMR perfusion plays an important role in detecting myocardial ischemia in patients with coronary artery disease. Breath-hold k-t based image acceleration techniques are typically used in CMR perfusion for superior spatial/temporal resolution, and improved coverage. In this study, we propose a novel compressed sensing based image reconstruction technique for CMR perfusion, with applicability to free-breathing examinations. This technique uses local spatio-temporal constraints by regularizing image patches across a small number of dynamics. The technique is compared to conventional dynamic-by-dynamic reconstruction, and sparsity regularization using a temporal principal-component (pc) basis, as well as zerofilled data in multi-slice 2D and 3D CMR perfusion. Qualitative image scores are used (1=poor, 4=excellent) to evaluate the technique in 3D perfusion in 10 patients and 5 healthy subjects. On 4 healthy subjects, the proposed technique was also compared to a breath-hold multi-slice 2D acquisition with parallel imaging in terms of signal intensity curves. Results The proposed technique results in images that are superior in terms of spatial and temporal blurring compared to the other techniques, even in free-breathing datasets. The image scores indicate a significant improvement compared to other techniques in 3D perfusion (2.8±0.5 vs. 2.3±0.5 for x-pc regularization, 1.7±0.5 for dynamic-by-dynamic, 1.1±0.2 for zerofilled). Signal intensity curves indicate similar dynamics of uptake between the proposed method with a 3D acquisition and the breath-hold multi-slice 2D acquisition with parallel imaging. Conclusion The proposed reconstruction utilizes sparsity regularization based on localized information in both spatial and temporal domains for highly-accelerated CMR perfusion with potential utility in free-breathing 3D acquisitions. PMID:24123058

Comparative OCT imaging of the human esophagus: How well can we localize the muscularis mucosae?

NASA Astrophysics Data System (ADS)

Cilesiz, Inci F.; Fockens, Paul; Kerindongo, Raphaela P.; Faber, Dirk J.; Tytgat, Guido N. J.; ten Kate, Febo; van Leeuwen, Ton G. J. M.

2002-06-01

Early diagnosis with esophageal cancer limited to the mucosa will allow for local endoscopic treatment and improve prognosis. We compared with histology OCT images of healthy human esophageal tissue from two systems operating at 800 and 1275 nm to investigate which wavelength was best suited for detailed OCT imaging of the esophageal wall, and to localize the muscularis mucosae. Within an hour of surgical resection, an esophageal specimen was cleaned of excess blood and soaked in formalin for a minimum of 48 hours. In order to precisely localize the different layers of the esophageal wall on an OCT image, well-defined structures within the esophageal wall were sought. Following OCT imaging the specimen was prepared for routine histology. We observed that our 1275 nm system with 12 micrometers resolution was superior in terms of penetration. As compared to histology, the 4 micrometers resolution of our 800 nm system made fine details more visible. Using either system, a minimally trained eye could recognize the muscularis mucosae as a hypo-reflective layer. Although different conditions may apply in vivo, our ex vivo study paves the path to precise interpretation of OCT images of the esophageal wall.

An algorithm for automated detection, localization and measurement of local calcium signals from camera-based imaging.

PubMed

Ellefsen, Kyle L; Settle, Brett; Parker, Ian; Smith, Ian F

2014-09-01

Local Ca(2+) transients such as puffs and sparks form the building blocks of cellular Ca(2+) signaling in numerous cell types. They have traditionally been studied by linescan confocal microscopy, but advances in TIRF microscopy together with improved electron-multiplied CCD (EMCCD) cameras now enable rapid (>500 frames s(-1)) imaging of subcellular Ca(2+) signals with high spatial resolution in two dimensions. This approach yields vastly more information (ca. 1 Gb min(-1)) than linescan imaging, rendering visual identification and analysis of local events imaged both laborious and subject to user bias. Here we describe a routine to rapidly automate identification and analysis of local Ca(2+) events. This features an intuitive graphical user-interfaces and runs under Matlab and the open-source Python software. The underlying algorithm features spatial and temporal noise filtering to reliably detect even small events in the presence of noisy and fluctuating baselines; localizes sites of Ca(2+) release with sub-pixel resolution; facilitates user review and editing of data; and outputs time-sequences of fluorescence ratio signals for identified event sites along with Excel-compatible tables listing amplitudes and kinetics of events. Copyright © 2014 Elsevier Ltd. All rights reserved.

Registration of PET and CT images based on multiresolution gradient of mutual information demons algorithm for positioning esophageal cancer patients.

PubMed

Jin, Shuo; Li, Dengwang; Wang, Hongjun; Yin, Yong

2013-01-07

Accurate registration of 18F-FDG PET (positron emission tomography) and CT (computed tomography) images has important clinical significance in radiation oncology. PET and CT images are acquired from (18)F-FDG PET/CT scanner, but the two acquisition processes are separate and take a long time. As a result, there are position errors in global and deformable errors in local caused by respiratory movement or organ peristalsis. The purpose of this work was to implement and validate a deformable CT to PET image registration method in esophageal cancer to eventually facilitate accurate positioning the tumor target on CT, and improve the accuracy of radiation therapy. Global registration was firstly utilized to preprocess position errors between PET and CT images, achieving the purpose of aligning these two images on the whole. Demons algorithm, based on optical flow field, has the features of fast process speed and high accuracy, and the gradient of mutual information-based demons (GMI demons) algorithm adds an additional external force based on the gradient of mutual information (GMI) between two images, which is suitable for multimodality images registration. In this paper, GMI demons algorithm was used to achieve local deformable registration of PET and CT images, which can effectively reduce errors between internal organs. In addition, to speed up the registration process, maintain its robustness, and avoid the local extremum, multiresolution image pyramid structure was used before deformable registration. By quantitatively and qualitatively analyzing cases with esophageal cancer, the registration scheme proposed in this paper can improve registration accuracy and speed, which is helpful for precisely positioning tumor target and developing the radiation treatment planning in clinical radiation therapy application.

Registration of PET and CT images based on multiresolution gradient of mutual information demons algorithm for positioning esophageal cancer patients

PubMed Central

Jin, Shuo; Li, Dengwang; Yin, Yong

2013-01-01

Accurate registration of  18F−FDG PET (positron emission tomography) and CT (computed tomography) images has important clinical significance in radiation oncology. PET and CT images are acquired from  18F−FDG PET/CT scanner, but the two acquisition processes are separate and take a long time. As a result, there are position errors in global and deformable errors in local caused by respiratory movement or organ peristalsis. The purpose of this work was to implement and validate a deformable CT to PET image registration method in esophageal cancer to eventually facilitate accurate positioning the tumor target on CT, and improve the accuracy of radiation therapy. Global registration was firstly utilized to preprocess position errors between PET and CT images, achieving the purpose of aligning these two images on the whole. Demons algorithm, based on optical flow field, has the features of fast process speed and high accuracy, and the gradient of mutual information‐based demons (GMI demons) algorithm adds an additional external force based on the gradient of mutual information (GMI) between two images, which is suitable for multimodality images registration. In this paper, GMI demons algorithm was used to achieve local deformable registration of PET and CT images, which can effectively reduce errors between internal organs. In addition, to speed up the registration process, maintain its robustness, and avoid the local extremum, multiresolution image pyramid structure was used before deformable registration. By quantitatively and qualitatively analyzing cases with esophageal cancer, the registration scheme proposed in this paper can improve registration accuracy and speed, which is helpful for precisely positioning tumor target and developing the radiation treatment planning in clinical radiation therapy application. PACS numbers: 87.57.nj, 87.57.Q‐, 87.57.uk PMID:23318381

An Integrated Tone Mapping for High Dynamic Range Image Visualization

NASA Astrophysics Data System (ADS)

Liang, Lei; Pan, Jeng-Shyang; Zhuang, Yongjun

2018-01-01

There are two type tone mapping operators for high dynamic range (HDR) image visualization. HDR image mapped by perceptual operators have strong sense of reality, but will lose local details. Empirical operators can maximize local detail information of HDR image, but realism is not strong. A common tone mapping operator suitable for all applications is not available. This paper proposes a novel integrated tone mapping framework which can achieve conversion between empirical operators and perceptual operators. In this framework, the empirical operator is rendered based on improved saliency map, which simulates the visual attention mechanism of the human eye to the natural scene. The results of objective evaluation prove the effectiveness of the proposed solution.

Diffusion Weighted Image Denoising Using Overcomplete Local PCA

PubMed Central

Manjón, José V.; Coupé, Pierrick; Concha, Luis; Buades, Antonio; Collins, D. Louis; Robles, Montserrat

2013-01-01

Diffusion Weighted Images (DWI) normally shows a low Signal to Noise Ratio (SNR) due to the presence of noise from the measurement process that complicates and biases the estimation of quantitative diffusion parameters. In this paper, a new denoising methodology is proposed that takes into consideration the multicomponent nature of multi-directional DWI datasets such as those employed in diffusion imaging. This new filter reduces random noise in multicomponent DWI by locally shrinking less significant Principal Components using an overcomplete approach. The proposed method is compared with state-of-the-art methods using synthetic and real clinical MR images, showing improved performance in terms of denoising quality and estimation of diffusion parameters. PMID:24019889

Online updating of context-aware landmark detectors for prostate localization in daily treatment CT images

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

Dai, Xiubin; Gao, Yaozong; Shen, Dinggang, E-mail: dgshen@med.unc.edu

2015-05-15

Purpose: In image guided radiation therapy, it is crucial to fast and accurately localize the prostate in the daily treatment images. To this end, the authors propose an online update scheme for landmark-guided prostate segmentation, which can fully exploit valuable patient-specific information contained in the previous treatment images and can achieve improved performance in landmark detection and prostate segmentation. Methods: To localize the prostate in the daily treatment images, the authors first automatically detect six anatomical landmarks on the prostate boundary by adopting a context-aware landmark detection method. Specifically, in this method, a two-layer regression forest is trained as amore » detector for each target landmark. Once all the newly detected landmarks from new treatment images are reviewed or adjusted (if necessary) by clinicians, they are further included into the training pool as new patient-specific information to update all the two-layer regression forests for the next treatment day. As more and more treatment images of the current patient are acquired, the two-layer regression forests can be continually updated by incorporating the patient-specific information into the training procedure. After all target landmarks are detected, a multiatlas random sample consensus (multiatlas RANSAC) method is used to segment the entire prostate by fusing multiple previously segmented prostates of the current patient after they are aligned to the current treatment image. Subsequently, the segmented prostate of the current treatment image is again reviewed (or even adjusted if needed) by clinicians before including it as a new shape example into the prostate shape dataset for helping localize the entire prostate in the next treatment image. Results: The experimental results on 330 images of 24 patients show the effectiveness of the authors’ proposed online update scheme in improving the accuracies of both landmark detection and prostate segmentation. Besides, compared to the other state-of-the-art prostate segmentation methods, the authors’ method achieves the best performance. Conclusions: By appropriate use of valuable patient-specific information contained in the previous treatment images, the authors’ proposed online update scheme can obtain satisfactory results for both landmark detection and prostate segmentation.« less

Mechanical stability of a microscope setup working at a few kelvins for single-molecule localization

NASA Astrophysics Data System (ADS)

Hinohara, Takuya; Hamada, Yuki I.; Nakamura, Ippei; Matsushita, Michio; Fujiyoshi, Satoru

2013-06-01

A great advantage of single-molecule fluorescence imaging is the localization precision of molecule beyond the diffraction limit. Although longer signal-acquisition yields higher precision, acquisition time at room temperature is normally limited by photobleaching, thermal diffusion, and so on. At low temperature of a few kelvins, much longer acquisition is possible and will improve precision if the sample and the objective are held stably enough. The present work examined holding stability of the sample and objective at 1.5 K in superfluid helium in the helium bath. The stability was evaluated by localization precision of a point scattering source of a polymer bead. Scattered light was collected by the objective, and imaged by a home-built rigid imaging unit. The standard deviation of the centroid position determined for 800 images taken continuously in 17 min was 0.5 nm in the horizontal and 0.9 nm in the vertical directions.

PET Image Reconstruction Incorporating 3D Mean-Median Sinogram Filtering

NASA Astrophysics Data System (ADS)

Mokri, S. S.; Saripan, M. I.; Rahni, A. A. Abd; Nordin, A. J.; Hashim, S.; Marhaban, M. H.

2016-02-01

Positron Emission Tomography (PET) projection data or sinogram contained poor statistics and randomness that produced noisy PET images. In order to improve the PET image, we proposed an implementation of pre-reconstruction sinogram filtering based on 3D mean-median filter. The proposed filter is designed based on three aims; to minimise angular blurring artifacts, to smooth flat region and to preserve the edges in the reconstructed PET image. The performance of the pre-reconstruction sinogram filter prior to three established reconstruction methods namely filtered-backprojection (FBP), Maximum likelihood expectation maximization-Ordered Subset (OSEM) and OSEM with median root prior (OSEM-MRP) is investigated using simulated NCAT phantom PET sinogram as generated by the PET Analytical Simulator (ASIM). The improvement on the quality of the reconstructed images with and without sinogram filtering is assessed according to visual as well as quantitative evaluation based on global signal to noise ratio (SNR), local SNR, contrast to noise ratio (CNR) and edge preservation capability. Further analysis on the achieved improvement is also carried out specific to iterative OSEM and OSEM-MRP reconstruction methods with and without pre-reconstruction filtering in terms of contrast recovery curve (CRC) versus noise trade off, normalised mean square error versus iteration, local CNR versus iteration and lesion detectability. Overall, satisfactory results are obtained from both visual and quantitative evaluations.

Non-local means denoising of dynamic PET images.

PubMed

Dutta, Joyita; Leahy, Richard M; Li, Quanzheng

2013-01-01

Dynamic positron emission tomography (PET), which reveals information about both the spatial distribution and temporal kinetics of a radiotracer, enables quantitative interpretation of PET data. Model-based interpretation of dynamic PET images by means of parametric fitting, however, is often a challenging task due to high levels of noise, thus necessitating a denoising step. The objective of this paper is to develop and characterize a denoising framework for dynamic PET based on non-local means (NLM). NLM denoising computes weighted averages of voxel intensities assigning larger weights to voxels that are similar to a given voxel in terms of their local neighborhoods or patches. We introduce three key modifications to tailor the original NLM framework to dynamic PET. Firstly, we derive similarities from less noisy later time points in a typical PET acquisition to denoise the entire time series. Secondly, we use spatiotemporal patches for robust similarity computation. Finally, we use a spatially varying smoothing parameter based on a local variance approximation over each spatiotemporal patch. To assess the performance of our denoising technique, we performed a realistic simulation on a dynamic digital phantom based on the Digimouse atlas. For experimental validation, we denoised [Formula: see text] PET images from a mouse study and a hepatocellular carcinoma patient study. We compared the performance of NLM denoising with four other denoising approaches - Gaussian filtering, PCA, HYPR, and conventional NLM based on spatial patches. The simulation study revealed significant improvement in bias-variance performance achieved using our NLM technique relative to all the other methods. The experimental data analysis revealed that our technique leads to clear improvement in contrast-to-noise ratio in Patlak parametric images generated from denoised preclinical and clinical dynamic images, indicating its ability to preserve image contrast and high intensity details while lowering the background noise variance.

Peering into Cells One Molecule at a Time: Single-molecule and plasmon-enhanced fluorescence super-resolution imaging

NASA Astrophysics Data System (ADS)

Biteen, Julie

2013-03-01

Single-molecule fluorescence brings the resolution of optical microscopy down to the nanometer scale, allowing us to unlock the mysteries of how biomolecules work together to achieve the complexity that is a cell. This high-resolution, non-destructive method for examining subcellular events has opened up an exciting new frontier: the study of macromolecular localization and dynamics in living cells. We have developed methods for single-molecule investigations of live bacterial cells, and have used these techniques to investigate thee important prokaryotic systems: membrane-bound transcription activation in Vibrio cholerae, carbohydrate catabolism in Bacteroides thetaiotaomicron, and DNA mismatch repair in Bacillus subtilis. Each system presents unique challenges, and we will discuss the important methods developed for each system. Furthermore, we use the plasmon modes of bio-compatible metal nanoparticles to enhance the emissivity of single-molecule fluorophores. The resolution of single-molecule imaging in cells is generally limited to 20-40 nm, far worse than the 1.5-nm localization accuracies which have been attained in vitro. We use plasmonics to improve the brightness and stability of single-molecule probes, and in particular fluorescent proteins, which are widely used for bio-imaging. We find that gold-coupled fluorophores demonstrate brighter, longer-lived emission, yielding an overall enhancement in total photons detected. Ultimately, this results in increased localization accuracy for single-molecule imaging. Furthermore, since fluorescence intensity is proportional to local electromagnetic field intensity, these changes in decay intensity and rate serve as a nm-scale read-out of the field intensity. Our work indicates that plasmonic substrates are uniquely advantageous for super-resolution imaging, and that plasmon-enhanced imaging is a promising technique for improving live cell single-molecule microscopy.

Adapting Local Features for Face Detection in Thermal Image.

PubMed

Ma, Chao; Trung, Ngo Thanh; Uchiyama, Hideaki; Nagahara, Hajime; Shimada, Atsushi; Taniguchi, Rin-Ichiro

2017-11-27

A thermal camera captures the temperature distribution of a scene as a thermal image. In thermal images, facial appearances of different people under different lighting conditions are similar. This is because facial temperature distribution is generally constant and not affected by lighting condition. This similarity in face appearances is advantageous for face detection. To detect faces in thermal images, cascade classifiers with Haar-like features are generally used. However, there are few studies exploring the local features for face detection in thermal images. In this paper, we introduce two approaches relying on local features for face detection in thermal images. First, we create new feature types by extending Multi-Block LBP. We consider a margin around the reference and the generally constant distribution of facial temperature. In this way, we make the features more robust to image noise and more effective for face detection in thermal images. Second, we propose an AdaBoost-based training method to get cascade classifiers with multiple types of local features. These feature types have different advantages. In this way we enhance the description power of local features. We did a hold-out validation experiment and a field experiment. In the hold-out validation experiment, we captured a dataset from 20 participants, comprising 14 males and 6 females. For each participant, we captured 420 images with 10 variations in camera distance, 21 poses, and 2 appearances (participant with/without glasses). We compared the performance of cascade classifiers trained by different sets of the features. The experiment results showed that the proposed approaches effectively improve the performance of face detection in thermal images. In the field experiment, we compared the face detection performance in realistic scenes using thermal and RGB images, and gave discussion based on the results.

Imaging-Assisted Large-Format Breast Pathology: Program Rationale and Development in a Nonprofit Health System in the United States

PubMed Central

Tucker, F. Lee

2012-01-01

Modern breast imaging, including magnetic resonance imaging, provides an increasingly clear depiction of breast cancer extent, often with suboptimal pathologic confirmation. Pathologic findings guide management decisions, and small increments in reported tumor characteristics may rationalize significant changes in therapy and staging. Pathologic techniques to grossly examine resected breast tissue have changed little during this era of improved breast imaging and still rely primarily on the techniques of gross inspection and specimen palpation. Only limited imaging information is typically conveyed to pathologists, typically in the form of wire-localization images from breast-conserving procedures. Conventional techniques of specimen dissection and section submission destroy the three-dimensional integrity of the breast anatomy and tumor distribution. These traditional methods of breast specimen examination impose unnecessary limitations on correlation with imaging studies, measurement of cancer extent, multifocality, and margin distance. Improvements in pathologic diagnosis, reporting, and correlation of breast cancer characteristics can be achieved by integrating breast imagers into the specimen examination process and the use of large-format sections which preserve local anatomy. This paper describes the successful creation of a large-format pathology program to routinely serve all patients in a busy interdisciplinary breast center associated with a community-based nonprofit health system in the United States. PMID:23316372

Reconstruction of biofilm images: combining local and global structural parameters

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

Resat, Haluk; Renslow, Ryan S.; Beyenal, Haluk

2014-10-20

Digitized images can be used for quantitative comparison of biofilms grown under different conditions. Using biofilm image reconstruction, it was previously found that biofilms with a completely different look can have nearly identical structural parameters and that the most commonly utilized global structural parameters were not sufficient to uniquely define these biofilms. Here, additional local and global parameters are introduced to show that these parameters considerably increase the reliability of the image reconstruction process. Assessment using human evaluators indicated that the correct identification rate of the reconstructed images increased from 50% to 72% with the introduction of the new parametersmore » into the reconstruction procedure. An expanded set of parameters especially improved the identification of biofilm structures with internal orientational features and of structures in which colony sizes and spatial locations varied. Hence, the newly introduced structural parameter sets helped to better classify the biofilms by incorporating finer local structural details into the reconstruction process.« less

Optimization of prostate biopsy: the role of magnetic resonance imaging targeted biopsy in detection, localization and risk assessment.

PubMed

Bjurlin, Marc A; Meng, Xiaosong; Le Nobin, Julien; Wysock, James S; Lepor, Herbert; Rosenkrantz, Andrew B; Taneja, Samir S

2014-09-01

Optimization of prostate biopsy requires addressing the shortcomings of standard systematic transrectal ultrasound guided biopsy, including false-negative rates, incorrect risk stratification, detection of clinically insignificant disease and the need for repeat biopsy. Magnetic resonance imaging is an evolving noninvasive imaging modality that increases the accurate localization of prostate cancer at the time of biopsy, and thereby enhances clinical risk assessment and improves the ability to appropriately counsel patients regarding therapy. In this review we 1) summarize the various sequences that comprise a prostate multiparametric magnetic resonance imaging examination along with its performance characteristics in cancer detection, localization and reporting standards; 2) evaluate potential applications of magnetic resonance imaging targeting in prostate biopsy among men with no previous biopsy, a negative previous biopsy and those with low stage cancer; and 3) describe the techniques of magnetic resonance imaging targeted biopsy and comparative study outcomes. A bibliographic search covering the period up to October 2013 was conducted using MEDLINE®/PubMed®. Articles were reviewed and categorized based on which of the 3 objectives of this review was addressed. Data were extracted, analyzed and summarized. Multiparametric magnetic resonance imaging consists of anatomical T2-weighted imaging coupled with at least 2 functional imaging techniques. It has demonstrated improved prostate cancer detection sensitivity up to 80% in the peripheral zone and 81% in the transition zone. A prostate cancer magnetic resonance imaging suspicion score has been developed, and is depicted using the Likert or PI-RADS (Prostate Imaging Reporting and Data System) scale for better standardization of magnetic resonance imaging interpretation and reporting. Among men with no previous biopsy, magnetic resonance imaging increases the frequency of significant cancer detection to 50% in low risk and 71% in high risk patients. In low risk men the negative predictive value of a combination of negative magnetic resonance imaging with prostate volume parameters is nearly 98%, suggesting a potential role in avoiding biopsy and reducing over detection/overtreatment. Among men with a previous negative biopsy 72% to 87% of cancers detected by magnetic resonance imaging guidance are clinically significant. Among men with a known low risk cancer, repeat biopsy using magnetic resonance targeting demonstrates a high likelihood of confirming low risk disease in low suspicion score lesions and of upgrading in high suspicion score lesions. Techniques of magnetic resonance imaging targeted biopsy include visual estimation transrectal ultrasound guided biopsy; software co-registered magnetic resonance imaging-ultrasound, transrectal ultrasound guided biopsy; and in-bore magnetic resonance imaging guided biopsy. Although the improvement in accuracy and efficiency of visual estimation biopsy compared to systematic appears limited, co-registered magnetic resonance imaging-ultrasound biopsy as well as in-bore magnetic resonance imaging guided biopsy appear to increase cancer detection rates in conjunction with increasing suspicion score. Use of magnetic resonance imaging for targeting prostate biopsies has the potential to reduce the sampling error associated with conventional biopsy by providing better disease localization and sampling. More accurate risk stratification through improved cancer sampling may impact therapeutic decision making. Optimal clinical application of magnetic resonance imaging targeted biopsy remains under investigation. Copyright © 2014 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Image-optimized Coronal Magnetic Field Models

NASA Astrophysics Data System (ADS)

Jones, Shaela I.; Uritsky, Vadim; Davila, Joseph M.

2017-08-01

We have reported previously on a new method we are developing for using image-based information to improve global coronal magnetic field models. In that work, we presented early tests of the method, which proved its capability to improve global models based on flawed synoptic magnetograms, given excellent constraints on the field in the model volume. In this follow-up paper, we present the results of similar tests given field constraints of a nature that could realistically be obtained from quality white-light coronagraph images of the lower corona. We pay particular attention to difficulties associated with the line-of-sight projection of features outside of the assumed coronagraph image plane and the effect on the outcome of the optimization of errors in the localization of constraints. We find that substantial improvement in the model field can be achieved with these types of constraints, even when magnetic features in the images are located outside of the image plane.

Image-Optimized Coronal Magnetic Field Models

NASA Technical Reports Server (NTRS)

Jones, Shaela I.; Uritsky, Vadim; Davila, Joseph M.

2017-01-01

We have reported previously on a new method we are developing for using image-based information to improve global coronal magnetic field models. In that work we presented early tests of the method which proved its capability to improve global models based on flawed synoptic magnetograms, given excellent constraints on the field in the model volume. In this follow-up paper we present the results of similar tests given field constraints of a nature that could realistically be obtained from quality white-light coronagraph images of the lower corona. We pay particular attention to difficulties associated with the line-of-sight projection of features outside of the assumed coronagraph image plane, and the effect on the outcome of the optimization of errors in localization of constraints. We find that substantial improvement in the model field can be achieved with this type of constraints, even when magnetic features in the images are located outside of the image plane.

Use of localized performance-based functions for the specification and correction of hybrid imaging systems

NASA Astrophysics Data System (ADS)

Lisson, Jerold B.; Mounts, Darryl I.; Fehniger, Michael J.

1992-08-01

Localized wavefront performance analysis (LWPA) is a system that allows the full utilization of the system optical transfer function (OTF) for the specification and acceptance of hybrid imaging systems. We show that LWPA dictates the correction of wavefront errors with the greatest impact on critical imaging spatial frequencies. This is accomplished by the generation of an imaging performance map-analogous to a map of the optic pupil error-using a local OTF. The resulting performance map a function of transfer function spatial frequency is directly relatable to the primary viewing condition of the end-user. In addition to optimizing quality for the viewer it will be seen that the system has the potential for an improved matching of the optical and electronic bandpass of the imager and for the development of more realistic acceptance specifications. 1. LOCAL WAVEFRONT PERFORMANCE ANALYSIS The LWPA system generates a local optical quality factor (LOQF) in the form of a map analogous to that used for the presentation and evaluation of wavefront errors. In conjunction with the local phase transfer function (LPTF) it can be used for maximally efficient specification and correction of imaging system pupil errors. The LOQF and LPTF are respectively equivalent to the global modulation transfer function (MTF) and phase transfer function (PTF) parts of the OTF. The LPTF is related to difference of the average of the errors in separated regions of the pupil. Figure

Vision function testing for a suprachoroidal retinal prosthesis: effects of image filtering

NASA Astrophysics Data System (ADS)

Barnes, Nick; Scott, Adele F.; Lieby, Paulette; Petoe, Matthew A.; McCarthy, Chris; Stacey, Ashley; Ayton, Lauren N.; Sinclair, Nicholas C.; Shivdasani, Mohit N.; Lovell, Nigel H.; McDermott, Hugh J.; Walker, Janine G.; BVA Consortium,the

2016-06-01

Objective. One strategy to improve the effectiveness of prosthetic vision devices is to process incoming images to ensure that key information can be perceived by the user. This paper presents the first comprehensive results of vision function testing for a suprachoroidal retinal prosthetic device utilizing of 20 stimulating electrodes. Further, we investigate whether using image filtering can improve results on a light localization task for implanted participants compared to minimal vision processing. No controlled implanted participant studies have yet investigated whether vision processing methods that are not task-specific can lead to improved results. Approach. Three participants with profound vision loss from retinitis pigmentosa were implanted with a suprachoroidal retinal prosthesis. All three completed multiple trials of a light localization test, and one participant completed multiple trials of acuity tests. The visual representations used were: Lanczos2 (a high quality Nyquist bandlimited downsampling filter); minimal vision processing (MVP); wide view regional averaging filtering (WV); scrambled; and, system off. Main results. Using Lanczos2, all three participants successfully completed a light localization task and obtained a significantly higher percentage of correct responses than using MVP (p≤slant 0.025) or with system off (p\\lt 0.0001). Further, in a preliminary result using Lanczos2, one participant successfully completed grating acuity and Landolt C tasks, and showed significantly better performance (p=0.004) compared to WV, scrambled and system off on the grating acuity task. Significance. Participants successfully completed vision tasks using a 20 electrode suprachoroidal retinal prosthesis. Vision processing with a Nyquist bandlimited image filter has shown an advantage for a light localization task. This result suggests that this and targeted, more advanced vision processing schemes may become important components of retinal prostheses to enhance performance. ClinicalTrials.gov Identifier: NCT01603576.

Multi-oriented windowed harmonic phase reconstruction for robust cardiac strain imaging.

PubMed

Cordero-Grande, Lucilio; Royuela-del-Val, Javier; Sanz-Estébanez, Santiago; Martín-Fernández, Marcos; Alberola-López, Carlos

2016-04-01

The purpose of this paper is to develop a method for direct estimation of the cardiac strain tensor by extending the harmonic phase reconstruction on tagged magnetic resonance images to obtain more precise and robust measurements. The extension relies on the reconstruction of the local phase of the image by means of the windowed Fourier transform and the acquisition of an overdetermined set of stripe orientations in order to avoid the phase interferences from structures outside the myocardium and the instabilities arising from the application of a gradient operator. Results have shown that increasing the number of acquired orientations provides a significant improvement in the reproducibility of the strain measurements and that the acquisition of an extended set of orientations also improves the reproducibility when compared with acquiring repeated samples from a smaller set of orientations. Additionally, biases in local phase estimation when using the original harmonic phase formulation are greatly diminished by the one here proposed. The ideas here presented allow the design of new methods for motion sensitive magnetic resonance imaging, which could simultaneously improve the resolution, robustness and accuracy of motion estimates. Copyright © 2015 Elsevier B.V. All rights reserved.

Global-local feature attention network with reranking strategy for image caption generation

NASA Astrophysics Data System (ADS)

Wu, Jie; Xie, Si-ya; Shi, Xin-bao; Chen, Yao-wen

2017-11-01

In this paper, a novel framework, named as global-local feature attention network with reranking strategy (GLAN-RS), is presented for image captioning task. Rather than only adopting unitary visual information in the classical models, GLAN-RS explores the attention mechanism to capture local convolutional salient image maps. Furthermore, we adopt reranking strategy to adjust the priority of the candidate captions and select the best one. The proposed model is verified using the Microsoft Common Objects in Context (MSCOCO) benchmark dataset across seven standard evaluation metrics. Experimental results show that GLAN-RS significantly outperforms the state-of-the-art approaches, such as multimodal recurrent neural network (MRNN) and Google NIC, which gets an improvement of 20% in terms of BLEU4 score and 13 points in terms of CIDER score.

Simultaneously Discovering and Localizing Common Objects in Wild Images.

PubMed

Wang, Zhenzhen; Yuan, Junsong

2018-09-01

Motivated by the recent success of supervised and weakly supervised common object discovery, in this paper, we move forward one step further to tackle common object discovery in a fully unsupervised way. Generally, object co-localization aims at simultaneously localizing objects of the same class across a group of images. Traditional object localization/detection usually trains specific object detectors which require bounding box annotations of object instances, or at least image-level labels to indicate the presence/absence of objects in an image. Given a collection of images without any annotations, our proposed fully unsupervised method is to simultaneously discover images that contain common objects and also localize common objects in corresponding images. Without requiring to know the total number of common objects, we formulate this unsupervised object discovery as a sub-graph mining problem from a weighted graph of object proposals, where nodes correspond to object proposals, and edges represent the similarities between neighbouring proposals. The positive images and common objects are jointly discovered by finding sub-graphs of strongly connected nodes, with each sub-graph capturing one object pattern. The optimization problem can be efficiently solved by our proposed maximal-flow-based algorithm. Instead of assuming that each image contains only one common object, our proposed solution can better address wild images where each image may contain multiple common objects or even no common object. Moreover, our proposed method can be easily tailored to the task of image retrieval in which the nodes correspond to the similarity between query and reference images. Extensive experiments on PASCAL VOC 2007 and Object Discovery data sets demonstrate that even without any supervision, our approach can discover/localize common objects of various classes in the presence of scale, view point, appearance variation, and partial occlusions. We also conduct broad experiments on image retrieval benchmarks, Holidays and Oxford5k data sets, to show that our proposed method, which considers both the similarity between query and reference images and also similarities among reference images, can help to improve the retrieval results significantly.

Fast algorithms of constrained Delaunay triangulation and skeletonization for band images

NASA Astrophysics Data System (ADS)

Zeng, Wei; Yang, ChengLei; Meng, XiangXu; Yang, YiJun; Yang, XiuKun

2004-09-01

For the boundary polygons of band-images, a fast constrained Delaunay triangulation algorithm is presented and based on it an efficient skeletonization algorithm is designed. In the process of triangulation the characters of uniform grid structure and the band-polygons are utilized to improve the speed of computing the third vertex for one edge within its local ranges when forming a Delaunay triangle. The final skeleton of the band-image is derived after reducing each triangle to local skeleton lines according to its topology. The algorithm with a simple data structure is easy to understand and implement. Moreover, it can deal with multiply connected polygons on the fly. Experiments show that there is a nearly linear dependence between triangulation time and size of band-polygons randomly generated. Correspondingly, the skeletonization algorithm is also an improvement over the previously known results in terms of time. Some practical examples are given in the paper.

Local adaptive tone mapping for video enhancement

NASA Astrophysics Data System (ADS)

Lachine, Vladimir; Dai, Min (.

2015-03-01

As new technologies like High Dynamic Range cameras, AMOLED and high resolution displays emerge on consumer electronics market, it becomes very important to deliver the best picture quality for mobile devices. Tone Mapping (TM) is a popular technique to enhance visual quality. However, the traditional implementation of Tone Mapping procedure is limited by pixel's value to value mapping, and the performance is restricted in terms of local sharpness and colorfulness. To overcome the drawbacks of traditional TM, we propose a spatial-frequency based framework in this paper. In the proposed solution, intensity component of an input video/image signal is split on low pass filtered (LPF) and high pass filtered (HPF) bands. Tone Mapping (TM) function is applied to LPF band to improve the global contrast/brightness, and HPF band is added back afterwards to keep the local contrast. The HPF band may be adjusted by a coring function to avoid noise boosting and signal overshooting. Colorfulness of an original image may be preserved or enhanced by chroma components correction by means of saturation function. Localized content adaptation is further improved by dividing an image to a set of non-overlapped regions and modifying each region individually. The suggested framework allows users to implement a wide range of tone mapping applications with perceptional local sharpness and colorfulness preserved or enhanced. Corresponding hardware circuit may be integrated in camera, video or display pipeline with minimal hardware budget

The algorithm of fast image stitching based on multi-feature extraction

NASA Astrophysics Data System (ADS)

Yang, Chunde; Wu, Ge; Shi, Jing

2018-05-01

This paper proposed an improved image registration method combining Hu-based invariant moment contour information and feature points detection, aiming to solve the problems in traditional image stitching algorithm, such as time-consuming feature points extraction process, redundant invalid information overload and inefficiency. First, use the neighborhood of pixels to extract the contour information, employing the Hu invariant moment as similarity measure to extract SIFT feature points in those similar regions. Then replace the Euclidean distance with Hellinger kernel function to improve the initial matching efficiency and get less mismatching points, further, estimate affine transformation matrix between the images. Finally, local color mapping method is adopted to solve uneven exposure, using the improved multiresolution fusion algorithm to fuse the mosaic images and realize seamless stitching. Experimental results confirm high accuracy and efficiency of method proposed in this paper.

Improving Large-Scale Image Retrieval Through Robust Aggregation of Local Descriptors.

PubMed

Husain, Syed Sameed; Bober, Miroslaw

2017-09-01

Visual search and image retrieval underpin numerous applications, however the task is still challenging predominantly due to the variability of object appearance and ever increasing size of the databases, often exceeding billions of images. Prior art methods rely on aggregation of local scale-invariant descriptors, such as SIFT, via mechanisms including Bag of Visual Words (BoW), Vector of Locally Aggregated Descriptors (VLAD) and Fisher Vectors (FV). However, their performance is still short of what is required. This paper presents a novel method for deriving a compact and distinctive representation of image content called Robust Visual Descriptor with Whitening (RVD-W). It significantly advances the state of the art and delivers world-class performance. In our approach local descriptors are rank-assigned to multiple clusters. Residual vectors are then computed in each cluster, normalized using a direction-preserving normalization function and aggregated based on the neighborhood rank. Importantly, the residual vectors are de-correlated and whitened in each cluster before aggregation, leading to a balanced energy distribution in each dimension and significantly improved performance. We also propose a new post-PCA normalization approach which improves separability between the matching and non-matching global descriptors. This new normalization benefits not only our RVD-W descriptor but also improves existing approaches based on FV and VLAD aggregation. Furthermore, we show that the aggregation framework developed using hand-crafted SIFT features also performs exceptionally well with Convolutional Neural Network (CNN) based features. The RVD-W pipeline outperforms state-of-the-art global descriptors on both the Holidays and Oxford datasets. On the large scale datasets, Holidays1M and Oxford1M, SIFT-based RVD-W representation obtains a mAP of 45.1 and 35.1 percent, while CNN-based RVD-W achieve a mAP of 63.5 and 44.8 percent, all yielding superior performance to the state-of-the-art.

An Improved Otsu Threshold Segmentation Method for Underwater Simultaneous Localization and Mapping-Based Navigation

PubMed Central

Yuan, Xin; Martínez, José-Fernán; Eckert, Martina; López-Santidrián, Lourdes

2016-01-01

The main focus of this paper is on extracting features with SOund Navigation And Ranging (SONAR) sensing for further underwater landmark-based Simultaneous Localization and Mapping (SLAM). According to the characteristics of sonar images, in this paper, an improved Otsu threshold segmentation method (TSM) has been developed for feature detection. In combination with a contour detection algorithm, the foreground objects, although presenting different feature shapes, are separated much faster and more precisely than by other segmentation methods. Tests have been made with side-scan sonar (SSS) and forward-looking sonar (FLS) images in comparison with other four TSMs, namely the traditional Otsu method, the local TSM, the iterative TSM and the maximum entropy TSM. For all the sonar images presented in this work, the computational time of the improved Otsu TSM is much lower than that of the maximum entropy TSM, which achieves the highest segmentation precision among the four above mentioned TSMs. As a result of the segmentations, the centroids of the main extracted regions have been computed to represent point landmarks which can be used for navigation, e.g., with the help of an Augmented Extended Kalman Filter (AEKF)-based SLAM algorithm. The AEKF-SLAM approach is a recursive and iterative estimation-update process, which besides a prediction and an update stage (as in classical Extended Kalman Filter (EKF)), includes an augmentation stage. During navigation, the robot localizes the centroids of different segments of features in sonar images, which are detected by our improved Otsu TSM, as point landmarks. Using them with the AEKF achieves more accurate and robust estimations of the robot pose and the landmark positions, than with those detected by the maximum entropy TSM. Together with the landmarks identified by the proposed segmentation algorithm, the AEKF-SLAM has achieved reliable detection of cycles in the map and consistent map update on loop closure, which is shown in simulated experiments. PMID:27455279

An Improved Otsu Threshold Segmentation Method for Underwater Simultaneous Localization and Mapping-Based Navigation.

PubMed

Yuan, Xin; Martínez, José-Fernán; Eckert, Martina; López-Santidrián, Lourdes

2016-07-22

The main focus of this paper is on extracting features with SOund Navigation And Ranging (SONAR) sensing for further underwater landmark-based Simultaneous Localization and Mapping (SLAM). According to the characteristics of sonar images, in this paper, an improved Otsu threshold segmentation method (TSM) has been developed for feature detection. In combination with a contour detection algorithm, the foreground objects, although presenting different feature shapes, are separated much faster and more precisely than by other segmentation methods. Tests have been made with side-scan sonar (SSS) and forward-looking sonar (FLS) images in comparison with other four TSMs, namely the traditional Otsu method, the local TSM, the iterative TSM and the maximum entropy TSM. For all the sonar images presented in this work, the computational time of the improved Otsu TSM is much lower than that of the maximum entropy TSM, which achieves the highest segmentation precision among the four above mentioned TSMs. As a result of the segmentations, the centroids of the main extracted regions have been computed to represent point landmarks which can be used for navigation, e.g., with the help of an Augmented Extended Kalman Filter (AEKF)-based SLAM algorithm. The AEKF-SLAM approach is a recursive and iterative estimation-update process, which besides a prediction and an update stage (as in classical Extended Kalman Filter (EKF)), includes an augmentation stage. During navigation, the robot localizes the centroids of different segments of features in sonar images, which are detected by our improved Otsu TSM, as point landmarks. Using them with the AEKF achieves more accurate and robust estimations of the robot pose and the landmark positions, than with those detected by the maximum entropy TSM. Together with the landmarks identified by the proposed segmentation algorithm, the AEKF-SLAM has achieved reliable detection of cycles in the map and consistent map update on loop closure, which is shown in simulated experiments.

Fusion of Computed Tomography and PROPELLER Diffusion-Weighted Magnetic Resonance Imaging for the Detection and Localization of Middle Ear Cholesteatoma.

PubMed

Locketz, Garrett D; Li, Peter M M C; Fischbein, Nancy J; Holdsworth, Samantha J; Blevins, Nikolas H

2016-10-01

A method to optimize imaging of cholesteatoma by combining the strengths of available modalities will improve diagnostic accuracy and help to target treatment. To assess whether fusing Periodically Rotated Overlapping Parallel Lines With Enhanced Reconstruction (PROPELLER) diffusion-weighted magnetic resonance imaging (DW-MRI) with corresponding temporal bone computed tomography (CT) images could increase cholesteatoma diagnostic and localization accuracy across 6 distinct anatomical regions of the temporal bone. Case series and preliminary technology evaluation of adults with preoperative temporal bone CT and PROPELLER DW-MRI scans who underwent surgery for clinically suggested cholesteatoma at a tertiary academic hospital. When cholesteatoma was encountered surgically, the precise location was recorded in a diagram of the middle ear and mastoid. For each patient, the 3 image data sets (CT, PROPELLER DW-MRI, and CT-MRI fusion) were reviewed in random order for the presence or absence of cholesteatoma by an investigator blinded to operative findings. If cholesteatoma was deemed present on review of each imaging modality, the location of the lesion was mapped presumptively. Image analysis was then compared with surgical findings. Twelve adults (5 women and 7 men; median [range] age, 45.5 [19-77] years) were included. The use of CT-MRI fusion had greater diagnostic sensitivity (0.88 vs 0.75), positive predictive value (0.88 vs 0.86), and negative predictive value (0.75 vs 0.60) than PROPELLER DW-MRI alone. Image fusion also showed increased overall localization accuracy when stratified across 6 distinct anatomical regions of the temporal bone (localization sensitivity and specificity, 0.76 and 0.98 for CT-MRI fusion vs 0.58 and 0.98 for PROPELLER DW-MRI). For PROPELLER DW-MRI, there were 15 true-positive, 45 true-negative, 1 false-positive, and 11 false-negative results; overall accuracy was 0.83. For CT-MRI fusion, there were 20 true-positive, 45 true-negative, 1 false-positive, and 6 false-negative results; overall accuracy was 0.90. The poor anatomical spatial resolution of DW-MRI makes precise localization of cholesteatoma within the middle ear and mastoid a diagnostic challenge. This study suggests that the bony anatomic detail obtained via CT coupled with the excellent sensitivity and specificity of PROPELLER DW-MRI for cholesteatoma can improve both preoperative identification and localization of disease over DW-MRI alone.

Accurate Iris Recognition at a Distance Using Stabilized Iris Encoding and Zernike Moments Phase Features.

PubMed

Tan, Chun-Wei; Kumar, Ajay

2014-07-10

Accurate iris recognition from the distantly acquired face or eye images requires development of effective strategies which can account for significant variations in the segmented iris image quality. Such variations can be highly correlated with the consistency of encoded iris features and the knowledge that such fragile bits can be exploited to improve matching accuracy. A non-linear approach to simultaneously account for both local consistency of iris bit and also the overall quality of the weight map is proposed. Our approach therefore more effectively penalizes the fragile bits while simultaneously rewarding more consistent bits. In order to achieve more stable characterization of local iris features, a Zernike moment-based phase encoding of iris features is proposed. Such Zernike moments-based phase features are computed from the partially overlapping regions to more effectively accommodate local pixel region variations in the normalized iris images. A joint strategy is adopted to simultaneously extract and combine both the global and localized iris features. The superiority of the proposed iris matching strategy is ascertained by providing comparison with several state-of-the-art iris matching algorithms on three publicly available databases: UBIRIS.v2, FRGC, CASIA.v4-distance. Our experimental results suggest that proposed strategy can achieve significant improvement in iris matching accuracy over those competing approaches in the literature, i.e., average improvement of 54.3%, 32.7% and 42.6% in equal error rates, respectively for UBIRIS.v2, FRGC, CASIA.v4-distance.

A new phase-correlation-based iris matching for degraded images.

PubMed

Krichen, Emine; Garcia-Salicetti, Sonia; Dorizzi, Bernadette

2009-08-01

In this paper, we present a new phase-correlation-based iris matching approach in order to deal with degradations in iris images due to unconstrained acquisition procedures. Our matching system is a fusion of global and local Gabor phase-correlation schemes. The main originality of our local approach is that we do not only consider the correlation peak amplitudes but also their locations in different regions of the images. Results on several degraded databases, namely, the CASIA-BIOSECURE and Iris Challenge Evaluation 2005 databases, show the improvement of our method compared to two available reference systems, Masek and Open Source for Iris (OSRIS), in verification mode.

Change detection of medical images using dictionary learning techniques and principal component analysis.

PubMed

Nika, Varvara; Babyn, Paul; Zhu, Hongmei

2014-07-01

Automatic change detection methods for identifying the changes of serial MR images taken at different times are of great interest to radiologists. The majority of existing change detection methods in medical imaging, and those of brain images in particular, include many preprocessing steps and rely mostly on statistical analysis of magnetic resonance imaging (MRI) scans. Although most methods utilize registration software, tissue classification remains a difficult and overwhelming task. Recently, dictionary learning techniques are being used in many areas of image processing, such as image surveillance, face recognition, remote sensing, and medical imaging. We present an improved version of the EigenBlockCD algorithm, named the EigenBlockCD-2. The EigenBlockCD-2 algorithm performs an initial global registration and identifies the changes between serial MR images of the brain. Blocks of pixels from a baseline scan are used to train local dictionaries to detect changes in the follow-up scan. We use PCA to reduce the dimensionality of the local dictionaries and the redundancy of data. Choosing the appropriate distance measure significantly affects the performance of our algorithm. We examine the differences between [Formula: see text] and [Formula: see text] norms as two possible similarity measures in the improved EigenBlockCD-2 algorithm. We show the advantages of the [Formula: see text] norm over the [Formula: see text] norm both theoretically and numerically. We also demonstrate the performance of the new EigenBlockCD-2 algorithm for detecting changes of MR images and compare our results with those provided in the recent literature. Experimental results with both simulated and real MRI scans show that our improved EigenBlockCD-2 algorithm outperforms the previous methods. It detects clinical changes while ignoring the changes due to the patient's position and other acquisition artifacts.

Iterative normalization method for improved prostate cancer localization with multispectral magnetic resonance imaging

NASA Astrophysics Data System (ADS)

Liu, Xin; Samil Yetik, Imam

2012-04-01

Use of multispectral magnetic resonance imaging has received a great interest for prostate cancer localization in research and clinical studies. Manual extraction of prostate tumors from multispectral magnetic resonance imaging is inefficient and subjective, while automated segmentation is objective and reproducible. For supervised, automated segmentation approaches, learning is essential to obtain the information from training dataset. However, in this procedure, all patients are assumed to have similar properties for the tumor and normal tissues, and the segmentation performance suffers since the variations across patients are ignored. To conquer this difficulty, we propose a new iterative normalization method based on relative intensity values of tumor and normal tissues to normalize multispectral magnetic resonance images and improve segmentation performance. The idea of relative intensity mimics the manual segmentation performed by human readers, who compare the contrast between regions without knowing the actual intensity values. We compare the segmentation performance of the proposed method with that of z-score normalization followed by support vector machine, local active contours, and fuzzy Markov random field. Our experimental results demonstrate that our method outperforms the three other state-of-the-art algorithms, and was found to have specificity of 0.73, sensitivity of 0.69, and accuracy of 0.79, significantly better than alternative methods.

A liver cirrhosis classification on B-mode ultrasound images by the use of higher order local autocorrelation features

NASA Astrophysics Data System (ADS)

Sasaki, Kenya; Mitani, Yoshihiro; Fujita, Yusuke; Hamamoto, Yoshihiko; Sakaida, Isao

2017-02-01

In this paper, in order to classify liver cirrhosis on regions of interest (ROIs) images from B-mode ultrasound images, we have proposed to use the higher order local autocorrelation (HLAC) features. In a previous study, we tried to classify liver cirrhosis by using a Gabor filter based approach. However, the classification performance of the Gabor feature was poor from our preliminary experimental results. In order accurately to classify liver cirrhosis, we examined to use the HLAC features for liver cirrhosis classification. The experimental results show the effectiveness of HLAC features compared with the Gabor feature. Furthermore, by using a binary image made by an adaptive thresholding method, the classification performance of HLAC features has improved.

Mobile, hybrid Compton/coded aperture imaging for detection, identification and localization of gamma-ray sources at stand-off distances

NASA Astrophysics Data System (ADS)

Tornga, Shawn R.

The Stand-off Radiation Detection System (SORDS) program is an Advanced Technology Demonstration (ATD) project through the Department of Homeland Security's Domestic Nuclear Detection Office (DNDO) with the goal of detection, identification and localization of weak radiological sources in the presence of large dynamic backgrounds. The Raytheon-SORDS Tri-Modal Imager (TMI) is a mobile truck-based, hybrid gamma-ray imaging system able to quickly detect, identify and localize, radiation sources at standoff distances through improved sensitivity while minimizing the false alarm rate. Reconstruction of gamma-ray sources is performed using a combination of two imaging modalities; coded aperture and Compton scatter imaging. The TMI consists of 35 sodium iodide (NaI) crystals 5x5x2 in3 each, arranged in a random coded aperture mask array (CA), followed by 30 position sensitive NaI bars each 24x2.5x3 in3 called the detection array (DA). The CA array acts as both a coded aperture mask and scattering detector for Compton events. The large-area DA array acts as a collection detector for both Compton scattered events and coded aperture events. In this thesis, developed coded aperture, Compton and hybrid imaging algorithms will be described along with their performance. It will be shown that multiple imaging modalities can be fused to improve detection sensitivity over a broader energy range than either alone. Since the TMI is a moving system, peripheral data, such as a Global Positioning System (GPS) and Inertial Navigation System (INS) must also be incorporated. A method of adapting static imaging algorithms to a moving platform has been developed. Also, algorithms were developed in parallel with detector hardware, through the use of extensive simulations performed with the Geometry and Tracking Toolkit v4 (GEANT4). Simulations have been well validated against measured data. Results of image reconstruction algorithms at various speeds and distances will be presented as well as localization capability. Utilizing imaging information will show signal-to-noise gains over spectroscopic algorithms alone.

Sensitivity enhancement of traveling wave MRI using free local resonators: an experimental demonstration.

PubMed

Zhang, Xiaoliang

2017-04-01

Traveling wave MR uses the far fields in signal excitation and reception, therefore its acquisition efficiency is low in contrast to the conventional near field magnetic resonance (MR). Here we show a simple and efficient method based on the local resonator to improving sensitivity of traveling wave MR technique. The proposed method utilizes a standalone or free local resonator to amplify the radio frequency magnetic fields in the interested target. The resonators have no wire connections to the MR system and thus can be conveniently placed to any place around imaging simples. A rectangular loop L/C resonator to be used as the free local resonator was tuned to the proton Larmor frequency at 7T. Traveling wave MR experiments with and without the wireless free local resonator were performed on a living rat using a 7T whole body MR scanner. The signal-to-noise ratio (SNR) or sensitivity of the images acquired was compared and evaluated. In vivo 7T imaging results show that traveling wave MR with a wireless free local resonator placed near the head of a living rat achieves at least 10-fold SNR gain over the images acquired on the same rat using conventional traveling wave MR method, i.e. imaging with no free local resonators. The proposed free local resonator technique is able to enhance the MR sensitivity and acquisition efficiency of traveling wave MR at ultrahigh fields in vivo . This method can be a simple solution to alleviating low sensitivity problem of traveling wave MRI.

SU-C-202-07: Protocol and Hardware for Improved Flood Field Calibration of TrueBeam FFF Cine Imaging

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

Adamson, J; Faught, A; Yin, F

2016-06-15

Purpose: Flattening filter free photon energies are commonly used for high dose treatments such as SBRT, where localization accuracy is essential. Often, MV cine imaging may be employed to verify correct localization. TrueBeam Electronic Portal Imaging Devices (EPIDs) equipped with the 40×30cm{sup 2} Image Detection Unit (IDU) are prone to image saturation at the image center especially for higher dose rates. While saturation often does not occur for cine imaging during treatment because the beam is attenuated by the patient, the flood field calibration is affected when the standard calibration procedure is followed. Here we describe the hardware and protocolmore » to achieve improved image quality for this model of TrueBeam EPID. Methods: A stainless steel filter of uniform thickness was designed to have sufficient attenuation to avoid panel saturation for both 6XFFF and 10XFFF at the maximum dose rates (1400 MU/min & 2400 MU/min, respectively). The cine imaging flood field calibration was then acquired with the filter in place for the FFF energies under the standard calibration geometry (SDD=150cm). Image quality during MV cine was assessed with & without the modified flood field calibration using a low contrast resolution phantom and an anthropomorphic phantom. Results: When the flood field is acquired using the standard procedure (no filter in place), a pixel gain artifact is clearly present in the image center (r=3cm for 10XFFF at 2400 MU/min) which appears similar to and may be mis-attributed to panel saturation in the subject image. The artifact obscured all low contrast inserts at the image center and was also visible on the anthropomorphic phantom. Using the filter for flood field calibration eliminated the artifact. Conclusion: Use of a modified flood field calibration procedure improves image quality for cine MV imaging with TrueBeams equipped with the 40×30cm{sup 2} IDU.« less

Advances in local ablation of malignant liver lesions

PubMed Central

Eisele, Robert M

2016-01-01

Local ablation of liver tumors matured during the recent years and is now proven to be an effective tool in the treatment of malignant liver lesions. Advances focus on the improvement of local tumor control by technical innovations, individual selection of imaging modalities, more accurate needle placement and the free choice of access to the liver. Considering data found in the current literature for conventional local ablative treatment strategies, virtually no single technology is able to demonstrate an unequivocal superiority. Hints at better performance of microwave compared to radiofrequency ablation regarding local tumor control, duration of the procedure and potentially achievable larger size of ablation areas favour the comparably more recent treatment modality; image fusion enables more patients to undergo ultrasound guided local ablation; magnetic resonance guidance may improve primary success rates in selected patients; navigation and robotics accelerate the needle placement and reduces deviation of needle positions; laparoscopic thermoablation results in larger ablation areas and therefore hypothetically better local tumor control under acceptable complication rates, but seems to be limited to patients with no, mild or moderate adhesions following earlier surgical procedures. Apart from that, most techniques appear technically feasible, albeit demanding. Which technology will in the long run become accepted, is subject to future work. PMID:27099433

Localization and spectral isolation of special nuclear material using stochastic image reconstruction

NASA Astrophysics Data System (ADS)

Hamel, M. C.; Polack, J. K.; Poitrasson-Rivière, A.; Clarke, S. D.; Pozzi, S. A.

2017-01-01

In this work we present a technique for isolating the gamma-ray and neutron energy spectra from multiple radioactive sources localized in an image. Image reconstruction algorithms for radiation scatter cameras typically focus on improving image quality. However, with scatter cameras being developed for non-proliferation applications, there is a need for not only source localization but also source identification. This work outlines a modified stochastic origin ensembles algorithm that provides localized spectra for all pixels in the image. We demonstrated the technique by performing three experiments with a dual-particle imager that measured various gamma-ray and neutron sources simultaneously. We showed that we could isolate the peaks from 22Na and 137Cs and that the energy resolution is maintained in the isolated spectra. To evaluate the spectral isolation of neutrons, a 252Cf source and a PuBe source were measured simultaneously and the reconstruction showed that the isolated PuBe spectrum had a higher average energy and a greater fraction of neutrons at higher energies than the 252Cf. Finally, spectrum isolation was used for an experiment with weapons grade plutonium, 252Cf, and AmBe. The resulting neutron and gamma-ray spectra showed the expected characteristics that could then be used to identify the sources.

Imaging of the meninges and the extra-axial spaces.

PubMed

Kirmi, Olga; Sheerin, Fintan; Patel, Neel

2009-12-01

The separate meningeal layers and extraaxial spaces are complex and can only be differentiated by pathologic processes on imaging. Differentiation of the location of such processes can be achieved using different imaging modalities. In this pictorial review we address the imaging techniques, enhancement and location patterns, and disease spread that will promote accurate localization of the pathology, thus improving accuracy of diagnosis. Typical and unusual magnetic resonance (MR), computed tomography (CT), and ultrasound imaging findings of many conditions affecting these layers and spaces are described.

Robust finger vein ROI localization based on flexible segmentation.

PubMed

Lu, Yu; Xie, Shan Juan; Yoon, Sook; Yang, Jucheng; Park, Dong Sun

2013-10-24

Finger veins have been proved to be an effective biometric for personal identification in the recent years. However, finger vein images are easily affected by influences such as image translation, orientation, scale, scattering, finger structure, complicated background, uneven illumination, and collection posture. All these factors may contribute to inaccurate region of interest (ROI) definition, and so degrade the performance of finger vein identification system. To improve this problem, in this paper, we propose a finger vein ROI localization method that has high effectiveness and robustness against the above factors. The proposed method consists of a set of steps to localize ROIs accurately, namely segmentation, orientation correction, and ROI detection. Accurate finger region segmentation and correct calculated orientation can support each other to produce higher accuracy in localizing ROIs. Extensive experiments have been performed on the finger vein image database, MMCBNU_6000, to verify the robustness of the proposed method. The proposed method shows the segmentation accuracy of 100%. Furthermore, the average processing time of the proposed method is 22 ms for an acquired image, which satisfies the criterion of a real-time finger vein identification system.

Robust Finger Vein ROI Localization Based on Flexible Segmentation

PubMed Central

Lu, Yu; Xie, Shan Juan; Yoon, Sook; Yang, Jucheng; Park, Dong Sun

2013-01-01

Finger veins have been proved to be an effective biometric for personal identification in the recent years. However, finger vein images are easily affected by influences such as image translation, orientation, scale, scattering, finger structure, complicated background, uneven illumination, and collection posture. All these factors may contribute to inaccurate region of interest (ROI) definition, and so degrade the performance of finger vein identification system. To improve this problem, in this paper, we propose a finger vein ROI localization method that has high effectiveness and robustness against the above factors. The proposed method consists of a set of steps to localize ROIs accurately, namely segmentation, orientation correction, and ROI detection. Accurate finger region segmentation and correct calculated orientation can support each other to produce higher accuracy in localizing ROIs. Extensive experiments have been performed on the finger vein image database, MMCBNU_6000, to verify the robustness of the proposed method. The proposed method shows the segmentation accuracy of 100%. Furthermore, the average processing time of the proposed method is 22 ms for an acquired image, which satisfies the criterion of a real-time finger vein identification system. PMID:24284769

A novel fusion framework of visible light and infrared images based on singular value decomposition and adaptive DUAL-PCNN in NSST domain

NASA Astrophysics Data System (ADS)

Cheng, Boyang; Jin, Longxu; Li, Guoning

2018-06-01

Visible light and infrared images fusion has been a significant subject in imaging science. As a new contribution to this field, a novel fusion framework of visible light and infrared images based on adaptive dual-channel unit-linking pulse coupled neural networks with singular value decomposition (ADS-PCNN) in non-subsampled shearlet transform (NSST) domain is present in this paper. First, the source images are decomposed into multi-direction and multi-scale sub-images by NSST. Furthermore, an improved novel sum modified-Laplacian (INSML) of low-pass sub-image and an improved average gradient (IAVG) of high-pass sub-images are input to stimulate the ADS-PCNN, respectively. To address the large spectral difference between infrared and visible light and the occurrence of black artifacts in fused images, a local structure information operator (LSI), which comes from local area singular value decomposition in each source image, is regarded as the adaptive linking strength that enhances fusion accuracy. Compared with PCNN models in other studies, the proposed method simplifies certain peripheral parameters, and the time matrix is utilized to decide the iteration number adaptively. A series of images from diverse scenes are used for fusion experiments and the fusion results are evaluated subjectively and objectively. The results of the subjective and objective evaluation show that our algorithm exhibits superior fusion performance and is more effective than the existing typical fusion techniques.

The method for detecting small lesions in medical image based on sliding window

NASA Astrophysics Data System (ADS)

Han, Guilai; Jiao, Yuan

2016-10-01

At present, the research on computer-aided diagnosis includes the sample image segmentation, extracting visual features, generating the classification model by learning, and according to the model generated to classify and judge the inspected images. However, this method has a large scale of calculation and speed is slow. And because medical images are usually low contrast, when the traditional image segmentation method is applied to the medical image, there is a complete failure. As soon as possible to find the region of interest, improve detection speed, this topic attempts to introduce the current popular visual attention model into small lesions detection. However, Itti model is mainly for natural images. But the effect is not ideal when it is used to medical images which usually are gray images. Especially in the early stages of some cancers, the focus of a disease in the whole image is not the most significant region and sometimes is very difficult to be found. But these lesions are prominent in the local areas. This paper proposes a visual attention mechanism based on sliding window, and use sliding window to calculate the significance of a local area. Combined with the characteristics of the lesion, select the features of gray, entropy, corner and edge to generate a saliency map. Then the significant region is segmented and distinguished. This method reduces the difficulty of image segmentation, and improves the detection accuracy of small lesions, and it has great significance to early discovery, early diagnosis and treatment of cancers.

Image fusion method based on regional feature and improved bidimensional empirical mode decomposition

NASA Astrophysics Data System (ADS)

Qin, Xinqiang; Hu, Gang; Hu, Kai

2018-01-01

The decomposition of multiple source images using bidimensional empirical mode decomposition (BEMD) often produces mismatched bidimensional intrinsic mode functions, either by their number or their frequency, making image fusion difficult. A solution to this problem is proposed using a fixed number of iterations and a union operation in the sifting process. By combining the local regional features of the images, an image fusion method has been developed. First, the source images are decomposed using the proposed BEMD to produce the first intrinsic mode function (IMF) and residue component. Second, for the IMF component, a selection and weighted average strategy based on local area energy is used to obtain a high-frequency fusion component. Third, for the residue component, a selection and weighted average strategy based on local average gray difference is used to obtain a low-frequency fusion component. Finally, the fused image is obtained by applying the inverse BEMD transform. Experimental results show that the proposed algorithm provides superior performance over methods based on wavelet transform, line and column-based EMD, and complex empirical mode decomposition, both in terms of visual quality and objective evaluation criteria.

Signal-to-noise ratio comparison of encoding methods for hyperpolarized noble gas MRI

NASA Technical Reports Server (NTRS)

Zhao, L.; Venkatesh, A. K.; Albert, M. S.; Panych, L. P.

2001-01-01

Some non-Fourier encoding methods such as wavelet and direct encoding use spatially localized bases. The spatial localization feature of these methods enables optimized encoding for improved spatial and temporal resolution during dynamically adaptive MR imaging. These spatially localized bases, however, have inherently reduced image signal-to-noise ratio compared with Fourier or Hadamad encoding for proton imaging. Hyperpolarized noble gases, on the other hand, have quite different MR properties compared to proton, primarily the nonrenewability of the signal. It could be expected, therefore, that the characteristics of image SNR with respect to encoding method will also be very different from hyperpolarized noble gas MRI compared to proton MRI. In this article, hyperpolarized noble gas image SNRs of different encoding methods are compared theoretically using a matrix description of the encoding process. It is shown that image SNR for hyperpolarized noble gas imaging is maximized for any orthonormal encoding method. Methods are then proposed for designing RF pulses to achieve normalized encoding profiles using Fourier, Hadamard, wavelet, and direct encoding methods for hyperpolarized noble gases. Theoretical results are confirmed with hyperpolarized noble gas MRI experiments. Copyright 2001 Academic Press.

Combination of surface and borehole seismic data for robust target-oriented imaging

NASA Astrophysics Data System (ADS)

Liu, Yi; van der Neut, Joost; Arntsen, Børge; Wapenaar, Kees

2016-05-01

A novel application of seismic interferometry (SI) and Marchenko imaging using both surface and borehole data is presented. A series of redatuming schemes is proposed to combine both data sets for robust deep local imaging in the presence of velocity uncertainties. The redatuming schemes create a virtual acquisition geometry where both sources and receivers lie at the horizontal borehole level, thus only a local velocity model near the borehole is needed for imaging, and erroneous velocities in the shallow area have no effect on imaging around the borehole level. By joining the advantages of SI and Marchenko imaging, a macrovelocity model is no longer required and the proposed schemes use only single-component data. Furthermore, the schemes result in a set of virtual data that have fewer spurious events and internal multiples than previous virtual source redatuming methods. Two numerical examples are shown to illustrate the workflow and to demonstrate the benefits of the method. One is a synthetic model and the other is a realistic model of a field in the North Sea. In both tests, improved local images near the boreholes are obtained using the redatumed data without accurate velocities, because the redatumed data are close to the target.

An improved algorithm of mask image dodging for aerial image

NASA Astrophysics Data System (ADS)

Zhang, Zuxun; Zou, Songbai; Zuo, Zhiqi

2011-12-01

The technology of Mask image dodging based on Fourier transform is a good algorithm in removing the uneven luminance within a single image. At present, the difference method and the ratio method are the methods in common use, but they both have their own defects .For example, the difference method can keep the brightness uniformity of the whole image, but it is deficient in local contrast; meanwhile the ratio method can work better in local contrast, but sometimes it makes the dark areas of the original image too bright. In order to remove the defects of the two methods effectively, this paper on the basis of research of the two methods proposes a balance solution. Experiments show that the scheme not only can combine the advantages of the difference method and the ratio method, but also can avoid the deficiencies of the two algorithms.

Observation of a cavitation cloud in tissue using correlation between ultrafast ultrasound images.

PubMed

Prieur, Fabrice; Zorgani, Ali; Catheline, Stefan; Souchon, Rémi; Mestas, Jean-Louis; Lafond, Maxime; Lafon, Cyril

2015-07-01

The local application of ultrasound is known to improve drug intake by tumors. Cavitating bubbles are one of the contributing effects. A setup in which two ultrasound transducers are placed confocally is used to generate cavitation in ex vivo tissue. As the transducers emit a series of short excitation bursts, the evolution of the cavitation activity is monitored using an ultrafast ultrasound imaging system. The frame rate of the system is several thousands of images per second, which provides several tens of images between consecutive excitation bursts. Using the correlation between consecutive images for speckle tracking, a decorrelation of the imaging signal appears due to the creation, fast movement, and dissolution of the bubbles in the cavitation cloud. By analyzing this area of decorrelation, the cavitation cloud can be localized and the spatial extent of the cavitation activity characterized.

Complete surgical resection improves outcome in INRG high-risk patients with localized neuroblastoma older than 18 months.

PubMed

Fischer, Janina; Pohl, Alexandra; Volland, Ruth; Hero, Barbara; Dübbers, Martin; Cernaianu, Grigore; Berthold, Frank; von Schweinitz, Dietrich; Simon, Thorsten

2017-08-04

Although several studies have been conducted on the role of surgery in localized neuroblastoma, the impact of surgical timing and extent of primary tumor resection on outcome in high-risk patients remains controversial. Patients from the German neuroblastoma trial NB97 with localized neuroblastoma INSS stage 1-3 age > 18 months were included for retrospective analysis. Imaging reports were reviewed by two independent physicians for Image Defined Risk Factors (IDRF). Operation notes and corresponding imaging reports were analyzed for surgical radicality. The extent of tumor resection was classified as complete resection (95-100%), gross total resection (90-95%), incomplete resection (50-90%), and biopsy (<50%) and correlated with local control rate and outcome. Patients were stratified according to the International Neuroblastoma Risk Group (INRG) staging system. Survival curves were estimated according to the method of Kaplan and Meier and compared by the log-rank test. A total of 179 patients were included in this study. 77 patients underwent more than one primary tumor operation. After best surgery, 68.7% of patients achieved complete resection of the primary tumor, 16.8% gross total resection, 14.0% incomplete surgery, and 0.5% biopsy only. The cumulative complication rate was 20.3% and the surgery associated mortality rate was 1.1%. Image defined risk factors (IDRF) predicted the extent of resection. Patients with complete resection had a better local-progression-free survival (LPFS), event-free survival (EFS) and OS (overall survival) than the other groups. Subgroup analyses showed better EFS, LPFS and OS for patients with complete resection in INRG high-risk patients. Multivariable analyses revealed resection (complete vs. other), and MYCN (non-amplified vs. amplified) as independent prognostic factors for EFS, LPFS and OS. In patients with localized neuroblastoma age 18 months or older, especially in INRG high-risk patients harboring MYCN amplification, extended surgery of the primary tumor site improved local control rate and survival with an acceptable risk of complications.

Real-time automatic registration in optical surgical navigation

NASA Astrophysics Data System (ADS)

Lin, Qinyong; Yang, Rongqian; Cai, Ken; Si, Xuan; Chen, Xiuwen; Wu, Xiaoming

2016-05-01

An image-guided surgical navigation system requires the improvement of the patient-to-image registration time to enhance the convenience of the registration procedure. A critical step in achieving this aim is performing a fully automatic patient-to-image registration. This study reports on a design of custom fiducial markers and the performance of a real-time automatic patient-to-image registration method using these markers on the basis of an optical tracking system for rigid anatomy. The custom fiducial markers are designed to be automatically localized in both patient and image spaces. An automatic localization method is performed by registering a point cloud sampled from the three dimensional (3D) pedestal model surface of a fiducial marker to each pedestal of fiducial markers searched in image space. A head phantom is constructed to estimate the performance of the real-time automatic registration method under four fiducial configurations. The head phantom experimental results demonstrate that the real-time automatic registration method is more convenient, rapid, and accurate than the manual method. The time required for each registration is approximately 0.1 s. The automatic localization method precisely localizes the fiducial markers in image space. The averaged target registration error for the four configurations is approximately 0.7 mm. The automatic registration performance is independent of the positions relative to the tracking system and the movement of the patient during the operation.

Geometric approach to segmentation and protein localization in cell culture assays.

PubMed

Raman, S; Maxwell, C A; Barcellos-Hoff, M H; Parvin, B

2007-01-01

Cell-based fluorescence imaging assays are heterogeneous and require the collection of a large number of images for detailed quantitative analysis. Complexities arise as a result of variation in spatial nonuniformity, shape, overlapping compartments and scale (size). A new technique and methodology has been developed and tested for delineating subcellular morphology and partitioning overlapping compartments at multiple scales. This system is packaged as an integrated software platform for quantifying images that are obtained through fluorescence microscopy. Proposed methods are model based, leveraging geometric shape properties of subcellular compartments and corresponding protein localization. From the morphological perspective, convexity constraint is imposed to delineate and partition nuclear compartments. From the protein localization perspective, radial symmetry is imposed to localize punctate protein events at submicron resolution. Convexity constraint is imposed against boundary information, which are extracted through a combination of zero-crossing and gradient operator. If the convexity constraint fails for the boundary then positive curvature maxima are localized along the contour and the entire blob is partitioned into disjointed convex objects representing individual nuclear compartment, by enforcing geometric constraints. Nuclear compartments provide the context for protein localization, which may be diffuse or punctate. Punctate signal are localized through iterative voting and radial symmetries for improved reliability and robustness. The technique has been tested against 196 images that were generated to study centrosome abnormalities. Corresponding computed representations are compared against manual counts for validation.

CT, MR, and ultrasound image artifacts from prostate brachytherapy seed implants: The impact of seed size

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

Robertson, Andrew K. H.; Basran, Parminder S.; Thomas, Steven D.

Purpose: To investigate the effects of brachytherapy seed size on the quality of x-ray computed tomography (CT), ultrasound (US), and magnetic resonance (MR) images and seed localization through comparison of the 6711 and 9011 {sup 125}I sources. Methods: For CT images, an acrylic phantom mimicking a clinical implantation plan and embedded with low contrast regions of interest (ROIs) was designed for both the 0.774 mm diameter 6711 (standard) and the 0.508 mm diameter 9011 (thin) seed models (Oncura, Inc., and GE Healthcare, Arlington Heights, IL). Image quality metrics were assessed using the standard deviation of ROIs between the seeds andmore » the contrast to noise ratio (CNR) within the low contrast ROIs. For US images, water phantoms with both single and multiseed arrangements were constructed for both seed sizes. For MR images, both seeds were implanted into a porcine gel and imaged with pelvic imaging protocols. The standard deviation of ROIs and CNR values were used as metrics of artifact quantification. Seed localization within the CT images was assessed using the automated seed finder in a commercial brachytherapy treatment planning system. The number of erroneous seed placements and the average and maximum error in seed placements were recorded as metrics of the localization accuracy. Results: With the thin seeds, CT image noise was reduced from 48.5 {+-} 0.2 to 32.0 {+-} 0.2 HU and CNR improved by a median value of 74% when compared with the standard seeds. Ultrasound image noise was measured at 50.3 {+-} 17.1 dB for the thin seed images and 50.0 {+-} 19.8 dB for the standard seed images, and artifacts directly behind the seeds were smaller and less prominent with the thin seed model. For MR images, CNR of the standard seeds reduced on average 17% when using the thin seeds for all different imaging sequences and seed orientations, but these differences are not appreciable. Automated seed localization required an average ({+-}SD) of 7.0 {+-} 3.5 manual corrections in seed positions for the thin seed scans and 3.0 {+-} 1.2 manual corrections in seed positions for the standard seed scans. The average error in seed placement was 1.2 mm for both seed types and the maximum error in seed placement was 2.1 mm for the thin seed scans and 1.8 mm for the standard seed scans. Conclusions: The 9011 thin seeds yielded significantly improved image quality for CT and US images but no significant differences in MR image quality.« less

SU-F-I-10: Spatially Local Statistics for Adaptive Image Filtering

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

Iliopoulos, AS; Sun, X; Floros, D

Purpose: To facilitate adaptive image filtering operations, addressing spatial variations in both noise and signal. Such issues are prevalent in cone-beam projections, where physical effects such as X-ray scattering result in spatially variant noise, violating common assumptions of homogeneous noise and challenging conventional filtering approaches to signal extraction and noise suppression. Methods: We present a computational mechanism for probing into and quantifying the spatial variance of noise throughout an image. The mechanism builds a pyramid of local statistics at multiple spatial scales; local statistical information at each scale includes (weighted) mean, median, standard deviation, median absolute deviation, as well asmore » histogram or dynamic range after local mean/median shifting. Based on inter-scale differences of local statistics, the spatial scope of distinguishable noise variation is detected in a semi- or un-supervised manner. Additionally, we propose and demonstrate the incorporation of such information in globally parametrized (i.e., non-adaptive) filters, effectively transforming the latter into spatially adaptive filters. The multi-scale mechanism is materialized by efficient algorithms and implemented in parallel CPU/GPU architectures. Results: We demonstrate the impact of local statistics for adaptive image processing and analysis using cone-beam projections of a Catphan phantom, fitted within an annulus to increase X-ray scattering. The effective spatial scope of local statistics calculations is shown to vary throughout the image domain, necessitating multi-scale noise and signal structure analysis. Filtering results with and without spatial filter adaptation are compared visually, illustrating improvements in imaging signal extraction and noise suppression, and in preserving information in low-contrast regions. Conclusion: Local image statistics can be incorporated in filtering operations to equip them with spatial adaptivity to spatial signal/noise variations. An efficient multi-scale computational mechanism is developed to curtail processing latency. Spatially adaptive filtering may impact subsequent processing tasks such as reconstruction and numerical gradient computations for deformable registration. NIH Grant No. R01-184173.« less

Superresolution Imaging using Single-Molecule Localization

PubMed Central

Patterson, George; Davidson, Michael; Manley, Suliana; Lippincott-Schwartz, Jennifer

2013-01-01

Superresolution imaging is a rapidly emerging new field of microscopy that dramatically improves the spatial resolution of light microscopy by over an order of magnitude (∼10–20-nm resolution), allowing biological processes to be described at the molecular scale. Here, we discuss a form of superresolution microscopy based on the controlled activation and sampling of sparse subsets of photoconvertible fluorescent molecules. In this single-molecule based imaging approach, a wide variety of probes have proved valuable, ranging from genetically encodable photoactivatable fluorescent proteins to photoswitchable cyanine dyes. These have been used in diverse applications of superresolution imaging: from three-dimensional, multicolor molecule localization to tracking of nanometric structures and molecules in living cells. Single-molecule-based superresolution imaging thus offers exciting possibilities for obtaining molecular-scale information on biological events occurring at variable timescales. PMID:20055680

Locally Enhanced Image Quality with Tunable Hybrid Metasurfaces

NASA Astrophysics Data System (ADS)

Shchelokova, Alena V.; Slobozhanyuk, Alexey P.; Melchakova, Irina V.; Glybovski, Stanislav B.; Webb, Andrew G.; Kivshar, Yuri S.; Belov, Pavel A.

2018-01-01

Metasurfaces represent a new paradigm in artificial subwavelength structures due to their potential to overcome many challenges typically associated with bulk metamaterials. The ability to make very thin structures and change their properties dynamically makes metasurfaces an exceptional meta-optics platform for engineering advanced electromagnetic and photonic metadevices. Here, we suggest and demonstrate experimentally a tunable metasurface capable of enhancing significantly the local image quality in magnetic resonance imaging. We present a design of the hybrid metasurface based on electromagnetically coupled dielectric and metallic elements. We demonstrate how to tailor the spectral characteristics of the metasurface eigenmodes by changing dynamically the effective permittivity of the structure. By maximizing a coupling between metasurface eigenmodes and transmitted and received fields in the magnetic resonance imaging (MRI) system, we enhance the device sensitivity that results in a substantial improvement of the image quality.

A semi-automatic method for extracting thin line structures in images as rooted tree network

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

Brazzini, Jacopo; Dillard, Scott; Soille, Pierre

2010-01-01

This paper addresses the problem of semi-automatic extraction of line networks in digital images - e.g., road or hydrographic networks in satellite images, blood vessels in medical images, robust. For that purpose, we improve a generic method derived from morphological and hydrological concepts and consisting in minimum cost path estimation and flow simulation. While this approach fully exploits the local contrast and shape of the network, as well as its arborescent nature, we further incorporate local directional information about the structures in the image. Namely, an appropriate anisotropic metric is designed by using both the characteristic features of the targetmore » network and the eigen-decomposition of the gradient structure tensor of the image. Following, the geodesic propagation from a given seed with this metric is combined with hydrological operators for overland flow simulation to extract the line network. The algorithm is demonstrated for the extraction of blood vessels in a retina image and of a river network in a satellite image.« less

Automatic selection of landmarks in T1-weighted head MRI with regression forests for image registration initialization

NASA Astrophysics Data System (ADS)

Wang, Jianing; Liu, Yuan; Noble, Jack H.; Dawant, Benoit M.

2017-02-01

Medical image registration establishes a correspondence between images of biological structures and it is at the core of many applications. Commonly used deformable image registration methods are dependent on a good preregistration initialization. The initialization can be performed by localizing homologous landmarks and calculating a point-based transformation between the images. The selection of landmarks is however important. In this work, we present a learning-based method to automatically find a set of robust landmarks in 3D MR image volumes of the head to initialize non-rigid transformations. To validate our method, these selected landmarks are localized in unknown image volumes and they are used to compute a smoothing thin-plate splines transformation that registers the atlas to the volumes. The transformed atlas image is then used as the preregistration initialization of an intensity-based non-rigid registration algorithm. We show that the registration accuracy of this algorithm is statistically significantly improved when using the presented registration initialization over a standard intensity-based affine registration.

Echo-Planar Imaging-Based, J-Resolved Spectroscopic Imaging for Improved Metabolite Detection in Prostate Cancer

DTIC Science & Technology

2015-10-01

cancer is through imaging techniques including ultrasound , computed tomography (CT), and magnetic resonance imaging (MRI) with or without the help...performed at least 8 weeks after transrectal ultrasound -guided sextant biopsy. The entire protocol was ap- proved by the Institutional Review Board...volume of interest (VOI) was localized using three slice-selective radiofrequency (RF) pulses (90°–180°–180°) (Fig. 1). The total time for the

Illumination normalization of face image based on illuminant direction estimation and improved Retinex.

PubMed

Yi, Jizheng; Mao, Xia; Chen, Lijiang; Xue, Yuli; Rovetta, Alberto; Caleanu, Catalin-Daniel

2015-01-01

Illumination normalization of face image for face recognition and facial expression recognition is one of the most frequent and difficult problems in image processing. In order to obtain a face image with normal illumination, our method firstly divides the input face image into sixteen local regions and calculates the edge level percentage in each of them. Secondly, three local regions, which meet the requirements of lower complexity and larger average gray value, are selected to calculate the final illuminant direction according to the error function between the measured intensity and the calculated intensity, and the constraint function for an infinite light source model. After knowing the final illuminant direction of the input face image, the Retinex algorithm is improved from two aspects: (1) we optimize the surround function; (2) we intercept the values in both ends of histogram of face image, determine the range of gray levels, and stretch the range of gray levels into the dynamic range of display device. Finally, we achieve illumination normalization and get the final face image. Unlike previous illumination normalization approaches, the method proposed in this paper does not require any training step or any knowledge of 3D face and reflective surface model. The experimental results using extended Yale face database B and CMU-PIE show that our method achieves better normalization effect comparing with the existing techniques.

SU-D-BRF-04: Digital Tomosynthesis for Improved Daily Setup in Treatment of Liver Lesions

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

Armstrong, H; Jones, B; Miften, M

Purpose: Daily localization of liver lesions with cone-beam CT (CBCT) is difficult due to poor image quality caused by scatter, respiratory motion, and the lack of radiographic contrast between the liver parenchyma and the lesion(s). Digital tomosynthesis (DTS) is investigated as a modality to improve liver visualization and lesion/parenchyma contrast for daily setup. Methods: An in-house tool was developed to generate DTS images using a point-by-point filtered back-projection method from on-board CBCT projection data. DTS image planes are generated in a user defined orientation to visualize the anatomy at various depths. Reference DTS images are obtained from forward projection ofmore » the planning CT dataset at each projection angle. The CBCT DTS image set can then be registered to the reference DTS image set as a means for localization. Contour data from the planning CT's associate RT Structure file and forward projected similarly to the planning CT data. DTS images are created for each contoured structure, which can then be overlaid onto the DTS images for organ volume visualization. Results: High resolution DTS images generated from CBCT projections show fine anatomical detail, including small blood vessels, within the patient. However, the reference DTS images generated from forward projection of the planning CT lacks this level of detail due to the low resolution of the CT voxels as compared to the pixel size in the projection images; typically 1mm-by-1mm-by-3mm (lat, vrt, lng) for the planning CT vs. 0.4mm-by-0.4mm for CBCT projections. Overlaying of the contours onto the DTS image allows for visualization of structures of interest. Conclusion: The ability to generate DTS images over a limited range of projection angles allows for reduction in the amount of respiratory motion within each acquisition. DTS may provide improved visualization of structures and lesions as compared to CBCT for highly mobile tumors.« less

Damage localization in aluminum plate with compact rectangular phased piezoelectric transducer array

NASA Astrophysics Data System (ADS)

Liu, Zenghua; Sun, Kunming; Song, Guorong; He, Cunfu; Wu, Bin

2016-03-01

In this work, a detection method for the damage in plate-like structure with a compact rectangular phased piezoelectric transducer array of 16 piezoelectric elements was presented. This compact array can not only detect and locate a single defect (through hole) in plate, but also identify multi-defects (through holes and surface defect simulated by an iron pillar glued to the plate). The experiments proved that the compact rectangular phased transducer array could detect the full range of plate structures and implement multiple-defect detection simultaneously. The processing algorithm proposed in this paper contains two parts: signal filtering and damage imaging. The former part was used to remove noise from signals. Continuous wavelet transform was applicable to signal filtering. Continuous wavelet transform can provide a plot of wavelet coefficients and the signal with narrow frequency band can be easily extracted from the plot. The latter part of processing algorithm was to implement damage detection and localization. In order to accurately locate defects and improve the imaging quality, two images were obtained from amplitude and phase information. One image was obtained with the Total Focusing Method (TFM) and another phase image was obtained with the Sign Coherence Factor (SCF). Furthermore, an image compounding technique for compact rectangular phased piezoelectric transducer array was proposed in this paper. With the proposed technique, the compounded image can be obtained by combining TFM image with SCF image, thus greatly improving the resolution and contrast of image.

Dorsolateral frontal lobe epilepsy.

PubMed

Lee, Ricky W; Worrell, Greg A

2012-10-01

Dorsolateral frontal lobe seizures often present as a diagnostic challenge. The diverse semiologies may not produce lateralizing or localizing signs and can appear bizarre and suggest psychogenic events. Unfortunately, scalp electroencephalographic (EEG) and magnetic resonance imaging (MRI) are often unsatisfactory. It is not uncommon that these traditional diagnostic studies are either unhelpful or even misleading. In some cases, SPECT and positron emission tomography imaging can be an effective tool to identify the origin of seizures. However, these techniques and other emerging techniques all have limitations, and new approaches are needed to improve source localization.

Facial recognition using multisensor images based on localized kernel eigen spaces.

PubMed

Gundimada, Satyanadh; Asari, Vijayan K

2009-06-01

A feature selection technique along with an information fusion procedure for improving the recognition accuracy of a visual and thermal image-based facial recognition system is presented in this paper. A novel modular kernel eigenspaces approach is developed and implemented on the phase congruency feature maps extracted from the visual and thermal images individually. Smaller sub-regions from a predefined neighborhood within the phase congruency images of the training samples are merged to obtain a large set of features. These features are then projected into higher dimensional spaces using kernel methods. The proposed localized nonlinear feature selection procedure helps to overcome the bottlenecks of illumination variations, partial occlusions, expression variations and variations due to temperature changes that affect the visual and thermal face recognition techniques. AR and Equinox databases are used for experimentation and evaluation of the proposed technique. The proposed feature selection procedure has greatly improved the recognition accuracy for both the visual and thermal images when compared to conventional techniques. Also, a decision level fusion methodology is presented which along with the feature selection procedure has outperformed various other face recognition techniques in terms of recognition accuracy.

Medical Image Compression Based on Vector Quantization with Variable Block Sizes in Wavelet Domain

PubMed Central

Jiang, Huiyan; Ma, Zhiyuan; Hu, Yang; Yang, Benqiang; Zhang, Libo

2012-01-01

An optimized medical image compression algorithm based on wavelet transform and improved vector quantization is introduced. The goal of the proposed method is to maintain the diagnostic-related information of the medical image at a high compression ratio. Wavelet transformation was first applied to the image. For the lowest-frequency subband of wavelet coefficients, a lossless compression method was exploited; for each of the high-frequency subbands, an optimized vector quantization with variable block size was implemented. In the novel vector quantization method, local fractal dimension (LFD) was used to analyze the local complexity of each wavelet coefficients, subband. Then an optimal quadtree method was employed to partition each wavelet coefficients, subband into several sizes of subblocks. After that, a modified K-means approach which is based on energy function was used in the codebook training phase. At last, vector quantization coding was implemented in different types of sub-blocks. In order to verify the effectiveness of the proposed algorithm, JPEG, JPEG2000, and fractal coding approach were chosen as contrast algorithms. Experimental results show that the proposed method can improve the compression performance and can achieve a balance between the compression ratio and the image visual quality. PMID:23049544

Medical image compression based on vector quantization with variable block sizes in wavelet domain.

PubMed

Jiang, Huiyan; Ma, Zhiyuan; Hu, Yang; Yang, Benqiang; Zhang, Libo

2012-01-01

An optimized medical image compression algorithm based on wavelet transform and improved vector quantization is introduced. The goal of the proposed method is to maintain the diagnostic-related information of the medical image at a high compression ratio. Wavelet transformation was first applied to the image. For the lowest-frequency subband of wavelet coefficients, a lossless compression method was exploited; for each of the high-frequency subbands, an optimized vector quantization with variable block size was implemented. In the novel vector quantization method, local fractal dimension (LFD) was used to analyze the local complexity of each wavelet coefficients, subband. Then an optimal quadtree method was employed to partition each wavelet coefficients, subband into several sizes of subblocks. After that, a modified K-means approach which is based on energy function was used in the codebook training phase. At last, vector quantization coding was implemented in different types of sub-blocks. In order to verify the effectiveness of the proposed algorithm, JPEG, JPEG2000, and fractal coding approach were chosen as contrast algorithms. Experimental results show that the proposed method can improve the compression performance and can achieve a balance between the compression ratio and the image visual quality.

A novel algorithm of super-resolution image reconstruction based on multi-class dictionaries for natural scene

NASA Astrophysics Data System (ADS)

Wu, Wei; Zhao, Dewei; Zhang, Huan

2015-12-01

Super-resolution image reconstruction is an effective method to improve the image quality. It has important research significance in the field of image processing. However, the choice of the dictionary directly affects the efficiency of image reconstruction. A sparse representation theory is introduced into the problem of the nearest neighbor selection. Based on the sparse representation of super-resolution image reconstruction method, a super-resolution image reconstruction algorithm based on multi-class dictionary is analyzed. This method avoids the redundancy problem of only training a hyper complete dictionary, and makes the sub-dictionary more representatives, and then replaces the traditional Euclidean distance computing method to improve the quality of the whole image reconstruction. In addition, the ill-posed problem is introduced into non-local self-similarity regularization. Experimental results show that the algorithm is much better results than state-of-the-art algorithm in terms of both PSNR and visual perception.

Cochlear Implant Electrode Localization Using an Ultra-High Resolution Scan Mode on Conventional 64-Slice and New Generation 192-Slice Multi-Detector Computed Tomography.

PubMed

Carlson, Matthew L; Leng, Shuai; Diehn, Felix E; Witte, Robert J; Krecke, Karl N; Grimes, Josh; Koeller, Kelly K; Bruesewitz, Michael R; McCollough, Cynthia H; Lane, John I

2017-08-01

A new generation 192-slice multi-detector computed tomography (MDCT) clinical scanner provides enhanced image quality and superior electrode localization over conventional MDCT. Currently, accurate and reliable cochlear implant electrode localization using conventional MDCT scanners remains elusive. Eight fresh-frozen cadaveric temporal bones were implanted with full-length cochlear implant electrodes. Specimens were subsequently scanned with conventional 64-slice and new generation 192-slice MDCT scanners utilizing ultra-high resolution modes. Additionally, all specimens were scanned with micro-CT to provide a reference criterion for electrode position. Images were reconstructed according to routine temporal bone clinical protocols. Three neuroradiologists, blinded to scanner type, reviewed images independently to assess resolution of individual electrodes, scalar localization, and severity of image artifact. Serving as the reference standard, micro-CT identified scalar crossover in one specimen; imaging of all remaining cochleae demonstrated complete scala tympani insertions. The 192-slice MDCT scanner exhibited improved resolution of individual electrodes (p < 0.01), superior scalar localization (p < 0.01), and reduced blooming artifact (p < 0.05), compared with conventional 64-slice MDCT. There was no significant difference between platforms when comparing streak or ring artifact. The new generation 192-slice MDCT scanner offers several notable advantages for cochlear implant imaging compared with conventional MDCT. This technology provides important feedback regarding electrode position and course, which may help in future optimization of surgical technique and electrode design.

Development and Evaluation of Real-Time Volumetric Compton Gamma-Ray Imaging

NASA Astrophysics Data System (ADS)

Barnowski, Ross Wegner

An approach to gamma-ray imaging has been developed that enables near real-time volumetric (3D) imaging of unknown environments thus improving the utility of gamma-ray imaging for source-search and radiation mapping applications. The approach, herein dubbed scene data fusion (SDF), is based on integrating mobile radiation imagers with real time tracking and scene reconstruction algorithms to enable a mobile mode of operation and 3D localization of gamma-ray sources. The real-time tracking allows the imager to be moved throughout the environment or around a particular object of interest, obtaining the multiple perspectives necessary for standoff 3D imaging. A 3D model of the scene, provided in real-time by a simultaneous localization and mapping (SLAM) algorithm, can be incorporated into the image reconstruction reducing the reconstruction time and improving imaging performance. The SDF concept is demonstrated in this work with a Microsoft Kinect RGB-D sensor, a real-time SLAM solver, and two different mobile gamma-ray imaging platforms. The first is a cart-based imaging platform known as the Volumetric Compton Imager (VCI), comprising two 3D position-sensitive high purity germanium (HPGe) detectors, exhibiting excellent gamma-ray imaging characteristics, but with limited mobility due to the size and weight of the cart. The second system is the High Efficiency Multimodal Imager (HEMI) a hand-portable gamma-ray imager comprising 96 individual cm3 CdZnTe crystals arranged in a two-plane, active-mask configuration. The HEMI instrument has poorer energy and angular resolution than the VCI, but is truly hand-portable, allowing the SDF concept to be tested in multiple environments and for more challenging imaging scenarios. An iterative algorithm based on Compton kinematics is used to reconstruct the gamma-ray source distribution in all three spatial dimensions. Each of the two mobile imaging systems are used to demonstrate SDF for a variety of scenarios, including general search and mapping scenarios with several point gamma-ray sources over the range of energies relevant for Compton imaging. More specific imaging scenarios are also addressed, including directed search and object interrogation scenarios. Finally, the volumetric image quality is quantitatively investigated with respect to the number of Compton events acquired during a measurement, the list-mode uncertainty of the Compton cone data, and the uncertainty in the pose estimate from the real-time tracking algorithm. SDF advances the real-world applicability of gamma-ray imaging for many search, mapping, and verification scenarios by improving the tractability of the gamma-ray image reconstruction and providing context for the 3D localization of gamma-ray sources within the environment in real-time.

[Application of an Adaptive Inertia Weight Particle Swarm Algorithm in the Magnetic Resonance Bias Field Correction].

PubMed

Wang, Chang; Qin, Xin; Liu, Yan; Zhang, Wenchao

2016-06-01

An adaptive inertia weight particle swarm algorithm is proposed in this study to solve the local optimal problem with the method of traditional particle swarm optimization in the process of estimating magnetic resonance(MR)image bias field.An indicator measuring the degree of premature convergence was designed for the defect of traditional particle swarm optimization algorithm.The inertia weight was adjusted adaptively based on this indicator to ensure particle swarm to be optimized globally and to avoid it from falling into local optimum.The Legendre polynomial was used to fit bias field,the polynomial parameters were optimized globally,and finally the bias field was estimated and corrected.Compared to those with the improved entropy minimum algorithm,the entropy of corrected image was smaller and the estimated bias field was more accurate in this study.Then the corrected image was segmented and the segmentation accuracy obtained in this research was 10% higher than that with improved entropy minimum algorithm.This algorithm can be applied to the correction of MR image bias field.

Evaluation of tumor localization in respiration motion-corrected cone-beam CT: Prospective study in lung

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

Dzyubak, Oleksandr; Kincaid, Russell; Hertanto, Agung

Purpose: Target localization accuracy of cone-beam CT (CBCT) images used in radiation treatment of respiratory disease sites is affected by motion artifacts (blurring and streaking). The authors have previously reported on a method of respiratory motion correction in thoracic CBCT at end expiration (EE). The previous retrospective study was limited to examination of reducing motion artifacts in a small number of patient cases. They report here on a prospective study in a larger group of lung cancer patients to evaluate respiratory motion-corrected (RMC)-CBCT ability to improve lung tumor localization accuracy and reduce motion artifacts in Linac-mounted CBCT images. A secondmore » study goal examines whether the motion correction derived from a respiration-correlated CT (RCCT) at simulation yields similar tumor localization accuracy at treatment. Methods: In an IRB-approved study, 19 lung cancer patients (22 tumors) received a RCCT at simulation, and on one treatment day received a RCCT, a respiratory-gated CBCT at end expiration, and a 1-min CBCT. A respiration monitor of abdominal displacement was used during all scans. In addition to a CBCT reconstruction without motion correction, the motion correction method was applied to the same 1-min scan. Projection images were sorted into ten bins based on abdominal displacement, and each bin was reconstructed to produce ten intermediate CBCT images. Each intermediate CBCT was deformed to the end expiration state using a motion model derived from RCCT. The deformed intermediate CBCT images were then added to produce a final RMC-CBCT. In order to evaluate the second study goal, the CBCT was corrected in two ways, one using a model derived from the RCCT at simulation [RMC-CBCT(sim)], the other from the RCCT at treatment [RMC-CBCT(tx)]. Image evaluation compared uncorrected CBCT, RMC-CBCT(sim), and RMC-CBCT(tx). The gated CBCT at end expiration served as the criterion standard for comparison. Using automatic rigid image registration, each CBCT was registered twice to the gated CBCT, first aligned to spine, second to tumor in lung. Localization discrepancy was defined as the difference between tumor and spine registration. Agreement in tumor localization with the gated CBCT was further evaluated by calculating a normalized cross correlation (NCC) of pixel intensities within a volume-of-interest enclosing the tumor in lung. Results: Tumor localization discrepancy was reduced with RMC-CBCT(tx) in 17 out of 22 cases relative to no correction. If one considers cases in which tumor motion is 5 mm or more in the RCCT, tumor localization discrepancy is reduced with RMC-CBCT(tx) in 14 out of 17 cases (p = 0.04), and with RMC-CBCT(sim) in 13 out of 17 cases (p = 0.05). Differences in localization discrepancy between correction models [RMC-CBCT(sim) vs RMC-CBCT(tx)] were less than 2 mm. In 21 out of 22 cases, improvement in NCC was higher with RMC-CBCT(tx) relative to no correction (p < 0.0001). Differences in NCC between RMC-CBCT(sim) and RMC-CBCT(tx) were small. Conclusions: Motion-corrected CBCT improves lung tumor localization accuracy and reduces motion artifacts in nearly all cases. Motion correction at end expiration using RCCT acquired at simulation yields similar results to that using a RCCT on the treatment day (2–3 weeks after simulation)« less

Evaluation of tumor localization in respiration motion-corrected cone-beam CT: prospective study in lung.

PubMed

Dzyubak, Oleksandr; Kincaid, Russell; Hertanto, Agung; Hu, Yu-Chi; Pham, Hai; Rimner, Andreas; Yorke, Ellen; Zhang, Qinghui; Mageras, Gig S

2014-10-01

Target localization accuracy of cone-beam CT (CBCT) images used in radiation treatment of respiratory disease sites is affected by motion artifacts (blurring and streaking). The authors have previously reported on a method of respiratory motion correction in thoracic CBCT at end expiration (EE). The previous retrospective study was limited to examination of reducing motion artifacts in a small number of patient cases. They report here on a prospective study in a larger group of lung cancer patients to evaluate respiratory motion-corrected (RMC)-CBCT ability to improve lung tumor localization accuracy and reduce motion artifacts in Linac-mounted CBCT images. A second study goal examines whether the motion correction derived from a respiration-correlated CT (RCCT) at simulation yields similar tumor localization accuracy at treatment. In an IRB-approved study, 19 lung cancer patients (22 tumors) received a RCCT at simulation, and on one treatment day received a RCCT, a respiratory-gated CBCT at end expiration, and a 1-min CBCT. A respiration monitor of abdominal displacement was used during all scans. In addition to a CBCT reconstruction without motion correction, the motion correction method was applied to the same 1-min scan. Projection images were sorted into ten bins based on abdominal displacement, and each bin was reconstructed to produce ten intermediate CBCT images. Each intermediate CBCT was deformed to the end expiration state using a motion model derived from RCCT. The deformed intermediate CBCT images were then added to produce a final RMC-CBCT. In order to evaluate the second study goal, the CBCT was corrected in two ways, one using a model derived from the RCCT at simulation [RMC-CBCT(sim)], the other from the RCCT at treatment [RMC-CBCT(tx)]. Image evaluation compared uncorrected CBCT, RMC-CBCT(sim), and RMC-CBCT(tx). The gated CBCT at end expiration served as the criterion standard for comparison. Using automatic rigid image registration, each CBCT was registered twice to the gated CBCT, first aligned to spine, second to tumor in lung. Localization discrepancy was defined as the difference between tumor and spine registration. Agreement in tumor localization with the gated CBCT was further evaluated by calculating a normalized cross correlation (NCC) of pixel intensities within a volume-of-interest enclosing the tumor in lung. Tumor localization discrepancy was reduced with RMC-CBCT(tx) in 17 out of 22 cases relative to no correction. If one considers cases in which tumor motion is 5 mm or more in the RCCT, tumor localization discrepancy is reduced with RMC-CBCT(tx) in 14 out of 17 cases (p = 0.04), and with RMC-CBCT(sim) in 13 out of 17 cases (p = 0.05). Differences in localization discrepancy between correction models [RMC-CBCT(sim) vs RMC-CBCT(tx)] were less than 2 mm. In 21 out of 22 cases, improvement in NCC was higher with RMC-CBCT(tx) relative to no correction (p < 0.0001). Differences in NCC between RMC-CBCT(sim) and RMC-CBCT(tx) were small. Motion-corrected CBCT improves lung tumor localization accuracy and reduces motion artifacts in nearly all cases. Motion correction at end expiration using RCCT acquired at simulation yields similar results to that using a RCCT on the treatment day (2-3 weeks after simulation).

Detection of Multiple Stationary Humans Using UWB MIMO Radar.

PubMed

Liang, Fulai; Qi, Fugui; An, Qiang; Lv, Hao; Chen, Fuming; Li, Zhao; Wang, Jianqi

2016-11-16

Remarkable progress has been achieved in the detection of single stationary human. However, restricted by the mutual interference of multiple humans (e.g., strong sidelobes of the torsos and the shadow effect), detection and localization of the multiple stationary humans remains a huge challenge. In this paper, ultra-wideband (UWB) multiple-input and multiple-output (MIMO) radar is exploited to improve the detection performance of multiple stationary humans for its multiple sight angles and high-resolution two-dimensional imaging capacity. A signal model of the vital sign considering both bi-static angles and attitude angle of the human body is firstly developed, and then a novel detection method is proposed to detect and localize multiple stationary humans. In this method, preprocessing is firstly implemented to improve the signal-to-noise ratio (SNR) of the vital signs, and then a vital-sign-enhanced imaging algorithm is presented to suppress the environmental clutters and mutual affection of multiple humans. Finally, an automatic detection algorithm including constant false alarm rate (CFAR), morphological filtering and clustering is implemented to improve the detection performance of weak human targets affected by heavy clutters and shadow effect. The simulation and experimental results show that the proposed method can get a high-quality image of multiple humans and we can use it to discriminate and localize multiple adjacent human targets behind brick walls.

Detection of Multiple Stationary Humans Using UWB MIMO Radar

PubMed Central

Liang, Fulai; Qi, Fugui; An, Qiang; Lv, Hao; Chen, Fuming; Li, Zhao; Wang, Jianqi

2016-01-01

Remarkable progress has been achieved in the detection of single stationary human. However, restricted by the mutual interference of multiple humans (e.g., strong sidelobes of the torsos and the shadow effect), detection and localization of the multiple stationary humans remains a huge challenge. In this paper, ultra-wideband (UWB) multiple-input and multiple-output (MIMO) radar is exploited to improve the detection performance of multiple stationary humans for its multiple sight angles and high-resolution two-dimensional imaging capacity. A signal model of the vital sign considering both bi-static angles and attitude angle of the human body is firstly developed, and then a novel detection method is proposed to detect and localize multiple stationary humans. In this method, preprocessing is firstly implemented to improve the signal-to-noise ratio (SNR) of the vital signs, and then a vital-sign-enhanced imaging algorithm is presented to suppress the environmental clutters and mutual affection of multiple humans. Finally, an automatic detection algorithm including constant false alarm rate (CFAR), morphological filtering and clustering is implemented to improve the detection performance of weak human targets affected by heavy clutters and shadow effect. The simulation and experimental results show that the proposed method can get a high-quality image of multiple humans and we can use it to discriminate and localize multiple adjacent human targets behind brick walls. PMID:27854356

Assessing the performance of quantitative image features on early stage prediction of treatment effectiveness for ovary cancer patients: a preliminary investigation

NASA Astrophysics Data System (ADS)

Zargari, Abolfazl; Du, Yue; Thai, Theresa C.; Gunderson, Camille C.; Moore, Kathleen; Mannel, Robert S.; Liu, Hong; Zheng, Bin; Qiu, Yuchen

2018-02-01

The objective of this study is to investigate the performance of global and local features to better estimate the characteristics of highly heterogeneous metastatic tumours, for accurately predicting the treatment effectiveness of the advanced stage ovarian cancer patients. In order to achieve this , a quantitative image analysis scheme was developed to estimate a total of 103 features from three different groups including shape and density, Wavelet, and Gray Level Difference Method (GLDM) features. Shape and density features are global features, which are directly applied on the entire target image; wavelet and GLDM features are local features, which are applied on the divided blocks of the target image. To assess the performance, the new scheme was applied on a retrospective dataset containing 120 recurrent and high grade ovary cancer patients. The results indicate that the three best performed features are skewness, root-mean-square (rms) and mean of local GLDM texture, indicating the importance of integrating local features. In addition, the averaged predicting performance are comparable among the three different categories. This investigation concluded that the local features contains at least as copious tumour heterogeneity information as the global features, which may be meaningful on improving the predicting performance of the quantitative image markers for the diagnosis and prognosis of ovary cancer patients.

Innovations in diagnostic imaging of localized prostate cancer.

PubMed

Pummer, Karl; Rieken, Malte; Augustin, Herbert; Gutschi, Thomas; Shariat, Shahrokh F

2014-08-01

In recent years, various imaging modalities have been developed to improve diagnosis, staging, and localization of early-stage prostate cancer (PCa). A MEDLINE literature search of the time frame between 01/2007 and 06/2013 was performed on imaging of localized PCa. Conventional transrectal ultrasound (TRUS) is mainly used to guide prostate biopsy. Contrast-enhanced ultrasound is based on the assumption that PCa tissue is hypervascularized and might be better identified after intravenous injection of a microbubble contrast agent. However, results on its additional value for cancer detection are controversial. Computer-based analysis of the transrectal ultrasound signal (C-TRUS) appears to detect cancer in a high rate of patients with previous biopsies. Real-time elastography seems to have higher sensitivity, specificity, and positive predictive value than conventional TRUS. However, the method still awaits prospective validation. The same is true for prostate histoscanning, an ultrasound-based method for tissue characterization. Currently, multiparametric MRI provides improved tissue visualization of the prostate, which may be helpful in the diagnosis and targeting of prostate lesions. However, most published series are small and suffer from variations in indication, methodology, quality, interpretation, and reporting. Among ultrasound-based techniques, real-time elastography and C-TRUS seem the most promising techniques. Multiparametric MRI appears to have advantages over conventional T2-weighted MRI in the detection of PCa. Despite these promising results, currently, no recommendation for the routine use of these novel imaging techniques can be made. Prospective studies defining the value of various imaging modalities are urgently needed.

Marginal space learning for efficient detection of 2D/3D anatomical structures in medical images.

PubMed

Zheng, Yefeng; Georgescu, Bogdan; Comaniciu, Dorin

2009-01-01

Recently, marginal space learning (MSL) was proposed as a generic approach for automatic detection of 3D anatomical structures in many medical imaging modalities [1]. To accurately localize a 3D object, we need to estimate nine pose parameters (three for position, three for orientation, and three for anisotropic scaling). Instead of exhaustively searching the original nine-dimensional pose parameter space, only low-dimensional marginal spaces are searched in MSL to improve the detection speed. In this paper, we apply MSL to 2D object detection and perform a thorough comparison between MSL and the alternative full space learning (FSL) approach. Experiments on left ventricle detection in 2D MRI images show MSL outperforms FSL in both speed and accuracy. In addition, we propose two novel techniques, constrained MSL and nonrigid MSL, to further improve the efficiency and accuracy. In many real applications, a strong correlation may exist among pose parameters in the same marginal spaces. For example, a large object may have large scaling values along all directions. Constrained MSL exploits this correlation for further speed-up. The original MSL only estimates the rigid transformation of an object in the image, therefore cannot accurately localize a nonrigid object under a large deformation. The proposed nonrigid MSL directly estimates the nonrigid deformation parameters to improve the localization accuracy. The comparison experiments on liver detection in 226 abdominal CT volumes demonstrate the effectiveness of the proposed methods. Our system takes less than a second to accurately detect the liver in a volume.

Prostate Specific Membrane Antigen Positron Emission Tomography May Improve the Diagnostic Accuracy of Multiparametric Magnetic Resonance Imaging in Localized Prostate Cancer.

PubMed

Rhee, H; Thomas, P; Shepherd, B; Gustafson, S; Vela, I; Russell, P J; Nelson, C; Chung, E; Wood, G; Malone, G; Wood, S; Heathcote, P

2016-10-01

Positron emission tomography using ligands targeting prostate specific membrane antigen has recently been introduced. Positron emission tomography imaging with (68)Ga-PSMA-HBED-CC has been shown to detect metastatic prostate cancer lesions at a high rate. In this study we compare multiparametric magnetic resonance imaging and prostate specific membrane antigen positron emission tomography of the prostate with whole mount ex vivo prostate histopathology to determine the true sensitivity and specificity of these imaging modalities for detecting and locating tumor foci within the prostate. In a prospective clinical trial setting 20 patients with localized prostate cancer and a planned radical prostatectomy were recruited. All patients underwent multiparametric magnetic resonance imaging and positron emission tomography before surgery, and whole mount histopathology slides were directly compared to the images. European Society of Urogenital Radiology guidelines for reporting magnetic resonance imaging were used as a template for regional units of analysis. The uropathologist and radiologists were blinded to individual components of the study, and the final correlation was performed by visual and deformable registration analysis. A total of 50 clinically significant lesions were identified from the whole mount histopathological analysis. Based on regional analysis the sensitivity, specificity, positive predictive value and negative predictive value for multiparametric magnetic resonance imaging were 44%, 94%, 81% and 76%, respectively. With prostate specific membrane antigen positron emission tomography the sensitivity, specificity, positive predictive value and negative predictive value were 49%, 95%, 85% and 88%, respectively. Prostate specific membrane antigen positron emission tomography yielded a higher specificity and positive predictive value. A significant proportion of cancers are potentially missed and underestimated by both imaging modalities. Prostate specific membrane antigen positron emission tomography may be used in addition to multiparametric magnetic resonance imaging to help improve local staging in those patients undergoing retropubic radical prostatectomy. Copyright © 2016 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Superelliptical insert gradient coil with a field-modifying layer for breast imaging.

PubMed

Moon, Sung M; Goodrich, K Craig; Hadley, J Rock; Kim, Seong-Eun; Zeng, Gengsheng L; Morrell, Glen R; McAlpine, Matthew A; Chronik, Blaine A; Parker, Dennis L

2011-03-01

Many MRI applications such as dynamic contrast-enhanced MRI of the breast require high spatial and temporal resolution and can benefit from improved gradient performance, e.g., increased gradient strength and reduced gradient rise time. The improved gradient performance required to achieve high spatial and temporal resolution for this application may be achieved by using local insert gradients specifically designed for a target anatomy. Current flat gradient systems cannot create an imaging volume large enough to accommodate both breasts; further, their gradient fields are not homogeneous, dropping off rapidly with distance from the gradient coil surface. To attain an imaging volume adequate for bilateral breast MRI, a planar local gradient system design has been modified into a superellipse shape, creating homogeneous gradient volumes that are 182% (Gx), 57% (Gy), and 75% (Gz) wider (left/right direction) than those of the corresponding standard planar gradient. Adding an additional field-modifying gradient winding results in an additional improvement of the homogeneous gradient field near the gradient coil surface over the already enlarged homogeneous gradient volumes of the superelliptical gradients (67%, 89%, and 214% for Gx, Gy, and Gz respectively). A prototype y-gradient insert has been built to demonstrate imaging and implementation characteristics of the superellipse gradient in a 3 T MRI system. Copyright © 2010 Wiley-Liss, Inc.

Near-infrared intraoperative imaging during resection of an anterior mediastinal soft tissue sarcoma.

PubMed

Predina, Jarrod D; Newton, Andrew D; Desphande, Charuhas; Singhal, Sunil

2018-01-01

Sarcomas are rare malignancies that are generally treated with multimodal therapy protocols incorporating complete local resection, chemotherapy and radiation. Unfortunately, even with this aggressive approach, local recurrences are common. Near-infrared intraoperative imaging is a novel technology that provides real-time visual feedback that can improve identification of disease during resection. The presented study describes utilization of a near-infrared agent (indocyanine green) during resection of an anterior mediastinal sarcoma. Real-time fluorescent feedback provided visual information that helped the surgeon during tumor localization, margin assessment and dissection from mediastinal structures. This rapidly evolving technology may prove useful in patients with primary sarcomas arising from other locations or with other mediastinal neoplasms.

Automatic segmentation of brain MRI in high-dimensional local and non-local feature space based on sparse representation.

PubMed

Khalilzadeh, Mohammad Mahdi; Fatemizadeh, Emad; Behnam, Hamid

2013-06-01

Automatic extraction of the varying regions of magnetic resonance images is required as a prior step in a diagnostic intelligent system. The sparsest representation and high-dimensional feature are provided based on learned dictionary. The classification is done by employing the technique that computes the reconstruction error locally and non-locally of each pixel. The acquired results from the real and simulated images are superior to the best MRI segmentation method with regard to the stability advantages. In addition, it is segmented exactly through a formula taken from the distance and sparse factors. Also, it is done automatically taking sparse factor in unsupervised clustering methods whose results have been improved. Copyright © 2013 Elsevier Inc. All rights reserved.

Improving oncoplastic breast tumor bed localization for radiotherapy planning using image registration algorithms

NASA Astrophysics Data System (ADS)

Wodzinski, Marek; Skalski, Andrzej; Ciepiela, Izabela; Kuszewski, Tomasz; Kedzierawski, Piotr; Gajda, Janusz

2018-02-01

Knowledge about tumor bed localization and its shape analysis is a crucial factor for preventing irradiation of healthy tissues during supportive radiotherapy and as a result, cancer recurrence. The localization process is especially hard for tumors placed nearby soft tissues, which undergo complex, nonrigid deformations. Among them, breast cancer can be considered as the most representative example. A natural approach to improving tumor bed localization is the use of image registration algorithms. However, this involves two unusual aspects which are not common in typical medical image registration: the real deformation field is discontinuous, and there is no direct correspondence between the cancer and its bed in the source and the target 3D images respectively. The tumor no longer exists during radiotherapy planning. Therefore, a traditional evaluation approach based on known, smooth deformations and target registration error are not directly applicable. In this work, we propose alternative artificial deformations which model the tumor bed creation process. We perform a comprehensive evaluation of the most commonly used deformable registration algorithms: B-Splines free form deformations (B-Splines FFD), different variants of the Demons and TV-L1 optical flow. The evaluation procedure includes quantitative assessment of the dedicated artificial deformations, target registration error calculation, 3D contour propagation and medical experts visual judgment. The results demonstrate that the currently, practically applied image registration (rigid registration and B-Splines FFD) are not able to correctly reconstruct discontinuous deformation fields. We show that the symmetric Demons provide the most accurate soft tissues alignment in terms of the ability to reconstruct the deformation field, target registration error and relative tumor volume change, while B-Splines FFD and TV-L1 optical flow are not an appropriate choice for the breast tumor bed localization problem, even though the visual alignment seems to be better than for the Demons algorithm. However, no algorithm could recover the deformation field with sufficient accuracy in terms of vector length and rotation angle differences.

Ultrasonic imaging of material flaws exploiting multipath information

NASA Astrophysics Data System (ADS)

Shen, Xizhong; Zhang, Yimin D.; Demirli, Ramazan; Amin, Moeness G.

2011-05-01

In this paper, we consider ultrasonic imaging for the visualization of flaws in a material. Ultrasonic imaging is a powerful nondestructive testing (NDT) tool which assesses material conditions via the detection, localization, and classification of flaws inside a structure. Multipath exploitations provide extended virtual array apertures and, in turn, enhance imaging capability beyond the limitation of traditional multisensor approaches. We utilize reflections of ultrasonic signals which occur when encountering different media and interior discontinuities. The waveforms observed at the physical as well as virtual sensors yield additional measurements corresponding to different aspect angles. Exploitation of multipath information addresses unique issues observed in ultrasonic imaging. (1) Utilization of physical and virtual sensors significantly extends the array aperture for image enhancement. (2) Multipath signals extend the angle of view of the narrow beamwidth of the ultrasound transducers, allowing improved visibility and array design flexibility. (3) Ultrasonic signals experience difficulty in penetrating a flaw, thus the aspect angle of the observation is limited unless access to other sides is available. The significant extension of the aperture makes it possible to yield flaw observation from multiple aspect angles. We show that data fusion of physical and virtual sensor data significantly improves the detection and localization performance. The effectiveness of the proposed multipath exploitation approach is demonstrated through experimental studies.

Constructing a Database from Multiple 2D Images for Camera Pose Estimation and Robot Localization

NASA Technical Reports Server (NTRS)

Wolf, Michael; Ansar, Adnan I.; Brennan, Shane; Clouse, Daniel S.; Padgett, Curtis W.

2012-01-01

The LMDB (Landmark Database) Builder software identifies persistent image features (landmarks) in a scene viewed multiple times and precisely estimates the landmarks 3D world positions. The software receives as input multiple 2D images of approximately the same scene, along with an initial guess of the camera poses for each image, and a table of features matched pair-wise in each frame. LMDB Builder aggregates landmarks across an arbitrarily large collection of frames with matched features. Range data from stereo vision processing can also be passed to improve the initial guess of the 3D point estimates. The LMDB Builder aggregates feature lists across all frames, manages the process to promote selected features to landmarks, and iteratively calculates the 3D landmark positions using the current camera pose estimations (via an optimal ray projection method), and then improves the camera pose estimates using the 3D landmark positions. Finally, it extracts image patches for each landmark from auto-selected key frames and constructs the landmark database. The landmark database can then be used to estimate future camera poses (and therefore localize a robotic vehicle that may be carrying the cameras) by matching current imagery to landmark database image patches and using the known 3D landmark positions to estimate the current pose.

Conductive resins improve charging and resolution of acquired images in electron microscopic volume imaging

PubMed Central

Nguyen, Huy Bang; Thai, Truc Quynh; Saitoh, Sei; Wu, Bao; Saitoh, Yurika; Shimo, Satoshi; Fujitani, Hiroshi; Otobe, Hirohide; Ohno, Nobuhiko

2016-01-01

Recent advances in serial block-face imaging using scanning electron microscopy (SEM) have enabled the rapid and efficient acquisition of 3-dimensional (3D) ultrastructural information from a large volume of biological specimens including brain tissues. However, volume imaging under SEM is often hampered by sample charging, and typically requires specific sample preparation to reduce charging and increase image contrast. In the present study, we introduced carbon-based conductive resins for 3D analyses of subcellular ultrastructures, using serial block-face SEM (SBF-SEM) to image samples. Conductive resins were produced by adding the carbon black filler, Ketjen black, to resins commonly used for electron microscopic observations of biological specimens. Carbon black mostly localized around tissues and did not penetrate cells, whereas the conductive resins significantly reduced the charging of samples during SBF-SEM imaging. When serial images were acquired, embedding into the conductive resins improved the resolution of images by facilitating the successful cutting of samples in SBF-SEM. These results suggest that improving the conductivities of resins with a carbon black filler is a simple and useful option for reducing charging and enhancing the resolution of images obtained for volume imaging with SEM. PMID:27020327

Method to improve cancerous lesion detection sensitivity in a dedicated dual-head scintimammography system

DOEpatents

Kieper, Douglas Arthur [Seattle, WA; Majewski, Stanislaw [Morgantown, WV; Welch, Benjamin L [Hampton, VA

2012-07-03

An improved method for enhancing the contrast between background and lesion areas of a breast undergoing dual-head scintimammographic examination comprising: 1) acquiring a pair of digital images from a pair of small FOV or mini gamma cameras compressing the breast under examination from opposing sides; 2) inverting one of the pair of images to align or co-register with the other of the images to obtain co-registered pixel values; 3) normalizing the pair of images pixel-by-pixel by dividing pixel values from each of the two acquired images and the co-registered image by the average count per pixel in the entire breast area of the corresponding detector; and 4) multiplying the number of counts in each pixel by the value obtained in step 3 to produce a normalization enhanced two dimensional contrast map. This enhanced (increased contrast) contrast map enhances the visibility of minor local increases (uptakes) of activity over the background and therefore improves lesion detection sensitivity, especially of small lesions.

Method to improve cancerous lesion detection sensitivity in a dedicated dual-head scintimammography system

DOEpatents

Kieper, Douglas Arthur [Newport News, VA; Majewski, Stanislaw [Yorktown, VA; Welch, Benjamin L [Hampton, VA

2008-10-28

An improved method for enhancing the contrast between background and lesion areas of a breast undergoing dual-head scintimammographic examination comprising: 1) acquiring a pair of digital images from a pair of small FOV or mini gamma cameras compressing the breast under examination from opposing sides; 2) inverting one of the pair of images to align or co-register with the other of the images to obtain co-registered pixel values; 3) normalizing the pair of images pixel-by-pixel by dividing pixel values from each of the two acquired images and the co-registered image by the average count per pixel in the entire breast area of the corresponding detector; and 4) multiplying the number of counts in each pixel by the value obtained in step 3 to produce a normalization enhanced two dimensional contrast map. This enhanced (increased contrast) contrast map enhances the visibility of minor local increases (uptakes) of activity over the background and therefore improves lesion detection sensitivity, especially of small lesions.

Image-optimized Coronal Magnetic Field Models

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

Jones, Shaela I.; Uritsky, Vadim; Davila, Joseph M., E-mail: shaela.i.jones-mecholsky@nasa.gov, E-mail: shaela.i.jonesmecholsky@nasa.gov

We have reported previously on a new method we are developing for using image-based information to improve global coronal magnetic field models. In that work, we presented early tests of the method, which proved its capability to improve global models based on flawed synoptic magnetograms, given excellent constraints on the field in the model volume. In this follow-up paper, we present the results of similar tests given field constraints of a nature that could realistically be obtained from quality white-light coronagraph images of the lower corona. We pay particular attention to difficulties associated with the line-of-sight projection of features outsidemore » of the assumed coronagraph image plane and the effect on the outcome of the optimization of errors in the localization of constraints. We find that substantial improvement in the model field can be achieved with these types of constraints, even when magnetic features in the images are located outside of the image plane.« less

Improving mass candidate detection in mammograms via feature maxima propagation and local feature selection.

PubMed

Melendez, Jaime; Sánchez, Clara I; van Ginneken, Bram; Karssemeijer, Nico

2014-08-01

Mass candidate detection is a crucial component of multistep computer-aided detection (CAD) systems. It is usually performed by combining several local features by means of a classifier. When these features are processed on a per-image-location basis (e.g., for each pixel), mismatching problems may arise while constructing feature vectors for classification, which is especially true when the behavior expected from the evaluated features is a peaked response due to the presence of a mass. In this study, two of these problems, consisting of maxima misalignment and differences of maxima spread, are identified and two solutions are proposed. The first proposed method, feature maxima propagation, reproduces feature maxima through their neighboring locations. The second method, local feature selection, combines different subsets of features for different feature vectors associated with image locations. Both methods are applied independently and together. The proposed methods are included in a mammogram-based CAD system intended for mass detection in screening. Experiments are carried out with a database of 382 digital cases. Sensitivity is assessed at two sets of operating points. The first one is the interval of 3.5-15 false positives per image (FPs/image), which is typical for mass candidate detection. The second one is 1 FP/image, which allows to estimate the quality of the mass candidate detector's output for use in subsequent steps of the CAD system. The best results are obtained when the proposed methods are applied together. In that case, the mean sensitivity in the interval of 3.5-15 FPs/image significantly increases from 0.926 to 0.958 (p < 0.0002). At the lower rate of 1 FP/image, the mean sensitivity improves from 0.628 to 0.734 (p < 0.0002). Given the improved detection performance, the authors believe that the strategies proposed in this paper can render mass candidate detection approaches based on image location classification more robust to feature discrepancies and prove advantageous not only at the candidate detection level, but also at subsequent steps of a CAD system.

Low-light-level image super-resolution reconstruction based on iterative projection photon localization algorithm

NASA Astrophysics Data System (ADS)

Ying, Changsheng; Zhao, Peng; Li, Ye

2018-01-01

The intensified charge-coupled device (ICCD) is widely used in the field of low-light-level (LLL) imaging. The LLL images captured by ICCD suffer from low spatial resolution and contrast, and the target details can hardly be recognized. Super-resolution (SR) reconstruction of LLL images captured by ICCDs is a challenging issue. The dispersion in the double-proximity-focused image intensifier is the main factor that leads to a reduction in image resolution and contrast. We divide the integration time into subintervals that are short enough to get photon images, so the overlapping effect and overstacking effect of dispersion can be eliminated. We propose an SR reconstruction algorithm based on iterative projection photon localization. In the iterative process, the photon image is sliced by projection planes, and photons are screened under the constraints of regularity. The accurate position information of the incident photons in the reconstructed SR image is obtained by the weighted centroids calculation. The experimental results show that the spatial resolution and contrast of our SR image are significantly improved.

An MRI denoising method using image data redundancy and local SNR estimation.

PubMed

Golshan, Hosein M; Hasanzadeh, Reza P R; Yousefzadeh, Shahrokh C

2013-09-01

This paper presents an LMMSE-based method for the three-dimensional (3D) denoising of MR images assuming a Rician noise model. Conventionally, the LMMSE method estimates the noise-less signal values using the observed MR data samples within local neighborhoods. This is not an efficient procedure to deal with this issue while the 3D MR data intrinsically includes many similar samples that can be used to improve the estimation results. To overcome this problem, we model MR data as random fields and establish a principled way which is capable of choosing the samples not only from a local neighborhood but also from a large portion of the given data. To follow the similar samples within the MR data, an effective similarity measure based on the local statistical moments of images is presented. The parameters of the proposed filter are automatically chosen from the estimated local signal-to-noise ratio. To further enhance the denoising performance, a recursive version of the introduced approach is also addressed. The proposed filter is compared with related state-of-the-art filters using both synthetic and real MR datasets. The experimental results demonstrate the superior performance of our proposal in removing the noise and preserving the anatomical structures of MR images. Copyright © 2013 Elsevier Inc. All rights reserved.

Rewiring the primary somatosensory cortex in carpal tunnel syndrome with acupuncture

PubMed Central

Maeda, Yumi; Kim, Hyungjun; Kettner, Norman; Kim, Jieun; Cina, Stephen; Malatesta, Cristina; Gerber, Jessica; McManus, Claire; Ong-Sutherland, Rebecca; Mezzacappa, Pia; Libby, Alexandra; Mawla, Ishtiaq; Morse, Leslie R.; Kaptchuk, Ted J.; Audette, Joseph

2017-01-01

Abstract Carpal tunnel syndrome is the most common entrapment neuropathy, affecting the median nerve at the wrist. Acupuncture is a minimally-invasive and conservative therapeutic option, and while rooted in a complex practice ritual, acupuncture overlaps significantly with many conventional peripherally-focused neuromodulatory therapies. However, the neurophysiological mechanisms by which acupuncture impacts accepted subjective/psychological and objective/physiological outcomes are not well understood. Eligible patients (n = 80, 65 female, age: 49.3 ± 8.6 years) were enrolled and randomized into three intervention arms: (i) verum electro-acupuncture ‘local’ to the more affected hand; (ii) verum electro-acupuncture at ‘distal’ body sites, near the ankle contralesional to the more affected hand; and (iii) local sham electro-acupuncture using non-penetrating placebo needles. Acupuncture therapy was provided for 16 sessions over 8 weeks. Boston Carpal Tunnel Syndrome Questionnaire assessed pain and paraesthesia symptoms at baseline, following therapy and at 3-month follow-up. Nerve conduction studies assessing median nerve sensory latency and brain imaging data were acquired at baseline and following therapy. Functional magnetic resonance imaging assessed somatotopy in the primary somatosensory cortex using vibrotactile stimulation over three digits (2, 3 and 5). While all three acupuncture interventions reduced symptom severity, verum (local and distal) acupuncture was superior to sham in producing improvements in neurophysiological outcomes, both local to the wrist (i.e. median sensory nerve conduction latency) and in the brain (i.e. digit 2/3 cortical separation distance). Moreover, greater improvement in second/third interdigit cortical separation distance following verum acupuncture predicted sustained improvements in symptom severity at 3-month follow-up. We further explored potential differential mechanisms of local versus distal acupuncture using diffusion tensor imaging of white matter microstructure adjacent to the primary somatosensory cortex. Compared to healthy adults (n = 34, 28 female, 49.7 ± 9.9 years old), patients with carpal tunnel syndrome demonstrated increased fractional anisotropy in several regions and, for these regions we found that improvement in median nerve latency was associated with reduction of fractional anisotropy near (i) contralesional hand area following verum, but not sham, acupuncture; (ii) ipsilesional hand area following local, but not distal or sham, acupuncture; and (iii) ipsilesional leg area following distal, but not local or sham, acupuncture. As these primary somatosensory cortex subregions are distinctly targeted by local versus distal acupuncture electrostimulation, acupuncture at local versus distal sites may improve median nerve function at the wrist by somatotopically distinct neuroplasticity in the primary somatosensory cortex following therapy. Our study further suggests that improvements in primary somatosensory cortex somatotopy can predict long-term clinical outcomes for carpal tunnel syndrome. PMID:28334999

Threshold Determination for Local Instantaneous Sea Surface Height Derivation with Icebridge Data in Beaufort Sea

NASA Astrophysics Data System (ADS)

Zhu, C.; Zhang, S.; Xiao, F.; Li, J.; Yuan, L.; Zhang, Y.; Zhu, T.

2018-05-01

The NASA Operation IceBridge (OIB) mission is the largest program in the Earth's polar remote sensing science observation project currently, initiated in 2009, which collects airborne remote sensing measurements to bridge the gap between NASA's ICESat and the upcoming ICESat-2 mission. This paper develop an improved method that optimizing the selection method of Digital Mapping System (DMS) image and using the optimal threshold obtained by experiments in Beaufort Sea to calculate the local instantaneous sea surface height in this area. The optimal threshold determined by comparing manual selection with the lowest (Airborne Topographic Mapper) ATM L1B elevation threshold of 2 %, 1 %, 0.5 %, 0.2 %, 0.1 % and 0.05 % in A, B, C sections, the mean of mean difference are 0.166 m, 0.124 m, 0.083 m, 0.018 m, 0.002 m and -0.034 m. Our study shows the lowest L1B data of 0.1 % is the optimal threshold. The optimal threshold and manual selections are also used to calculate the instantaneous sea surface height over images with leads, we find that improved methods has closer agreement with those from L1B manual selections. For these images without leads, the local instantaneous sea surface height estimated by using the linear equations between distance and sea surface height calculated over images with leads.

Improvement of cardiac CT reconstruction using local motion vector fields.

PubMed

Schirra, Carsten Oliver; Bontus, Claas; van Stevendaal, Udo; Dössel, Olaf; Grass, Michael

2009-03-01

The motion of the heart is a major challenge for cardiac imaging using CT. A novel approach to decrease motion blur and to improve the signal to noise ratio is motion compensated reconstruction which takes motion vector fields into account in order to correct motion. The presented work deals with the determination of local motion vector fields from high contrast objects and their utilization within motion compensated filtered back projection reconstruction. Image registration is applied during the quiescent cardiac phases. Temporal interpolation in parameter space is used in order to estimate motion during strong motion phases. The resulting motion vector fields are during image reconstruction. The method is assessed using a software phantom and several clinical cases for calcium scoring. As a criterion for reconstruction quality, calcium volume scores were derived from both, gated cardiac reconstruction and motion compensated reconstruction throughout the cardiac phases using low pitch helical cone beam CT acquisitions. The presented technique is a robust method to determine and utilize local motion vector fields. Motion compensated reconstruction using the derived motion vector fields leads to superior image quality compared to gated reconstruction. As a result, the gating window can be enlarged significantly, resulting in increased SNR, while reliable Hounsfield units are achieved due to the reduced level of motion artefacts. The enlargement of the gating window can be translated into reduced dose requirements.

Fundamental limits of reconstruction-based superresolution algorithms under local translation.

PubMed

Lin, Zhouchen; Shum, Heung-Yeung

2004-01-01

Superresolution is a technique that can produce images of a higher resolution than that of the originally captured ones. Nevertheless, improvement in resolution using such a technique is very limited in practice. This makes it significant to study the problem: "Do fundamental limits exist for superresolution?" In this paper, we focus on a major class of superresolution algorithms, called the reconstruction-based algorithms, which compute high-resolution images by simulating the image formation process. Assuming local translation among low-resolution images, this paper is the first attempt to determine the explicit limits of reconstruction-based algorithms, under both real and synthetic conditions. Based on the perturbation theory of linear systems, we obtain the superresolution limits from the conditioning analysis of the coefficient matrix. Moreover, we determine the number of low-resolution images that are sufficient to achieve the limit. Both real and synthetic experiments are carried out to verify our analysis.

Perceived visual speed constrained by image segmentation

NASA Technical Reports Server (NTRS)

Verghese, P.; Stone, L. S.

1996-01-01

Little is known about how or where the visual system parses the visual scene into objects or surfaces. However, it is generally assumed that the segmentation and grouping of pieces of the image into discrete entities is due to 'later' processing stages, after the 'early' processing of the visual image by local mechanisms selective for attributes such as colour, orientation, depth, and motion. Speed perception is also thought to be mediated by early mechanisms tuned for speed. Here we show that manipulating the way in which an image is parsed changes the way in which local speed information is processed. Manipulations that cause multiple stimuli to appear as parts of a single patch degrade speed discrimination, whereas manipulations that perceptually divide a single large stimulus into parts improve discrimination. These results indicate that processes as early as speed perception may be constrained by the parsing of the visual image into discrete entities.

Automated artifact detection and removal for improved tensor estimation in motion-corrupted DTI data sets using the combination of local binary patterns and 2D partial least squares.

PubMed

Zhou, Zhenyu; Liu, Wei; Cui, Jiali; Wang, Xunheng; Arias, Diana; Wen, Ying; Bansal, Ravi; Hao, Xuejun; Wang, Zhishun; Peterson, Bradley S; Xu, Dongrong

2011-02-01

Signal variation in diffusion-weighted images (DWIs) is influenced both by thermal noise and by spatially and temporally varying artifacts, such as rigid-body motion and cardiac pulsation. Motion artifacts are particularly prevalent when scanning difficult patient populations, such as human infants. Although some motion during data acquisition can be corrected using image coregistration procedures, frequently individual DWIs are corrupted beyond repair by sudden, large amplitude motion either within or outside of the imaging plane. We propose a novel approach to identify and reject outlier images automatically using local binary patterns (LBP) and 2D partial least square (2D-PLS) to estimate diffusion tensors robustly. This method uses an enhanced LBP algorithm to extract texture features from a local texture feature of the image matrix from the DWI data. Because the images have been transformed to local texture matrices, we are able to extract discriminating information that identifies outliers in the data set by extending a traditional one-dimensional PLS algorithm to a two-dimension operator. The class-membership matrix in this 2D-PLS algorithm is adapted to process samples that are image matrix, and the membership matrix thus represents varying degrees of importance of local information within the images. We also derive the analytic form of the generalized inverse of the class-membership matrix. We show that this method can effectively extract local features from brain images obtained from a large sample of human infants to identify images that are outliers in their textural features, permitting their exclusion from further processing when estimating tensors using the DWIs. This technique is shown to be superior in performance when compared with visual inspection and other common methods to address motion-related artifacts in DWI data. This technique is applicable to correct motion artifact in other magnetic resonance imaging (MRI) techniques (e.g., the bootstrapping estimation) that use univariate or multivariate regression methods to fit MRI data to a pre-specified model. Copyright © 2011 Elsevier Inc. All rights reserved.

Infrared Ship Target Segmentation Based on Spatial Information Improved FCM.

PubMed

Bai, Xiangzhi; Chen, Zhiguo; Zhang, Yu; Liu, Zhaoying; Lu, Yi

2016-12-01

Segmentation of infrared (IR) ship images is always a challenging task, because of the intensity inhomogeneity and noise. The fuzzy C-means (FCM) clustering is a classical method widely used in image segmentation. However, it has some shortcomings, like not considering the spatial information or being sensitive to noise. In this paper, an improved FCM method based on the spatial information is proposed for IR ship target segmentation. The improvements include two parts: 1) adding the nonlocal spatial information based on the ship target and 2) using the spatial shape information of the contour of the ship target to refine the local spatial constraint by Markov random field. In addition, the results of K -means are used to initialize the improved FCM method. Experimental results show that the improved method is effective and performs better than the existing methods, including the existing FCM methods, for segmentation of the IR ship images.

Context-aware and locality-constrained coding for image categorization.

PubMed

Xiao, Wenhua; Wang, Bin; Liu, Yu; Bao, Weidong; Zhang, Maojun

2014-01-01

Improving the coding strategy for BOF (Bag-of-Features) based feature design has drawn increasing attention in recent image categorization works. However, the ambiguity in coding procedure still impedes its further development. In this paper, we introduce a context-aware and locality-constrained Coding (CALC) approach with context information for describing objects in a discriminative way. It is generally achieved by learning a word-to-word cooccurrence prior to imposing context information over locality-constrained coding. Firstly, the local context of each category is evaluated by learning a word-to-word cooccurrence matrix representing the spatial distribution of local features in neighbor region. Then, the learned cooccurrence matrix is used for measuring the context distance between local features and code words. Finally, a coding strategy simultaneously considers locality in feature space and context space, while introducing the weight of feature is proposed. This novel coding strategy not only semantically preserves the information in coding, but also has the ability to alleviate the noise distortion of each class. Extensive experiments on several available datasets (Scene-15, Caltech101, and Caltech256) are conducted to validate the superiority of our algorithm by comparing it with baselines and recent published methods. Experimental results show that our method significantly improves the performance of baselines and achieves comparable and even better performance with the state of the arts.

Localisation of epileptic foci using novel imaging modalities

PubMed Central

De Ciantis, Alessio; Lemieux, Louis

2013-01-01

Purpose of review This review examines recent reports on the use of advanced techniques to map the regions and networks involved during focal epileptic seizure generation in humans. Recent findings A number of imaging techniques are capable of providing new localizing information on the ictal processes and epileptogenic zone. Evaluating the clinical utility of these findings has been mainly performed through post-hoc comparison with the findings of invasive EEG and ictal single-photon emission computed tomography, using postsurgical seizure reduction as the main outcome measure. Added value has been demonstrated in MRI-negative cases. Improved understanding of the human ictiogenic processes and the focus vs. network hypothesis is likely to result from the application of multimodal techniques that combine electrophysiological, semiological, and whole-brain coverage of brain activity changes. Summary On the basis of recent research in the field of neuroimaging, several novel imaging modalities have been improved and developed to provide information about the localization of epileptic foci. PMID:23823464

WE-AB-BRA-01: 3D-2D Image Registration for Target Localization in Spine Surgery: Comparison of Similarity Metrics Against Robustness to Content Mismatch

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

De Silva, T; Ketcha, M; Siewerdsen, J H

Purpose: In image-guided spine surgery, mapping 3D preoperative images to 2D intraoperative images via 3D-2D registration can provide valuable assistance in target localization. However, the presence of surgical instrumentation, hardware implants, and soft-tissue resection/displacement causes mismatches in image content, confounding existing registration methods. Manual/semi-automatic methods to mask such extraneous content is time consuming, user-dependent, error prone, and disruptive to clinical workflow. We developed and evaluated 2 novel similarity metrics within a robust registration framework to overcome such challenges in target localization. Methods: An IRB-approved retrospective study in 19 spine surgery patients included 19 preoperative 3D CT images and 50 intraoperativemore » mobile radiographs in cervical, thoracic, and lumbar spine regions. A neuroradiologist provided truth definition of vertebral positions in CT and radiography. 3D-2D registration was performed using the CMA-ES optimizer with 4 gradient-based image similarity metrics: (1) gradient information (GI); (2) gradient correlation (GC); (3) a novel variant referred to as gradient orientation (GO); and (4) a second variant referred to as truncated gradient correlation (TGC). Registration accuracy was evaluated in terms of the projection distance error (PDE) of the vertebral levels. Results: Conventional similarity metrics were susceptible to gross registration error and failure modes associated with the presence of surgical instrumentation: for GI, the median PDE and interquartile range was 33.0±43.6 mm; similarly for GC, PDE = 23.0±92.6 mm respectively. The robust metrics GO and TGC, on the other hand, demonstrated major improvement in PDE (7.6 ±9.4 mm and 8.1± 18.1 mm, respectively) and elimination of gross failure modes. Conclusion: The proposed GO and TGC similarity measures improve registration accuracy and robustness to gross failure in the presence of strong image content mismatch. Such registration capability could offer valuable assistance in target localization without disruption of clinical workflow. G. Kleinszig and S. Vogt are employees of Siemens Healthcare.« less

Multisensor Super Resolution Using Directionally-Adaptive Regularization for UAV Images

PubMed Central

Kang, Wonseok; Yu, Soohwan; Ko, Seungyong; Paik, Joonki

2015-01-01

In various unmanned aerial vehicle (UAV) imaging applications, the multisensor super-resolution (SR) technique has become a chronic problem and attracted increasing attention. Multisensor SR algorithms utilize multispectral low-resolution (LR) images to make a higher resolution (HR) image to improve the performance of the UAV imaging system. The primary objective of the paper is to develop a multisensor SR method based on the existing multispectral imaging framework instead of using additional sensors. In order to restore image details without noise amplification or unnatural post-processing artifacts, this paper presents an improved regularized SR algorithm by combining the directionally-adaptive constraints and multiscale non-local means (NLM) filter. As a result, the proposed method can overcome the physical limitation of multispectral sensors by estimating the color HR image from a set of multispectral LR images using intensity-hue-saturation (IHS) image fusion. Experimental results show that the proposed method provides better SR results than existing state-of-the-art SR methods in the sense of objective measures. PMID:26007744

Multisensor Super Resolution Using Directionally-Adaptive Regularization for UAV Images.

PubMed

Kang, Wonseok; Yu, Soohwan; Ko, Seungyong; Paik, Joonki

2015-05-22

In various unmanned aerial vehicle (UAV) imaging applications, the multisensor super-resolution (SR) technique has become a chronic problem and attracted increasing attention. Multisensor SR algorithms utilize multispectral low-resolution (LR) images to make a higher resolution (HR) image to improve the performance of the UAV imaging system. The primary objective of the paper is to develop a multisensor SR method based on the existing multispectral imaging framework instead of using additional sensors. In order to restore image details without noise amplification or unnatural post-processing artifacts, this paper presents an improved regularized SR algorithm by combining the directionally-adaptive constraints and multiscale non-local means (NLM) filter. As a result, the proposed method can overcome the physical limitation of multispectral sensors by estimating the color HR image from a set of multispectral LR images using intensity-hue-saturation (IHS) image fusion. Experimental results show that the proposed method provides better SR results than existing state-of-the-art SR methods in the sense of objective measures.

Correlation of breast image alignment using biomechanical modelling

NASA Astrophysics Data System (ADS)

Lee, Angela; Rajagopal, Vijay; Bier, Peter; Nielsen, Poul M. F.; Nash, Martyn P.

2009-02-01

Breast cancer is one of the most common causes of cancer death among women around the world. Researchers have found that a combination of imaging modalities (such as x-ray mammography, magnetic resonance, and ultrasound) leads to more effective diagnosis and management of breast cancers because each imaging modality displays different information about the breast tissues. In order to aid clinicians in interpreting the breast images from different modalities, we have developed a computational framework for generating individual-specific, 3D, finite element (FE) models of the breast. Medical images are embedded into this model, which is subsequently used to simulate the large deformations that the breasts undergo during different imaging procedures, thus warping the medical images to the deformed views of the breast in the different modalities. In this way, medical images of the breast taken in different geometric configurations (compression, gravity, etc.) can be aligned according to physically feasible transformations. In order to analyse the accuracy of the biomechanical model predictions, squared normalised cross correlation (NCC2) was used to provide both local and global comparisons of the model-warped images with clinical images of the breast subject to different gravity loaded states. The local comparison results were helpful in indicating the areas for improvement in the biomechanical model. To improve the modelling accuracy, we will need to investigate the incorporation of breast tissue heterogeneity into the model and altering the boundary conditions for the breast model. A biomechanical image registration tool of this kind will help radiologists to provide more reliable diagnosis and localisation of breast cancer.

WE-G-207-04: Non-Local Total-Variation (NLTV) Combined with Reweighted L1-Norm for Compressed Sensing Based CT Reconstruction

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

Kim, H; Chen, J; Pouliot, J

2015-06-15

Purpose: Compressed sensing (CS) has been used for CT (4DCT/CBCT) reconstruction with few projections to reduce dose of radiation. Total-variation (TV) in L1-minimization (min.) with local information is the prevalent technique in CS, while it can be prone to noise. To address the problem, this work proposes to apply a new image processing technique, called non-local TV (NLTV), to CS based CT reconstruction, and incorporate reweighted L1-norm into it for more precise reconstruction. Methods: TV minimizes intensity variations by considering two local neighboring voxels, which can be prone to noise, possibly damaging the reconstructed CT image. NLTV, contrarily, utilizes moremore » global information by computing a weight function of current voxel relative to surrounding search area. In fact, it might be challenging to obtain an optimal solution due to difficulty in defining the weight function with appropriate parameters. Introducing reweighted L1-min., designed for approximation to ideal L0-min., can reduce the dependence on defining the weight function, therefore improving accuracy of the solution. This work implemented the NLTV combined with reweighted L1-min. by Split Bregman Iterative method. For evaluation, a noisy digital phantom and a pelvic CT images are employed to compare the quality of images reconstructed by TV, NLTV and reweighted NLTV. Results: In both cases, conventional and reweighted NLTV outperform TV min. in signal-to-noise ratio (SNR) and root-mean squared errors of the reconstructed images. Relative to conventional NLTV, NLTV with reweighted L1-norm was able to slightly improve SNR, while greatly increasing the contrast between tissues due to additional iterative reweighting process. Conclusion: NLTV min. can provide more precise compressed sensing based CT image reconstruction by incorporating the reweighted L1-norm, while maintaining greater robustness to the noise effect than TV min.« less

Local-search based prediction of medical image registration error

NASA Astrophysics Data System (ADS)

Saygili, Görkem

2018-03-01

Medical image registration is a crucial task in many different medical imaging applications. Hence, considerable amount of work has been published recently that aim to predict the error in a registration without any human effort. If provided, these error predictions can be used as a feedback to the registration algorithm to further improve its performance. Recent methods generally start with extracting image-based and deformation-based features, then apply feature pooling and finally train a Random Forest (RF) regressor to predict the real registration error. Image-based features can be calculated after applying a single registration but provide limited accuracy whereas deformation-based features such as variation of deformation vector field may require up to 20 registrations which is a considerably high time-consuming task. This paper proposes to use extracted features from a local search algorithm as image-based features to estimate the error of a registration. The proposed method comprises a local search algorithm to find corresponding voxels between registered image pairs and based on the amount of shifts and stereo confidence measures, it predicts the amount of registration error in millimetres densely using a RF regressor. Compared to other algorithms in the literature, the proposed algorithm does not require multiple registrations, can be efficiently implemented on a Graphical Processing Unit (GPU) and can still provide highly accurate error predictions in existence of large registration error. Experimental results with real registrations on a public dataset indicate a substantially high accuracy achieved by using features from the local search algorithm.

Brain tumor segmentation based on local independent projection-based classification.

PubMed

Huang, Meiyan; Yang, Wei; Wu, Yao; Jiang, Jun; Chen, Wufan; Feng, Qianjin

2014-10-01

Brain tumor segmentation is an important procedure for early tumor diagnosis and radiotherapy planning. Although numerous brain tumor segmentation methods have been presented, enhancing tumor segmentation methods is still challenging because brain tumor MRI images exhibit complex characteristics, such as high diversity in tumor appearance and ambiguous tumor boundaries. To address this problem, we propose a novel automatic tumor segmentation method for MRI images. This method treats tumor segmentation as a classification problem. Additionally, the local independent projection-based classification (LIPC) method is used to classify each voxel into different classes. A novel classification framework is derived by introducing the local independent projection into the classical classification model. Locality is important in the calculation of local independent projections for LIPC. Locality is also considered in determining whether local anchor embedding is more applicable in solving linear projection weights compared with other coding methods. Moreover, LIPC considers the data distribution of different classes by learning a softmax regression model, which can further improve classification performance. In this study, 80 brain tumor MRI images with ground truth data are used as training data and 40 images without ground truth data are used as testing data. The segmentation results of testing data are evaluated by an online evaluation tool. The average dice similarities of the proposed method for segmenting complete tumor, tumor core, and contrast-enhancing tumor on real patient data are 0.84, 0.685, and 0.585, respectively. These results are comparable to other state-of-the-art methods.

Clinical evaluation of a confocal microendoscope system for imaging the ovary

NASA Astrophysics Data System (ADS)

Tanbakuchi, Anthony A.; Rouse, Andrew R.; Hatch, Kenneth D.; Sampliner, Richard E.; Udovich, Josh A.; Gmitro, Arthur F.

2008-02-01

We have developed a mobile confocal microendoscope system that provides live cellular imaging during surgery to aid in diagnosing microscopic abnormalities including cancer. We present initial clinical trial results using the device to image ovaries in-vivo using fluorescein and ex-vivo results using acridine orange. The imaging catheter has improved depth control and localized dye delivery mechanisms than previously presented. A manual control now provides a simple way for the surgeon to adjust and optimize imaging depth during the procedure while a tiny piezo valve in the imaging catheter controls the dye delivery.

Automatic localization of cochlear implant electrodes in CTs with a limited intensity range

NASA Astrophysics Data System (ADS)

Zhao, Yiyuan; Dawant, Benoit M.; Noble, Jack H.

2017-02-01

Cochlear implants (CIs) are neural prosthetics for treating severe-to-profound hearing loss. Our group has developed an image-guided cochlear implant programming (IGCIP) system that uses image analysis techniques to recommend patientspecific CI processor settings to improve hearing outcomes. One crucial step in IGCIP is the localization of CI electrodes in post-implantation CTs. Manual localization of electrodes requires time and expertise. To automate this process, our group has proposed automatic techniques that have been validated on CTs acquired with scanners that produce images with an extended range of intensity values. However, there are many clinical CTs acquired with a limited intensity range. This limitation complicates the electrode localization process. In this work, we present a pre-processing step for CTs with a limited intensity range and extend the methods we proposed for full intensity range CTs to localize CI electrodes in CTs with limited intensity range. We evaluate our method on CTs of 20 subjects implanted with CI arrays produced by different manufacturers. Our method achieves a mean localization error of 0.21mm. This indicates our method is robust for automatic localization of CI electrodes in different types of CTs, which represents a crucial step for translating IGCIP from research laboratory to clinical use.

Deformable registration of the inflated and deflated lung in cone-beam CT-guided thoracic surgery: Initial investigation of a combined model- and image-driven approach

PubMed Central

Uneri, Ali; Nithiananthan, Sajendra; Schafer, Sebastian; Otake, Yoshito; Stayman, J. Webster; Kleinszig, Gerhard; Sussman, Marc S.; Prince, Jerry L.; Siewerdsen, Jeffrey H.

2013-01-01

Purpose: Surgical resection is the preferred modality for curative treatment of early stage lung cancer, but localization of small tumors (<10 mm diameter) during surgery presents a major challenge that is likely to increase as more early-stage disease is detected incidentally and in low-dose CT screening. To overcome the difficulty of manual localization (fingers inserted through intercostal ports) and the cost, logistics, and morbidity of preoperative tagging (coil or dye placement under CT-fluoroscopy), the authors propose the use of intraoperative cone-beam CT (CBCT) and deformable image registration to guide targeting of small tumors in video-assisted thoracic surgery (VATS). A novel algorithm is reported for registration of the lung from its inflated state (prior to pleural breach) to the deflated state (during resection) to localize surgical targets and adjacent critical anatomy. Methods: The registration approach geometrically resolves images of the inflated and deflated lung using a coarse model-driven stage followed by a finer image-driven stage. The model-driven stage uses image features derived from the lung surfaces and airways: triangular surface meshes are morphed to capture bulk motion; concurrently, the airways generate graph structures from which corresponding nodes are identified. Interpolation of the sparse motion fields computed from the bounding surface and interior airways provides a 3D motion field that coarsely registers the lung and initializes the subsequent image-driven stage. The image-driven stage employs an intensity-corrected, symmetric form of the Demons method. The algorithm was validated over 12 datasets, obtained from porcine specimen experiments emulating CBCT-guided VATS. Geometric accuracy was quantified in terms of target registration error (TRE) in anatomical targets throughout the lung, and normalized cross-correlation. Variations of the algorithm were investigated to study the behavior of the model- and image-driven stages by modifying individual algorithmic steps and examining the effect in comparison to the nominal process. Results: The combined model- and image-driven registration process demonstrated accuracy consistent with the requirements of minimally invasive VATS in both target localization (∼3–5 mm within the target wedge) and critical structure avoidance (∼1–2 mm). The model-driven stage initialized the registration to within a median TRE of 1.9 mm (95% confidence interval (CI) maximum = 5.0 mm), while the subsequent image-driven stage yielded higher accuracy localization with 0.6 mm median TRE (95% CI maximum = 4.1 mm). The variations assessing the individual algorithmic steps elucidated the role of each step and in some cases identified opportunities for further simplification and improvement in computational speed. Conclusions: The initial studies show the proposed registration method to successfully register CBCT images of the inflated and deflated lung. Accuracy appears sufficient to localize the target and adjacent critical anatomy within ∼1–2 mm and guide localization under conditions in which the target cannot be discerned directly in CBCT (e.g., subtle, nonsolid tumors). The ability to directly localize tumors in the operating room could provide a valuable addition to the VATS arsenal, obviate the cost, logistics, and morbidity of preoperative tagging, and improve patient safety. Future work includes in vivo testing, optimization of workflow, and integration with a CBCT image guidance system. PMID:23298134

Deformable registration of the inflated and deflated lung in cone-beam CT-guided thoracic surgery: initial investigation of a combined model- and image-driven approach.

PubMed

Uneri, Ali; Nithiananthan, Sajendra; Schafer, Sebastian; Otake, Yoshito; Stayman, J Webster; Kleinszig, Gerhard; Sussman, Marc S; Prince, Jerry L; Siewerdsen, Jeffrey H

2013-01-01

Surgical resection is the preferred modality for curative treatment of early stage lung cancer, but localization of small tumors (<10 mm diameter) during surgery presents a major challenge that is likely to increase as more early-stage disease is detected incidentally and in low-dose CT screening. To overcome the difficulty of manual localization (fingers inserted through intercostal ports) and the cost, logistics, and morbidity of preoperative tagging (coil or dye placement under CT-fluoroscopy), the authors propose the use of intraoperative cone-beam CT (CBCT) and deformable image registration to guide targeting of small tumors in video-assisted thoracic surgery (VATS). A novel algorithm is reported for registration of the lung from its inflated state (prior to pleural breach) to the deflated state (during resection) to localize surgical targets and adjacent critical anatomy. The registration approach geometrically resolves images of the inflated and deflated lung using a coarse model-driven stage followed by a finer image-driven stage. The model-driven stage uses image features derived from the lung surfaces and airways: triangular surface meshes are morphed to capture bulk motion; concurrently, the airways generate graph structures from which corresponding nodes are identified. Interpolation of the sparse motion fields computed from the bounding surface and interior airways provides a 3D motion field that coarsely registers the lung and initializes the subsequent image-driven stage. The image-driven stage employs an intensity-corrected, symmetric form of the Demons method. The algorithm was validated over 12 datasets, obtained from porcine specimen experiments emulating CBCT-guided VATS. Geometric accuracy was quantified in terms of target registration error (TRE) in anatomical targets throughout the lung, and normalized cross-correlation. Variations of the algorithm were investigated to study the behavior of the model- and image-driven stages by modifying individual algorithmic steps and examining the effect in comparison to the nominal process. The combined model- and image-driven registration process demonstrated accuracy consistent with the requirements of minimally invasive VATS in both target localization (∼3-5 mm within the target wedge) and critical structure avoidance (∼1-2 mm). The model-driven stage initialized the registration to within a median TRE of 1.9 mm (95% confidence interval (CI) maximum = 5.0 mm), while the subsequent image-driven stage yielded higher accuracy localization with 0.6 mm median TRE (95% CI maximum = 4.1 mm). The variations assessing the individual algorithmic steps elucidated the role of each step and in some cases identified opportunities for further simplification and improvement in computational speed. The initial studies show the proposed registration method to successfully register CBCT images of the inflated and deflated lung. Accuracy appears sufficient to localize the target and adjacent critical anatomy within ∼1-2 mm and guide localization under conditions in which the target cannot be discerned directly in CBCT (e.g., subtle, nonsolid tumors). The ability to directly localize tumors in the operating room could provide a valuable addition to the VATS arsenal, obviate the cost, logistics, and morbidity of preoperative tagging, and improve patient safety. Future work includes in vivo testing, optimization of workflow, and integration with a CBCT image guidance system.

MO-FG-204-03: Using Edge-Preserving Algorithm for Significantly Improved Image-Domain Material Decomposition in Dual Energy CT

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

Zhao, W; Niu, T; Xing, L

2015-06-15

Purpose: To significantly improve dual energy CT (DECT) imaging by establishing a new theoretical framework of image-domain material decomposition with incorporation of edge-preserving techniques. Methods: The proposed algorithm, HYPR-NLM, combines the edge-preserving non-local mean filter (NLM) with the HYPR-LR (Local HighlY constrained backPRojection Reconstruction) framework. Image denoising using HYPR-LR framework depends on the noise level of the composite image which is the average of the different energy images. For DECT, the composite image is the average of high- and low-energy images. To further reduce noise, one may want to increase the window size of the filter of the HYPR-LR, leadingmore » resolution degradation. By incorporating the NLM filtering and the HYPR-LR framework, HYPR-NLM reduces the boost material decomposition noise using energy information redundancies as well as the non-local mean. We demonstrate the noise reduction and resolution preservation of the algorithm with both iodine concentration numerical phantom and clinical patient data by comparing the HYPR-NLM algorithm to the direct matrix inversion, HYPR-LR and iterative image-domain material decomposition (Iter-DECT). Results: The results show iterative material decomposition method reduces noise to the lowest level and provides improved DECT images. HYPR-NLM significantly reduces noise while preserving the accuracy of quantitative measurement and resolution. For the iodine concentration numerical phantom, the averaged noise levels are about 2.0, 0.7, 0.2 and 0.4 for direct inversion, HYPR-LR, Iter- DECT and HYPR-NLM, respectively. For the patient data, the noise levels of the water images are about 0.36, 0.16, 0.12 and 0.13 for direct inversion, HYPR-LR, Iter-DECT and HYPR-NLM, respectively. Difference images of both HYPR-LR and Iter-DECT show edge effect, while no significant edge effect is shown for HYPR-NLM, suggesting spatial resolution is well preserved for HYPR-NLM. Conclusion: HYPR-NLM provides an effective way to reduce the generic magnified image noise of dual–energy material decomposition while preserving resolution. This work is supported in part by NIH grants 7R01HL111141 and 1R01-EB016777. This work is also supported by the Natural Science Foundation of China (NSFC Grant No. 81201091), Fundamental Research Funds for the Central Universities in China, and Fund Project for Excellent Abroad Scholar Personnel in Science and Technology.« less

Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering.

PubMed

Gong, Maoguo; Zhou, Zhiqiang; Ma, Jingjing

2012-04-01

This paper presents an unsupervised distribution-free change detection approach for synthetic aperture radar (SAR) images based on an image fusion strategy and a novel fuzzy clustering algorithm. The image fusion technique is introduced to generate a difference image by using complementary information from a mean-ratio image and a log-ratio image. In order to restrain the background information and enhance the information of changed regions in the fused difference image, wavelet fusion rules based on an average operator and minimum local area energy are chosen to fuse the wavelet coefficients for a low-frequency band and a high-frequency band, respectively. A reformulated fuzzy local-information C-means clustering algorithm is proposed for classifying changed and unchanged regions in the fused difference image. It incorporates the information about spatial context in a novel fuzzy way for the purpose of enhancing the changed information and of reducing the effect of speckle noise. Experiments on real SAR images show that the image fusion strategy integrates the advantages of the log-ratio operator and the mean-ratio operator and gains a better performance. The change detection results obtained by the improved fuzzy clustering algorithm exhibited lower error than its preexistences.

Cone-beam x-ray luminescence computed tomography based on x-ray absorption dosage

NASA Astrophysics Data System (ADS)

Liu, Tianshuai; Rong, Junyan; Gao, Peng; Zhang, Wenli; Liu, Wenlei; Zhang, Yuanke; Lu, Hongbing

2018-02-01

With the advances of x-ray excitable nanophosphors, x-ray luminescence computed tomography (XLCT) has become a promising hybrid imaging modality. In particular, a cone-beam XLCT (CB-XLCT) system has demonstrated its potential in in vivo imaging with the advantage of fast imaging speed over other XLCT systems. Currently, the imaging models of most XLCT systems assume that nanophosphors emit light based on the intensity distribution of x-ray within the object, not completely reflecting the nature of the x-ray excitation process. To improve the imaging quality of CB-XLCT, an imaging model that adopts an excitation model of nanophosphors based on x-ray absorption dosage is proposed in this study. To solve the ill-posed inverse problem, a reconstruction algorithm that combines the adaptive Tikhonov regularization method with the imaging model is implemented for CB-XLCT reconstruction. Numerical simulations and phantom experiments indicate that compared with the traditional forward model based on x-ray intensity, the proposed dose-based model could improve the image quality of CB-XLCT significantly in terms of target shape, localization accuracy, and image contrast. In addition, the proposed model behaves better in distinguishing closer targets, demonstrating its advantage in improving spatial resolution.

Toward optimal spatial and spectral quality in widefield infrared spectromicroscopy of IR labelled single cells.

PubMed

Mattson, Eric C; Unger, Miriam; Clède, Sylvain; Lambert, François; Policar, Clotilde; Imtiaz, Asher; D'Souza, Roshan; Hirschmugl, Carol J

2013-10-07

Advancements in widefield infrared spectromicroscopy have recently been demonstrated following the commissioning of IRENI (InfraRed ENvironmental Imaging), a Fourier Transform infrared (FTIR) chemical imaging beamline at the Synchrotron Radiation Center. The present study demonstrates the effects of magnification, spatial oversampling, spectral pre-processing and deconvolution, focusing on the intracellular detection and distribution of an exogenous metal tris-carbonyl derivative 1 in a single MDA-MB-231 breast cancer cell. We demonstrate here that spatial oversampling for synchrotron-based infrared imaging is critical to obtain accurate diffraction-limited images at all wavelengths simultaneously. Resolution criteria and results from raw and deconvoluted images for two Schwarzschild objectives (36×, NA 0.5 and 74×, NA 0.65) are compared to each other and to prior reports for raster-scanned, confocal microscopes. The resolution of the imaging data can be improved by deconvolving the instrumental broadening that is determined with the measured PSFs, which is implemented with GPU programming architecture for fast hyperspectral processing. High definition, rapidly acquired, FTIR chemical images of respective spectral signatures of the cell 1 and shows that 1 is localized next to the phosphate- and Amide-rich regions, in agreement with previous infrared and luminescence studies. The infrared image contrast, localization and definition are improved after applying proven spectral pre-processing (principal component analysis based noise reduction and RMie scattering correction algorithms) to individual pixel spectra in the hyperspectral cube.

Non-rigid registration between 3D ultrasound and CT images of the liver based on intensity and gradient information

NASA Astrophysics Data System (ADS)

Lee, Duhgoon; Nam, Woo Hyun; Lee, Jae Young; Ra, Jong Beom

2011-01-01

In order to utilize both ultrasound (US) and computed tomography (CT) images of the liver concurrently for medical applications such as diagnosis and image-guided intervention, non-rigid registration between these two types of images is an essential step, as local deformation between US and CT images exists due to the different respiratory phases involved and due to the probe pressure that occurs in US imaging. This paper introduces a voxel-based non-rigid registration algorithm between the 3D B-mode US and CT images of the liver. In the proposed algorithm, to improve the registration accuracy, we utilize the surface information of the liver and gallbladder in addition to the information of the vessels inside the liver. For an effective correlation between US and CT images, we treat those anatomical regions separately according to their characteristics in US and CT images. Based on a novel objective function using a 3D joint histogram of the intensity and gradient information, vessel-based non-rigid registration is followed by surface-based non-rigid registration in sequence, which improves the registration accuracy. The proposed algorithm is tested for ten clinical datasets and quantitative evaluations are conducted. Experimental results show that the registration error between anatomical features of US and CT images is less than 2 mm on average, even with local deformation due to different respiratory phases and probe pressure. In addition, the lesion registration error is less than 3 mm on average with a maximum of 4.5 mm that is considered acceptable for clinical applications.

A novel SPECT camera for molecular imaging of the prostate

NASA Astrophysics Data System (ADS)

Cebula, Alan; Gilland, David; Su, Li-Ming; Wagenaar, Douglas; Bahadori, Amir

2011-10-01

The objective of this work is to develop an improved SPECT camera for dedicated prostate imaging. Complementing the recent advancements in agents for molecular prostate imaging, this device has the potential to assist in distinguishing benign from aggressive cancers, to improve site-specific localization of cancer, to improve accuracy of needle-guided prostate biopsy of cancer sites, and to aid in focal therapy procedures such as cryotherapy and radiation. Theoretical calculations show that the spatial resolution/detection sensitivity of the proposed SPECT camera can rival or exceed 3D PET and further signal-to-noise advantage is attained with the better energy resolution of the CZT modules. Based on photon transport simulation studies, the system has a reconstructed spatial resolution of 4.8 mm with a sensitivity of 0.0001. Reconstruction of a simulated prostate distribution demonstrates the focal imaging capability of the system.

Wavelets in medical imaging

NASA Astrophysics Data System (ADS)

Zahra, Noor e.; Sevindir, Hulya Kodal; Aslan, Zafer; Siddiqi, A. H.

2012-07-01

The aim of this study is to provide emerging applications of wavelet methods to medical signals and images, such as electrocardiogram, electroencephalogram, functional magnetic resonance imaging, computer tomography, X-ray and mammography. Interpretation of these signals and images are quite important. Nowadays wavelet methods have a significant impact on the science of medical imaging and the diagnosis of disease and screening protocols. Based on our initial investigations, future directions include neurosurgical planning and improved assessment of risk for individual patients, improved assessment and strategies for the treatment of chronic pain, improved seizure localization, and improved understanding of the physiology of neurological disorders. We look ahead to these and other emerging applications as the benefits of this technology become incorporated into current and future patient care. In this chapter by applying Fourier transform and wavelet transform, analysis and denoising of one of the important biomedical signals like EEG is carried out. The presence of rhythm, template matching, and correlation is discussed by various method. Energy of EEG signal is used to detect seizure in an epileptic patient. We have also performed denoising of EEG signals by SWT.

Localization of lung fields in HRCT images using a deep convolution neural network

NASA Astrophysics Data System (ADS)

Kumar, Abhishek; Agarwala, Sunita; Dhara, Ashis Kumar; Mukhopadhyay, Sudipta; Nandi, Debashis; Garg, Mandeep; Khandelwal, Niranjan; Kalra, Naveen

2018-02-01

Lung field segmentation is a prerequisite step for the development of a computer-aided diagnosis system for interstitial lung diseases observed in chest HRCT images. Conventional methods of lung field segmentation rely on a large gray value contrast between lung fields and surrounding tissues. These methods fail on lung HRCT images with dense and diffused pathology. An efficient prepro- cessing could improve the accuracy of segmentation of pathological lung field in HRCT images. In this paper, a convolution neural network is used for localization of lung fields in HRCT images. The proposed method provides an optimal bounding box enclosing the lung fields irrespective of the presence of diffuse pathology. The performance of the proposed algorithm is validated on 330 lung HRCT images obtained from MedGift database on ZF and VGG networks. The model achieves a mean average precision of 0.94 with ZF net and a slightly better performance giving a mean average precision of 0.95 in case of VGG net.

Large deformation image classification using generalized locality-constrained linear coding.

PubMed

Zhang, Pei; Wee, Chong-Yaw; Niethammer, Marc; Shen, Dinggang; Yap, Pew-Thian

2013-01-01

Magnetic resonance (MR) imaging has been demonstrated to be very useful for clinical diagnosis of Alzheimer's disease (AD). A common approach to using MR images for AD detection is to spatially normalize the images by non-rigid image registration, and then perform statistical analysis on the resulting deformation fields. Due to the high nonlinearity of the deformation field, recent studies suggest to use initial momentum instead as it lies in a linear space and fully encodes the deformation field. In this paper we explore the use of initial momentum for image classification by focusing on the problem of AD detection. Experiments on the public ADNI dataset show that the initial momentum, together with a simple sparse coding technique-locality-constrained linear coding (LLC)--can achieve a classification accuracy that is comparable to or even better than the state of the art. We also show that the performance of LLC can be greatly improved by introducing proper weights to the codebook.

Local intensity area descriptor for facial recognition in ideal and noise conditions

NASA Astrophysics Data System (ADS)

Tran, Chi-Kien; Tseng, Chin-Dar; Chao, Pei-Ju; Ting, Hui-Min; Chang, Liyun; Huang, Yu-Jie; Lee, Tsair-Fwu

2017-03-01

We propose a local texture descriptor, local intensity area descriptor (LIAD), which is applied for human facial recognition in ideal and noisy conditions. Each facial image is divided into small regions from which LIAD histograms are extracted and concatenated into a single feature vector to represent the facial image. The recognition is performed using a nearest neighbor classifier with histogram intersection and chi-square statistics as dissimilarity measures. Experiments were conducted with LIAD using the ORL database of faces (Olivetti Research Laboratory, Cambridge), the Face94 face database, the Georgia Tech face database, and the FERET database. The results demonstrated the improvement in accuracy of our proposed descriptor compared to conventional descriptors [local binary pattern (LBP), uniform LBP, local ternary pattern, histogram of oriented gradients, and local directional pattern]. Moreover, the proposed descriptor was less sensitive to noise and had low histogram dimensionality. Thus, it is expected to be a powerful texture descriptor that can be used for various computer vision problems.

The Research of Feature Extraction Method of Liver Pathological Image Based on Multispatial Mapping and Statistical Properties

PubMed Central

Liu, Huiling; Xia, Bingbing; Yi, Dehui

2016-01-01

We propose a new feature extraction method of liver pathological image based on multispatial mapping and statistical properties. For liver pathological images of Hematein Eosin staining, the image of R and B channels can reflect the sensitivity of liver pathological images better, while the entropy space and Local Binary Pattern (LBP) space can reflect the texture features of the image better. To obtain the more comprehensive information, we map liver pathological images to the entropy space, LBP space, R space, and B space. The traditional Higher Order Local Autocorrelation Coefficients (HLAC) cannot reflect the overall information of the image, so we propose an average correction HLAC feature. We calculate the statistical properties and the average gray value of pathological images and then update the current pixel value as the absolute value of the difference between the current pixel gray value and the average gray value, which can be more sensitive to the gray value changes of pathological images. Lastly the HLAC template is used to calculate the features of the updated image. The experiment results show that the improved features of the multispatial mapping have the better classification performance for the liver cancer. PMID:27022407

Interactive local super-resolution reconstruction of whole-body MRI mouse data: a pilot study with applications to bone and kidney metastases.

PubMed

Dzyubachyk, Oleh; Khmelinskii, Artem; Plenge, Esben; Kok, Peter; Snoeks, Thomas J A; Poot, Dirk H J; Löwik, Clemens W G M; Botha, Charl P; Niessen, Wiro J; van der Weerd, Louise; Meijering, Erik; Lelieveldt, Boudewijn P F

2014-01-01

In small animal imaging studies, when the locations of the micro-structures of interest are unknown a priori, there is a simultaneous need for full-body coverage and high resolution. In MRI, additional requirements to image contrast and acquisition time will often make it impossible to acquire such images directly. Recently, a resolution enhancing post-processing technique called super-resolution reconstruction (SRR) has been demonstrated to improve visualization and localization of micro-structures in small animal MRI by combining multiple low-resolution acquisitions. However, when the field-of-view is large relative to the desired voxel size, solving the SRR problem becomes very expensive, in terms of both memory requirements and computation time. In this paper we introduce a novel local approach to SRR that aims to overcome the computational problems and allow researchers to efficiently explore both global and local characteristics in whole-body small animal MRI. The method integrates state-of-the-art image processing techniques from the areas of articulated atlas-based segmentation, planar reformation, and SRR. A proof-of-concept is provided with two case studies involving CT, BLI, and MRI data of bone and kidney tumors in a mouse model. We show that local SRR-MRI is a computationally efficient complementary imaging modality for the precise characterization of tumor metastases, and that the method provides a feasible high-resolution alternative to conventional MRI.

Neurologic 3D MR Spectroscopic Imaging with Low-Power Adiabatic Pulses and Fast Spiral Acquisition

PubMed Central

Gagoski, Borjan A.; Sorensen, A. Gregory

2012-01-01

Purpose: To improve clinical three-dimensional (3D) MR spectroscopic imaging with more accurate localization and faster acquisition schemes. Materials and Methods: Institutional review board approval and patient informed consent were obtained. Data were acquired with a 3-T MR imager and a 32-channel head coil in phantoms, five healthy volunteers, and five patients with glioblastoma. Excitation was performed with localized adiabatic spin-echo refocusing (LASER) by using adiabatic gradient-offset independent adiabaticity wideband uniform rate and smooth truncation (GOIA-W[16,4]) pulses with 3.5-msec duration, 20-kHz bandwidth, 0.81-kHz amplitude, and 45-msec echo time. Interleaved constant-density spirals simultaneously encoded one frequency and two spatial dimensions. Conventional phase encoding (PE) (1-cm3 voxels) was performed after LASER excitation and was the reference standard. Spectra acquired with spiral encoding at similar and higher spatial resolution and with shorter imaging time were compared with those acquired with PE. Metabolite levels were fitted with software, and Bland-Altman analysis was performed. Results: Clinical 3D MR spectroscopic images were acquired four times faster with spiral protocols than with the elliptical PE protocol at low spatial resolution (1 cm3). Higher-spatial-resolution images (0.39 cm3) were acquired twice as fast with spiral protocols compared with the low-spatial-resolution elliptical PE protocol. A minimum signal-to-noise ratio (SNR) of 5 was obtained with spiral protocols under these conditions and was considered clinically adequate to reliably distinguish metabolites from noise. The apparent SNR loss was not linear with decreasing voxel sizes because of longer local T2* times. Improvement of spectral line width from 4.8 Hz to 3.5 Hz was observed at high spatial resolution. The Bland-Altman agreement between spiral and PE data is characterized by narrow 95% confidence intervals for their differences (0.12, 0.18 of their means). GOIA-W(16,4) pulses minimize chemical-shift displacement error to 2.1%, reduce nonuniformity of excitation to 5%, and eliminate the need for outer volume suppression. Conclusion: The proposed adiabatic spiral 3D MR spectroscopic imaging sequence can be performed in a standard clinical MR environment. Improvements in image quality and imaging time could enable more routine acquisition of spectroscopic data than is possible with current pulse sequences. © RSNA, 2011 PMID:22187628

Security Analysis of Image Encryption Based on Gyrator Transform by Searching the Rotation Angle with Improved PSO Algorithm.

PubMed

Sang, Jun; Zhao, Jun; Xiang, Zhili; Cai, Bin; Xiang, Hong

2015-08-05

Gyrator transform has been widely used for image encryption recently. For gyrator transform-based image encryption, the rotation angle used in the gyrator transform is one of the secret keys. In this paper, by analyzing the properties of the gyrator transform, an improved particle swarm optimization (PSO) algorithm was proposed to search the rotation angle in a single gyrator transform. Since the gyrator transform is continuous, it is time-consuming to exhaustedly search the rotation angle, even considering the data precision in a computer. Therefore, a computational intelligence-based search may be an alternative choice. Considering the properties of severe local convergence and obvious global fluctuations of the gyrator transform, an improved PSO algorithm was proposed to be suitable for such situations. The experimental results demonstrated that the proposed improved PSO algorithm can significantly improve the efficiency of searching the rotation angle in a single gyrator transform. Since gyrator transform is the foundation of image encryption in gyrator transform domains, the research on the method of searching the rotation angle in a single gyrator transform is useful for further study on the security of such image encryption algorithms.

Metric Learning for Hyperspectral Image Segmentation

NASA Technical Reports Server (NTRS)

Bue, Brian D.; Thompson, David R.; Gilmore, Martha S.; Castano, Rebecca

2011-01-01

We present a metric learning approach to improve the performance of unsupervised hyperspectral image segmentation. Unsupervised spatial segmentation can assist both user visualization and automatic recognition of surface features. Analysts can use spatially-continuous segments to decrease noise levels and/or localize feature boundaries. However, existing segmentation methods use tasks-agnostic measures of similarity. Here we learn task-specific similarity measures from training data, improving segment fidelity to classes of interest. Multiclass Linear Discriminate Analysis produces a linear transform that optimally separates a labeled set of training classes. The defines a distance metric that generalized to a new scenes, enabling graph-based segmentation that emphasizes key spectral features. We describe tests based on data from the Compact Reconnaissance Imaging Spectrometer (CRISM) in which learned metrics improve segment homogeneity with respect to mineralogical classes.

Electroencephalographic inverse localization of brain activity in acute traumatic brain injury as a guide to surgery, monitoring and treatment

PubMed Central

Irimia, Andrei; Goh, S.-Y. Matthew; Torgerson, Carinna M.; Stein, Nathan R.; Chambers, Micah C.; Vespa, Paul M.; Van Horn, John D.

2013-01-01

Objective To inverse-localize epileptiform cortical electrical activity recorded from severe traumatic brain injury (TBI) patients using electroencephalography (EEG). Methods Three acute TBI cases were imaged using computed tomography (CT) and multimodal magnetic resonance imaging (MRI). Semi-automatic segmentation was performed to partition the complete TBI head into 25 distinct tissue types, including 6 tissue types accounting for pathology. Segmentations were employed to generate a finite element method model of the head, and EEG activity generators were modeled as dipolar currents distributed over the cortical surface. Results We demonstrate anatomically faithful localization of EEG generators responsible for epileptiform discharges in severe TBI. By accounting for injury-related tissue conductivity changes, our work offers the most realistic implementation currently available for the inverse estimation of cortical activity in TBI. Conclusion Whereas standard localization techniques are available for electrical activity mapping in uninjured brains, they are rarely applied to acute TBI. Modern models of TBI-induced pathology can inform the localization of epileptogenic foci, improve surgical efficacy, contribute to the improvement of critical care monitoring and provide guidance for patient-tailored treatment. With approaches such as this, neurosurgeons and neurologists can study brain activity in acute TBI and obtain insights regarding injury effects upon brain metabolism and clinical outcome. PMID:24011495

Electroencephalographic inverse localization of brain activity in acute traumatic brain injury as a guide to surgery, monitoring and treatment.

PubMed

Irimia, Andrei; Goh, S-Y Matthew; Torgerson, Carinna M; Stein, Nathan R; Chambers, Micah C; Vespa, Paul M; Van Horn, John D

2013-10-01

To inverse-localize epileptiform cortical electrical activity recorded from severe traumatic brain injury (TBI) patients using electroencephalography (EEG). Three acute TBI cases were imaged using computed tomography (CT) and multimodal magnetic resonance imaging (MRI). Semi-automatic segmentation was performed to partition the complete TBI head into 25 distinct tissue types, including 6 tissue types accounting for pathology. Segmentations were employed to generate a finite element method model of the head, and EEG activity generators were modeled as dipolar currents distributed over the cortical surface. We demonstrate anatomically faithful localization of EEG generators responsible for epileptiform discharges in severe TBI. By accounting for injury-related tissue conductivity changes, our work offers the most realistic implementation currently available for the inverse estimation of cortical activity in TBI. Whereas standard localization techniques are available for electrical activity mapping in uninjured brains, they are rarely applied to acute TBI. Modern models of TBI-induced pathology can inform the localization of epileptogenic foci, improve surgical efficacy, contribute to the improvement of critical care monitoring and provide guidance for patient-tailored treatment. With approaches such as this, neurosurgeons and neurologists can study brain activity in acute TBI and obtain insights regarding injury effects upon brain metabolism and clinical outcome. Published by Elsevier B.V.

iROLL: does 3-D radioguided occult lesion localization improve surgical management in early-stage breast cancer?

PubMed

Bluemel, Christina; Cramer, Andreas; Grossmann, Christoph; Kajdi, Georg W; Malzahn, Uwe; Lamp, Nora; Langen, Heinz-Jakob; Schmid, Jan; Buck, Andreas K; Grimminger, Hanns-Jörg; Herrmann, Ken

2015-10-01

To prospectively evaluate the feasibility of 3-D radioguided occult lesion localization (iROLL) and to compare iROLL with wire-guided localization (WGL) in patients with early-stage breast cancer undergoing breast-conserving surgery and sentinel lymph node biopsy (SLNB). WGL (standard procedure) and iROLL in combination with SLNB were performed in 31 women (mean age 65.1 ± 11.2 years) with early-stage breast cancer and clinically negative axillae. Patient comfort in respect of both methods was assessed using a ten point scale. SLNB and iROLL were guided by freehand SPECT (fhSPECT). The results of the novel 3-D image-based method were compared with those of WGL, ultrasound-based lesion localization, and histopathology. iROLL successfully detected the malignant primary and at least one sentinel lymph node in 97% of patients. In a single patient (3%), only iROLL, and not WGL, enabled lesion localization. The variability between fhSPECT and ultrasound-based depth localization of breast lesions was low (1.2 ± 1.4 mm). Clear margins were achieved in 81% of the patients; however, precise prediction of clear histopathological surgical margins was not feasible using iROLL. Patients rated iROLL as less painful than WGL with a pain score 0.8 ± 1.2 points (p < 0.01) lower than the score for iROLL. iROLL is a well-tolerated and feasible technique for localizing early-stage breast cancer in the course of breast-conserving surgery, and is a suitable replacement for WGL. As a single image-based procedure for localization of breast lesions and sentinel nodes, iROLL may improve the entire surgical procedure. However, no advantages of the image-guided procedure were found with regard to prediction of complete tumour resection.

High-dose MVCT image guidance for stereotactic body radiation therapy

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

Westerly, David C.; Schefter, Tracey E.; Kavanagh, Brian D.

Purpose: Stereotactic body radiation therapy (SBRT) is a potent treatment for early stage primary and limited metastatic disease. Accurate tumor localization is essential to administer SBRT safely and effectively. Tomotherapy combines helical IMRT with onboard megavoltage CT (MVCT) imaging and is well suited for SBRT; however, MVCT results in reduced soft tissue contrast and increased image noise compared with kilovoltage CT. The goal of this work was to investigate the use of increased imaging doses on a clinical tomotherapy machine to improve image quality for SBRT image guidance. Methods: Two nonstandard, high-dose imaging modes were created on a tomotherapy machinemore » by increasing the linear accelerator (LINAC) pulse rate from the nominal setting of 80 Hz, to 160 Hz and 300 Hz, respectively. Weighted CT dose indexes (wCTDIs) were measured for the standard, medium, and high-dose modes in a 30 cm solid water phantom using a calibrated A1SL ion chamber. Image quality was assessed from scans of a customized image quality phantom. Metrics evaluated include: contrast-to-noise ratios (CNRs), high-contrast spatial resolution, image uniformity, and percent image noise. In addition, two patients receiving SBRT were localized using high-dose MVCT scans. Raw detector data collected after each scan were used to reconstruct standard-dose images for comparison. Results: MVCT scans acquired using a pitch of 1.0 resulted in wCTDI values of 2.2, 4.7, and 8.5 cGy for the standard, medium, and high-dose modes respectively. CNR values for both low and high-contrast materials were found to increase with the square root of dose. Axial high-contrast spatial resolution was comparable for all imaging modes at 0.5 lp/mm. Image uniformity was improved and percent noise decreased as the imaging dose increased. Similar improvements in image quality were observed in patient images, with decreases in image noise being the most notable. Conclusions: High-dose imaging modes are made possible on a clinical tomotherapy machine by increasing the LINAC pulse rate. Increasing the imaging dose results in increased CNRs; making it easier to distinguish the boundaries of low contrast objects. The imaging dose levels observed in this work are considered acceptable at our institution for SBRT treatments delivered in 3-5 fractions.« less

High-dose MVCT image guidance for stereotactic body radiation therapy.

PubMed

Westerly, David C; Schefter, Tracey E; Kavanagh, Brian D; Chao, Edward; Lucas, Dan; Flynn, Ryan T; Miften, Moyed

2012-08-01

Stereotactic body radiation therapy (SBRT) is a potent treatment for early stage primary and limited metastatic disease. Accurate tumor localization is essential to administer SBRT safely and effectively. Tomotherapy combines helical IMRT with onboard megavoltage CT (MVCT) imaging and is well suited for SBRT; however, MVCT results in reduced soft tissue contrast and increased image noise compared with kilovoltage CT. The goal of this work was to investigate the use of increased imaging doses on a clinical tomotherapy machine to improve image quality for SBRT image guidance. Two nonstandard, high-dose imaging modes were created on a tomotherapy machine by increasing the linear accelerator (LINAC) pulse rate from the nominal setting of 80 Hz, to 160 Hz and 300 Hz, respectively. Weighted CT dose indexes (wCTDIs) were measured for the standard, medium, and high-dose modes in a 30 cm solid water phantom using a calibrated A1SL ion chamber. Image quality was assessed from scans of a customized image quality phantom. Metrics evaluated include: contrast-to-noise ratios (CNRs), high-contrast spatial resolution, image uniformity, and percent image noise. In addition, two patients receiving SBRT were localized using high-dose MVCT scans. Raw detector data collected after each scan were used to reconstruct standard-dose images for comparison. MVCT scans acquired using a pitch of 1.0 resulted in wCTDI values of 2.2, 4.7, and 8.5 cGy for the standard, medium, and high-dose modes respectively. CNR values for both low and high-contrast materials were found to increase with the square root of dose. Axial high-contrast spatial resolution was comparable for all imaging modes at 0.5 lp∕mm. Image uniformity was improved and percent noise decreased as the imaging dose increased. Similar improvements in image quality were observed in patient images, with decreases in image noise being the most notable. High-dose imaging modes are made possible on a clinical tomotherapy machine by increasing the LINAC pulse rate. Increasing the imaging dose results in increased CNRs; making it easier to distinguish the boundaries of low contrast objects. The imaging dose levels observed in this work are considered acceptable at our institution for SBRT treatments delivered in 3-5 fractions.

Toward Intraoperative Image-Guided Transoral Robotic Surgery

PubMed Central

Liu, Wen P.; Reaugamornrat, Sureerat; Deguet, Anton; Sorger, Jonathan M.; Siewerdsen, Jeffrey H.; Richmon, Jeremy; Taylor, Russell H.

2014-01-01

This paper presents the development and evaluation of video augmentation on the stereoscopic da Vinci S system with intraoperative image guidance for base of tongue tumor resection in transoral robotic surgery (TORS). Proposed workflow for image-guided TORS begins by identifying and segmenting critical oropharyngeal structures (e.g., the tumor and adjacent arteries and nerves) from preoperative computed tomography (CT) and/or magnetic resonance (MR) imaging. These preoperative planned data can be deformably registered to the intraoperative endoscopic view using mobile C-arm cone-beam computed tomography (CBCT) [1, 2]. Augmentation of TORS endoscopic video defining surgical targets and critical structures has the potential to improve navigation, spatial orientation, and confidence in tumor resection. Experiments in animal specimens achieved statistically significant improvement in target localization error when comparing the proposed image guidance system to simulated current practice. PMID:25525474

Deep Learning Nuclei Detection in Digitized Histology Images by Superpixels.

PubMed

Sornapudi, Sudhir; Stanley, Ronald Joe; Stoecker, William V; Almubarak, Haidar; Long, Rodney; Antani, Sameer; Thoma, George; Zuna, Rosemary; Frazier, Shelliane R

2018-01-01

Advances in image analysis and computational techniques have facilitated automatic detection of critical features in histopathology images. Detection of nuclei is critical for squamous epithelium cervical intraepithelial neoplasia (CIN) classification into normal, CIN1, CIN2, and CIN3 grades. In this study, a deep learning (DL)-based nuclei segmentation approach is investigated based on gathering localized information through the generation of superpixels using a simple linear iterative clustering algorithm and training with a convolutional neural network. The proposed approach was evaluated on a dataset of 133 digitized histology images and achieved an overall nuclei detection (object-based) accuracy of 95.97%, with demonstrated improvement over imaging-based and clustering-based benchmark techniques. The proposed DL-based nuclei segmentation Method with superpixel analysis has shown improved segmentation results in comparison to state-of-the-art methods.

Confidence estimation for quantitative photoacoustic imaging

NASA Astrophysics Data System (ADS)

Gröhl, Janek; Kirchner, Thomas; Maier-Hein, Lena

2018-02-01

Quantification of photoacoustic (PA) images is one of the major challenges currently being addressed in PA research. Tissue properties can be quantified by correcting the recorded PA signal with an estimation of the corresponding fluence. Fluence estimation itself, however, is an ill-posed inverse problem which usually needs simplifying assumptions to be solved with state-of-the-art methods. These simplifications, as well as noise and artifacts in PA images reduce the accuracy of quantitative PA imaging (PAI). This reduction in accuracy is often localized to image regions where the assumptions do not hold true. This impedes the reconstruction of functional parameters when averaging over entire regions of interest (ROI). Averaging over a subset of voxels with a high accuracy would lead to an improved estimation of such parameters. To achieve this, we propose a novel approach to the local estimation of confidence in quantitative reconstructions of PA images. It makes use of conditional probability densities to estimate confidence intervals alongside the actual quantification. It encapsulates an estimation of the errors introduced by fluence estimation as well as signal noise. We validate the approach using Monte Carlo generated data in combination with a recently introduced machine learning-based approach to quantitative PAI. Our experiments show at least a two-fold improvement in quantification accuracy when evaluating on voxels with high confidence instead of thresholding signal intensity.

Leveraging the Cloud for Robust and Efficient Lunar Image Processing

NASA Technical Reports Server (NTRS)

Chang, George; Malhotra, Shan; Wolgast, Paul

2011-01-01

The Lunar Mapping and Modeling Project (LMMP) is tasked to aggregate lunar data, from the Apollo era to the latest instruments on the LRO spacecraft, into a central repository accessible by scientists and the general public. A critical function of this task is to provide users with the best solution for browsing the vast amounts of imagery available. The image files LMMP manages range from a few gigabytes to hundreds of gigabytes in size with new data arriving every day. Despite this ever-increasing amount of data, LMMP must make the data readily available in a timely manner for users to view and analyze. This is accomplished by tiling large images into smaller images using Hadoop, a distributed computing software platform implementation of the MapReduce framework, running on a small cluster of machines locally. Additionally, the software is implemented to use Amazon's Elastic Compute Cloud (EC2) facility. We also developed a hybrid solution to serve images to users by leveraging cloud storage using Amazon's Simple Storage Service (S3) for public data while keeping private information on our own data servers. By using Cloud Computing, we improve upon our local solution by reducing the need to manage our own hardware and computing infrastructure, thereby reducing costs. Further, by using a hybrid of local and cloud storage, we are able to provide data to our users more efficiently and securely. 12 This paper examines the use of a distributed approach with Hadoop to tile images, an approach that provides significant improvements in image processing time, from hours to minutes. This paper describes the constraints imposed on the solution and the resulting techniques developed for the hybrid solution of a customized Hadoop infrastructure over local and cloud resources in managing this ever-growing data set. It examines the performance trade-offs of using the more plentiful resources of the cloud, such as those provided by S3, against the bandwidth limitations such use encounters with remote resources. As part of this discussion this paper will outline some of the technologies employed, the reasons for their selection, the resulting performance metrics and the direction the project is headed based upon the demonstrated capabilities thus far.

Prostate Brachytherapy Seed Reconstruction with Gaussian Blurring and Optimal Coverage Cost

PubMed Central

Lee, Junghoon; Liu, Xiaofeng; Jain, Ameet K.; Song, Danny Y.; Burdette, E. Clif; Prince, Jerry L.; Fichtinger, Gabor

2009-01-01

Intraoperative dosimetry in prostate brachytherapy requires localization of the implanted radioactive seeds. A tomosynthesis-based seed reconstruction method is proposed. A three-dimensional volume is reconstructed from Gaussian-blurred projection images and candidate seed locations are computed from the reconstructed volume. A false positive seed removal process, formulated as an optimal coverage problem, iteratively removes “ghost” seeds that are created by tomosynthesis reconstruction. In an effort to minimize pose errors that are common in conventional C-arms, initial pose parameter estimates are iteratively corrected by using the detected candidate seeds as fiducials, which automatically “focuses” the collected images and improves successive reconstructed volumes. Simulation results imply that the implanted seed locations can be estimated with a detection rate of ≥ 97.9% and ≥ 99.3% from three and four images, respectively, when the C-arm is calibrated and the pose of the C-arm is known. The algorithm was also validated on phantom data sets successfully localizing the implanted seeds from four or five images. In a Phase-1 clinical trial, we were able to localize the implanted seeds from five intraoperative fluoroscopy images with 98.8% (STD=1.6) overall detection rate. PMID:19605321

Pneumothorax detection in chest radiographs using local and global texture signatures

NASA Astrophysics Data System (ADS)

Geva, Ofer; Zimmerman-Moreno, Gali; Lieberman, Sivan; Konen, Eli; Greenspan, Hayit

2015-03-01

A novel framework for automatic detection of pneumothorax abnormality in chest radiographs is presented. The suggested method is based on a texture analysis approach combined with supervised learning techniques. The proposed framework consists of two main steps: at first, a texture analysis process is performed for detection of local abnormalities. Labeled image patches are extracted in the texture analysis procedure following which local analysis values are incorporated into a novel global image representation. The global representation is used for training and detection of the abnormality at the image level. The presented global representation is designed based on the distinctive shape of the lung, taking into account the characteristics of typical pneumothorax abnormalities. A supervised learning process was performed on both the local and global data, leading to trained detection system. The system was tested on a dataset of 108 upright chest radiographs. Several state of the art texture feature sets were experimented with (Local Binary Patterns, Maximum Response filters). The optimal configuration yielded sensitivity of 81% with specificity of 87%. The results of the evaluation are promising, establishing the current framework as a basis for additional improvements and extensions.

The review and results of different methods for facial recognition

NASA Astrophysics Data System (ADS)

Le, Yifan

2017-09-01

In recent years, facial recognition draws much attention due to its wide potential applications. As a unique technology in Biometric Identification, facial recognition represents a significant improvement since it could be operated without cooperation of people under detection. Hence, facial recognition will be taken into defense system, medical detection, human behavior understanding, etc. Several theories and methods have been established to make progress in facial recognition: (1) A novel two-stage facial landmark localization method is proposed which has more accurate facial localization effect under specific database; (2) A statistical face frontalization method is proposed which outperforms state-of-the-art methods for face landmark localization; (3) It proposes a general facial landmark detection algorithm to handle images with severe occlusion and images with large head poses; (4) There are three methods proposed on Face Alignment including shape augmented regression method, pose-indexed based multi-view method and a learning based method via regressing local binary features. The aim of this paper is to analyze previous work of different aspects in facial recognition, focusing on concrete method and performance under various databases. In addition, some improvement measures and suggestions in potential applications will be put forward.

Computational efficiency improvements for image colorization

NASA Astrophysics Data System (ADS)

Yu, Chao; Sharma, Gaurav; Aly, Hussein

2013-03-01

We propose an efficient algorithm for colorization of greyscale images. As in prior work, colorization is posed as an optimization problem: a user specifies the color for a few scribbles drawn on the greyscale image and the color image is obtained by propagating color information from the scribbles to surrounding regions, while maximizing the local smoothness of colors. In this formulation, colorization is obtained by solving a large sparse linear system, which normally requires substantial computation and memory resources. Our algorithm improves the computational performance through three innovations over prior colorization implementations. First, the linear system is solved iteratively without explicitly constructing the sparse matrix, which significantly reduces the required memory. Second, we formulate each iteration in terms of integral images obtained by dynamic programming, reducing repetitive computation. Third, we use a coarseto- fine framework, where a lower resolution subsampled image is first colorized and this low resolution color image is upsampled to initialize the colorization process for the fine level. The improvements we develop provide significant speedup and memory savings compared to the conventional approach of solving the linear system directly using off-the-shelf sparse solvers, and allow us to colorize images with typical sizes encountered in realistic applications on typical commodity computing platforms.

An Improved Text Localization Method for Natural Scene Images

NASA Astrophysics Data System (ADS)

Jiang, Mengdi; Cheng, Jianghua; Chen, Minghui; Ku, Xishu

2018-01-01

In order to extract text information effectively from natural scene image with complex background, multi-orientation perspective and multilingual languages, we present a new method based on the improved Stroke Feature Transform (SWT). Firstly, The Maximally Stable Extremal Region (MSER) method is used to detect text candidate regions. Secondly, the SWT algorithm is used in the candidate regions, which can improve the edge detection compared with tradition SWT method. Finally, the Frequency-tuned (FT) visual saliency is introduced to remove non-text candidate regions. The experiment results show that, the method can achieve good robustness for complex background with multi-orientation perspective, various characters and font sizes.

Integration of co-localized glandular morphometry and protein biomarker expression in immunofluorescent images for prostate cancer prognosis

NASA Astrophysics Data System (ADS)

Scott, Richard; Khan, Faisal M.; Zeineh, Jack; Donovan, Michael; Fernandez, Gerardo

2015-03-01

Immunofluorescent (IF) image analysis of tissue pathology has proven to be extremely valuable and robust in developing prognostic assessments of disease, particularly in prostate cancer. There have been significant advances in the literature in quantitative biomarker expression as well as characterization of glandular architectures in discrete gland rings. However, while biomarker and glandular morphometric features have been combined as separate predictors in multivariate models, there is a lack of integrative features for biomarkers co-localized within specific morphological sub-types; for example the evaluation of androgen receptor (AR) expression within Gleason 3 glands only. In this work we propose a novel framework employing multiple techniques to generate integrated metrics of morphology and biomarker expression. We demonstrate the utility of the approaches in predicting clinical disease progression in images from 326 prostate biopsies and 373 prostatectomies. Our proposed integrative approaches yield significant improvements over existing IF image feature metrics. This work presents some of the first algorithms for generating innovative characteristics in tissue diagnostics that integrate co-localized morphometry and protein biomarker expression.

Localization Using Visual Odometry and a Single Downward-Pointing Camera

NASA Technical Reports Server (NTRS)

Swank, Aaron J.

2012-01-01

Stereo imaging is a technique commonly employed for vision-based navigation. For such applications, two images are acquired from different vantage points and then compared using transformations to extract depth information. The technique is commonly used in robotics for obstacle avoidance or for Simultaneous Localization And Mapping, (SLAM). Yet, the process requires a number of image processing steps and therefore tends to be CPU-intensive, which limits the real-time data rate and use in power-limited applications. Evaluated here is a technique where a monocular camera is used for vision-based odometry. In this work, an optical flow technique with feature recognition is performed to generate odometry measurements. The visual odometry sensor measurements are intended to be used as control inputs or measurements in a sensor fusion algorithm using low-cost MEMS based inertial sensors to provide improved localization information. Presented here are visual odometry results which demonstrate the challenges associated with using ground-pointing cameras for visual odometry. The focus is for rover-based robotic applications for localization within GPS-denied environments.

Using dynamic programming to improve fiducial marker localization

NASA Astrophysics Data System (ADS)

Wan, Hanlin; Ge, Jiajia; Parikh, Parag

2014-04-01

Fiducial markers are used in a wide range of medical imaging applications. In radiation therapy, they are often implanted near tumors and used as motion surrogates that are tracked with fluoroscopy. We propose a novel and robust method based on dynamic programming (DP) for retrospectively localizing radiopaque fiducial markers in fluoroscopic images. Our method was compared to template matching (TM) algorithms on 407 data sets from 24 patients. We found that the performance of TM varied dramatically depending on the template used (ranging from 47% to 92% of data sets with a mean error <1 mm). DP by itself requires no template and performed as well as the best TM method, localizing the markers in 91% of the data sets with a mean error <1 mm. Finally, by combining DP and TM, we were able to localize the markers in 99% of the data sets with a mean error <1 mm, regardless of the template used. Our results show that DP can be a powerful tool for analyzing tumor motion, capable of accurately locating fiducial markers in fluoroscopic images regardless of marker type, shape, and size.

A Dynamic Graph Cuts Method with Integrated Multiple Feature Maps for Segmenting Kidneys in 2D Ultrasound Images.

PubMed

Zheng, Qiang; Warner, Steven; Tasian, Gregory; Fan, Yong

2018-02-12

Automatic segmentation of kidneys in ultrasound (US) images remains a challenging task because of high speckle noise, low contrast, and large appearance variations of kidneys in US images. Because texture features may improve the US image segmentation performance, we propose a novel graph cuts method to segment kidney in US images by integrating image intensity information and texture feature maps. We develop a new graph cuts-based method to segment kidney US images by integrating original image intensity information and texture feature maps extracted using Gabor filters. To handle large appearance variation within kidney images and improve computational efficiency, we build a graph of image pixels close to kidney boundary instead of building a graph of the whole image. To make the kidney segmentation robust to weak boundaries, we adopt localized regional information to measure similarity between image pixels for computing edge weights to build the graph of image pixels. The localized graph is dynamically updated and the graph cuts-based segmentation iteratively progresses until convergence. Our method has been evaluated based on kidney US images of 85 subjects. The imaging data of 20 randomly selected subjects were used as training data to tune parameters of the image segmentation method, and the remaining data were used as testing data for validation. Experiment results demonstrated that the proposed method obtained promising segmentation results for bilateral kidneys (average Dice index = 0.9446, average mean distance = 2.2551, average specificity = 0.9971, average accuracy = 0.9919), better than other methods under comparison (P < .05, paired Wilcoxon rank sum tests). The proposed method achieved promising performance for segmenting kidneys in two-dimensional US images, better than segmentation methods built on any single channel of image information. This method will facilitate extraction of kidney characteristics that may predict important clinical outcomes such as progression of chronic kidney disease. Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

SU-C-201-03: Coded Aperture Gamma-Ray Imaging Using Pixelated Semiconductor Detectors

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

Joshi, S; Kaye, W; Jaworski, J

2015-06-15

Purpose: Improved localization of gamma-ray emissions from radiotracers is essential to the progress of nuclear medicine. Polaris is a portable, room-temperature operated gamma-ray imaging spectrometer composed of two 3×3 arrays of thick CdZnTe (CZT) detectors, which detect gammas between 30keV and 3MeV with energy resolution of <1% FWHM at 662keV. Compton imaging is used to map out source distributions in 4-pi space; however, is only effective above 300keV where Compton scatter is dominant. This work extends imaging to photoelectric energies (<300keV) using coded aperture imaging (CAI), which is essential for localization of Tc-99m (140keV). Methods: CAI, similar to the pinholemore » camera, relies on an attenuating mask, with open/closed elements, placed between the source and position-sensitive detectors. Partial attenuation of the source results in a “shadow” or count distribution that closely matches a portion of the mask pattern. Ideally, each source direction corresponds to a unique count distribution. Using backprojection reconstruction, the source direction is determined within the field of view. The knowledge of 3D position of interaction results in improved image quality. Results: Using a single array of detectors, a coded aperture mask, and multiple Co-57 (122keV) point sources, image reconstruction is performed in real-time, on an event-by-event basis, resulting in images with an angular resolution of ∼6 degrees. Although material nonuniformities contribute to image degradation, the superposition of images from individual detectors results in improved SNR. CAI was integrated with Compton imaging for a seamless transition between energy regimes. Conclusion: For the first time, CAI has been applied to thick, 3D position sensitive CZT detectors. Real-time, combined CAI and Compton imaging is performed using two 3×3 detector arrays, resulting in a source distribution in space. This system has been commercialized by H3D, Inc. and is being acquired for various applications worldwide, including proton therapy imaging R&D.« less

Robust registration of sparsely sectioned histology to ex-vivo MRI of temporal lobe resections

NASA Astrophysics Data System (ADS)

Goubran, Maged; Khan, Ali R.; Crukley, Cathie; Buchanan, Susan; Santyr, Brendan; deRibaupierre, Sandrine; Peters, Terry M.

2012-02-01

Surgical resection of epileptic foci is a typical treatment for drug-resistant epilepsy, however, accurate preoperative localization is challenging and often requires invasive sub-dural or intra-cranial electrode placement. The presence of cellular abnormalities in the resected tissue can be used to validate the effectiveness of multispectralMagnetic Resonance Imaging (MRI) in pre-operative foci localization and surgical planning. If successful, these techniques can lead to improved surgical outcomes and less invasive procedures. Towards this goal, a novel pipeline is presented here for post-operative imaging of temporal lobe specimens involving MRI and digital histology, and present and evaluate methods for bringing these images into spatial correspondence. The sparsely-sectioned histology images of resected tissue represents a challenge for 3D reconstruction which we address with a combined 3D and 2D rigid registration algorithm that alternates between slice-based and volume-based registration with the ex-vivo MRI. We also evaluate four methods for non-rigid within-plane registration using both images and fiducials, with the top performing method resulting in a target registration error of 0.87 mm. This work allows for the spatially-local comparison of histology with post-operative MRI and paves the way for eventual registration with pre-operative MRI images.

Low-noise heterodyne receiver for electron cyclotron emission imaging and microwave imaging reflectometry

NASA Astrophysics Data System (ADS)

Tobias, B.; Domier, C. W.; Luhmann, N. C.; Luo, C.; Mamidanna, M.; Phan, T.; Pham, A.-V.; Wang, Y.

2016-11-01

The critical component enabling electron cyclotron emission imaging (ECEI) and microwave imaging reflectometry (MIR) to resolve 2D and 3D electron temperature and density perturbations is the heterodyne imaging array that collects and downconverts radiated emission and/or reflected signals (50-150 GHz) to an intermediate frequency (IF) band (e.g. 0.1-18 GHz) that can be transmitted by a shielded coaxial cable for further filtering and detection. New circuitry has been developed for this task, integrating gallium arsenide (GaAs) monolithic microwave integrated circuits (MMICs) mounted on a liquid crystal polymer (LCP) substrate. The improved topology significantly increases electromagnetic shielding from out-of-band interference, leads to 10× improvement in the signal-to-noise ratio, and dramatic cost savings through integration. The current design, optimized for reflectometry and edge radiometry on mid-sized tokamaks, has demonstrated >20 dB conversion gain in upper V-band (60-75 GHz). Implementation of the circuit in a multi-channel electron cyclotron emission imaging (ECEI) array will improve the diagnosis of edge-localized modes and fluctuations of the high-confinement, or H-mode, pedestal.

Low-noise heterodyne receiver for electron cyclotron emission imaging and microwave imaging reflectometry.

PubMed

Tobias, B; Domier, C W; Luhmann, N C; Luo, C; Mamidanna, M; Phan, T; Pham, A-V; Wang, Y

2016-11-01

The critical component enabling electron cyclotron emission imaging (ECEI) and microwave imaging reflectometry (MIR) to resolve 2D and 3D electron temperature and density perturbations is the heterodyne imaging array that collects and downconverts radiated emission and/or reflected signals (50-150 GHz) to an intermediate frequency (IF) band (e.g. 0.1-18 GHz) that can be transmitted by a shielded coaxial cable for further filtering and detection. New circuitry has been developed for this task, integrating gallium arsenide (GaAs) monolithic microwave integrated circuits (MMICs) mounted on a liquid crystal polymer (LCP) substrate. The improved topology significantly increases electromagnetic shielding from out-of-band interference, leads to 10× improvement in the signal-to-noise ratio, and dramatic cost savings through integration. The current design, optimized for reflectometry and edge radiometry on mid-sized tokamaks, has demonstrated >20 dB conversion gain in upper V-band (60-75 GHz). Implementation of the circuit in a multi-channel electron cyclotron emission imaging (ECEI) array will improve the diagnosis of edge-localized modes and fluctuations of the high-confinement, or H-mode, pedestal.

Comparison between infrared optical and stereoscopic X-ray technologies for patient setup in image guided stereotactic radiotherapy.

PubMed

Tagaste, Barbara; Riboldi, Marco; Spadea, Maria F; Bellante, Simone; Baroni, Guido; Cambria, Raffaella; Garibaldi, Cristina; Ciocca, Mario; Catalano, Gianpiero; Alterio, Daniela; Orecchia, Roberto

2012-04-01

To compare infrared (IR) optical vs. stereoscopic X-ray technologies for patient setup in image-guided stereotactic radiotherapy. Retrospective data analysis of 233 fractions in 127 patients treated with hypofractionated stereotactic radiotherapy was performed. Patient setup at the linear accelerator was carried out by means of combined IR optical localization and stereoscopic X-ray image fusion in 6 degrees of freedom (6D). Data were analyzed to evaluate the geometric and dosimetric discrepancy between the two patient setup strategies. Differences between IR optical localization and 6D X-ray image fusion parameters were on average within the expected localization accuracy, as limited by CT image resolution (3 mm). A disagreement between the two systems below 1 mm in all directions was measured in patients treated for cranial tumors. In extracranial sites, larger discrepancies and higher variability were observed as a function of the initial patient alignment. The compensation of IR-detected rotational errors resulted in a significantly improved agreement with 6D X-ray image fusion. On the basis of the bony anatomy registrations, the measured differences were found not to be sensitive to patient breathing. The related dosimetric analysis showed that IR-based patient setup caused limited variations in three cases, with 7% maximum dose reduction in the clinical target volume and no dose increase in organs at risk. In conclusion, patient setup driven by IR external surrogates localization in 6D featured comparable accuracy with respect to procedures based on stereoscopic X-ray imaging. Copyright © 2012 Elsevier Inc. All rights reserved.

An improved robust blind motion de-blurring algorithm for remote sensing images

NASA Astrophysics Data System (ADS)

He, Yulong; Liu, Jin; Liang, Yonghui

2016-10-01

Shift-invariant motion blur can be modeled as a convolution of the true latent image and the blur kernel with additive noise. Blind motion de-blurring estimates a sharp image from a motion blurred image without the knowledge of the blur kernel. This paper proposes an improved edge-specific motion de-blurring algorithm which proved to be fit for processing remote sensing images. We find that an inaccurate blur kernel is the main factor to the low-quality restored images. To improve image quality, we do the following contributions. For the robust kernel estimation, first, we adapt the multi-scale scheme to make sure that the edge map could be constructed accurately; second, an effective salient edge selection method based on RTV (Relative Total Variation) is used to extract salient structure from texture; third, an alternative iterative method is introduced to perform kernel optimization, in this step, we adopt l1 and l0 norm as the priors to remove noise and ensure the continuity of blur kernel. For the final latent image reconstruction, an improved adaptive deconvolution algorithm based on TV-l2 model is used to recover the latent image; we control the regularization weight adaptively in different region according to the image local characteristics in order to preserve tiny details and eliminate noise and ringing artifacts. Some synthetic remote sensing images are used to test the proposed algorithm, and results demonstrate that the proposed algorithm obtains accurate blur kernel and achieves better de-blurring results.

Multi-modality 3D breast imaging with X-Ray tomosynthesis and automated ultrasound.

PubMed

Sinha, Sumedha P; Roubidoux, Marilyn A; Helvie, Mark A; Nees, Alexis V; Goodsitt, Mitchell M; LeCarpentier, Gerald L; Fowlkes, J Brian; Chalek, Carl L; Carson, Paul L

2007-01-01

This study evaluated the utility of 3D automated ultrasound in conjunction with 3D digital X-Ray tomosynthesis for breast cancer detection and assessment, to better localize and characterize lesions in the breast. Tomosynthesis image volumes and automated ultrasound image volumes were acquired in the same geometry and in the same view for 27 patients. 3 MQSA certified radiologists independently reviewed the image volumes, visually correlating the images from the two modalities with in-house software. More sophisticated software was used on a smaller set of 10 cases, which enabled the radiologist to draw a 3D box around the suspicious lesion in one image set and isolate an anatomically correlated, similarly boxed region in the other modality image set. In the primary study, correlation was found to be moderately useful to the readers. In the additional study, using improved software, the median usefulness rating increased and confidence in localizing and identifying the suspicious mass increased in more than half the cases. As automated scanning and reading software techniques advance, superior results are expected.

[Rational imaging in locally advanced prostate cancer].

PubMed

Beissert, M; Lorenz, R; Gerharz, E W

2008-11-01

Prostate cancer is one of the principal medical problems facing the male population in developed countries with an increasing need for sophisticated imaging techniques and risk-adapted treatment options. This article presents an overview of the current imaging procedures in the diagnosis of locally advanced prostate cancer. Apart from conventional gray-scale transrectal ultrasound (TRUS) as the most frequently used primary imaging modality we describe computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). CT and MRI not only allow assessment of prostate anatomy but also a specific evaluation of the pelvic region. Color-coded and contrast-enhanced ultrasound, real-time elastography, dynamic contrast enhancement in MR imaging, diffusion imaging, and MR spectroscopy may lead to a clinically relevant improvement in the diagnosis of prostate cancer. While bone scintigraphy with (99m)Tc-bisphosphonates is still the method of choice in the evaluation of bone metastasis, whole-body MRI and PET using (18)F-NaF, (18)F-FDG, (11)C-choline, (11)C-acetate, and (18)F-choline as tracers achieve higher sensitivities.

Denoised and texture enhanced MVCT to improve soft tissue conspicuity

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

Sheng, Ke, E-mail: ksheng@mednet.ucla.edu; Qi, Sharon X.; Gou, Shuiping

Purpose: MVCT images have been used in TomoTherapy treatment to align patients based on bony anatomies but its usefulness for soft tissue registration, delineation, and adaptive radiation therapy is limited due to insignificant photoelectric interaction components and the presence of noise resulting from low detector quantum efficiency of megavoltage x-rays. Algebraic reconstruction with sparsity regularizers as well as local denoising methods has not significantly improved the soft tissue conspicuity. The authors aim to utilize a nonlocal means denoising method and texture enhancement to recover the soft tissue information in MVCT (DeTECT). Methods: A block matching 3D (BM3D) algorithm was adaptedmore » to reduce the noise while keeping the texture information of the MVCT images. Following imaging denoising, a saliency map was created to further enhance visual conspicuity of low contrast structures. In this study, BM3D and saliency maps were applied to MVCT images of a CT imaging quality phantom, a head and neck, and four prostate patients. Following these steps, the contrast-to-noise ratios (CNRs) were quantified. Results: By applying BM3D denoising and saliency map, postprocessed MVCT images show remarkable improvements in imaging contrast without compromising resolution. For the head and neck patient, the difficult-to-see lymph nodes and vein in the carotid space in the original MVCT image became conspicuous in DeTECT. For the prostate patients, the ambiguous boundary between the bladder and the prostate in the original MVCT was clarified. The CNRs of phantom low contrast inserts were improved from 1.48 and 3.8 to 13.67 and 16.17, respectively. The CNRs of two regions-of-interest were improved from 1.5 and 3.17 to 3.14 and 15.76, respectively, for the head and neck patient. DeTECT also increased the CNR of prostate from 0.13 to 1.46 for the four prostate patients. The results are substantially better than a local denoising method using anisotropic diffusion. Conclusions: The authors showed that it is feasible to extract more soft tissue contrast information from the noisy MVCT images using a nonlocal means 3D block matching method in combination with saliency maps, revealing information that was originally unperceivable to human observers.« less

Blind restoration of retinal images degraded by space-variant blur with adaptive blur estimation

NASA Astrophysics Data System (ADS)

Marrugo, Andrés. G.; Millán, María. S.; Å orel, Michal; Å roubek, Filip

2013-11-01

Retinal images are often degraded with a blur that varies across the field view. Because traditional deblurring algorithms assume the blur to be space-invariant they typically fail in the presence of space-variant blur. In this work we consider the blur to be both unknown and space-variant. To carry out the restoration, we assume that in small regions the space-variant blur can be approximated by a space-invariant point-spread function (PSF). However, instead of deblurring the image on a per-patch basis, we extend individual PSFs by linear interpolation and perform a global restoration. Because the blind estimation of local PSFs may fail we propose a strategy for the identification of valid local PSFs and perform interpolation to obtain the space-variant PSF. The method was tested on artificial and real degraded retinal images. Results show significant improvement in the visibility of subtle details like small blood vessels.

Disjunctive Normal Shape and Appearance Priors with Applications to Image Segmentation.

PubMed

Mesadi, Fitsum; Cetin, Mujdat; Tasdizen, Tolga

2015-10-01

The use of appearance and shape priors in image segmentation is known to improve accuracy; however, existing techniques have several drawbacks. Active shape and appearance models require landmark points and assume unimodal shape and appearance distributions. Level set based shape priors are limited to global shape similarity. In this paper, we present a novel shape and appearance priors for image segmentation based on an implicit parametric shape representation called disjunctive normal shape model (DNSM). DNSM is formed by disjunction of conjunctions of half-spaces defined by discriminants. We learn shape and appearance statistics at varying spatial scales using nonparametric density estimation. Our method can generate a rich set of shape variations by locally combining training shapes. Additionally, by studying the intensity and texture statistics around each discriminant of our shape model, we construct a local appearance probability map. Experiments carried out on both medical and natural image datasets show the potential of the proposed method.

Radiological and Radionuclide Imaging of Degenerative Disease of the Facet Joints

PubMed Central

Shur, Natalie; Corrigan, Alexis; Agrawal, Kanhaiyalal; Desai, Amidevi; Gnanasegaran, Gopinath

2015-01-01

The facet joint has been increasingly implicated as a potential source of lower back pain. Diagnosis can be challenging as there is not a direct correlation between facet joint disease and clinical or radiological features. The purpose of this article is to review the diagnosis, treatment, and current imaging modality options in the context of degenerative facet joint disease. We describe each modality in turn with a pictorial review using current evidence. Newer hybrid imaging techniques such as single photon emission computed tomography/computed tomography (SPECT/CT) provide additional information relative to the historic gold standard magnetic resonance imaging. The diagnostic benefits of SPECT/CT include precise localization and characterization of spinal lesions and improved diagnosis for lower back pain. It may have a role in selecting patients for local therapeutic injections, as well as guiding their location with increased precision. PMID:26170560

A user's guide to localization-based super-resolution fluorescence imaging.

PubMed

Dempsey, Graham T

2013-01-01

Advances in far-field fluorescence microscopy over the past decade have led to the development of super-resolution imaging techniques that provide more than an order of magnitude improvement in spatial resolution compared to conventional light microscopy. One such approach, called Stochastic Optical Reconstruction Microscopy (STORM) uses the sequential, nanometer-scale localization of individual fluorophores to reconstruct a high-resolution image of a structure of interest. This is an attractive method for biological investigation at the nanoscale due to its relative simplicity, both conceptually and practically in the laboratory. Like most research tools, however, the devil is in the details. The aim of this chapter is to serve as a guide for applying STORM to the study of biological samples. This chapter will discuss considerations for choosing a photoswitchable fluorescent probe, preparing a sample, selecting hardware for data acquisition, and collecting and analyzing data for image reconstruction. Copyright © 2013 Elsevier Inc. All rights reserved.

Face recognition algorithm based on Gabor wavelet and locality preserving projections

NASA Astrophysics Data System (ADS)

Liu, Xiaojie; Shen, Lin; Fan, Honghui

2017-07-01

In order to solve the effects of illumination changes and differences of personal features on the face recognition rate, this paper presents a new face recognition algorithm based on Gabor wavelet and Locality Preserving Projections (LPP). The problem of the Gabor filter banks with high dimensions was solved effectively, and also the shortcoming of the LPP on the light illumination changes was overcome. Firstly, the features of global image information were achieved, which used the good spatial locality and orientation selectivity of Gabor wavelet filters. Then the dimensions were reduced by utilizing the LPP, which well-preserved the local information of the image. The experimental results shown that this algorithm can effectively extract the features relating to facial expressions, attitude and other information. Besides, it can reduce influence of the illumination changes and the differences in personal features effectively, which improves the face recognition rate to 99.2%.

A magnetic resonance (MR) microscopy system using a microfluidically cryo-cooled planar coil.

PubMed

Koo, Chiwan; Godley, Richard F; Park, Jaewon; McDougall, Mary P; Wright, Steven M; Han, Arum

2011-07-07

We present the development of a microfluidically cryo-cooled planar coil for magnetic resonance (MR) microscopy. Cryogenically cooling radiofrequency (RF) coils for magnetic resonance imaging (MRI) can improve the signal to noise ratio (SNR) of the experiment. Conventional cryostats typically use a vacuum gap to keep samples to be imaged, especially biological samples, at or near room temperature during cryo-cooling. This limits how close a cryo-cooled coil can be placed to the sample. At the same time, a small coil-to-sample distance significantly improves the MR imaging capability due to the limited imaging depth of planar MR microcoils. These two conflicting requirements pose challenges to the use of cryo-cooling in MR microcoils. The use of a microfluidic based cryostat for localized cryo-cooling of MR microcoils is a step towards eliminating these constraints. The system presented here consists of planar receive-only coils with integrated cryo-cooling microfluidic channels underneath, and an imaging surface on top of the planar coils separated by a thin nitrogen gas gap. Polymer microfluidic channel structures fabricated through soft lithography processes were used to flow liquid nitrogen under the coils in order to cryo-cool the planar coils to liquid nitrogen temperature (-196 °C). Two unique features of the cryo-cooling system minimize the distance between the coil and the sample: (1) the small dimension of the polymer microfluidic channel enables localized cooling of the planar coils, while minimizing thermal effects on the nearby imaging surface. (2) The imaging surface is separated from the cryo-cooled planar coil by a thin gap through which nitrogen gas flows to thermally insulate the imaging surface, keeping it above 0 °C and preventing potential damage to biological samples. The localized cooling effect was validated by simulations, bench testing, and MR imaging experiments. Using this cryo-cooled planar coil system inside a 4.7 Tesla MR system resulted in an average image SNR enhancement of 1.47 ± 0.11 times relative to similar room-temperature coils. This journal is © The Royal Society of Chemistry 2011

A Magnetic Resonance (MR) Microscopy System using a Microfluidically Cryo-Cooled Planar Coil

PubMed Central

Koo, Chiwan; Godley, Richard F.; Park, Jaewon; McDougall, Mary P.; Wright, Steven M.; Han, Arum

2011-01-01

We present the development of a microfluidically cryo-cooled planar coil for magnetic resonance (MR) microscopy. Cryogenically cooling radiofrequency (RF) coils for magnetic resonance imaging (MRI) can improve the signal to noise ratio (SNR) of the experiment. Conventional cryostats typically use a vacuum gap to keep samples to be imaged, especially biological samples, at or near room temperature during cryo-cooling. This limits how close a cryo-cooled coil can be placed to the sample. At the same time, a small coil-to-sample distance significantly improves the MR imaging capability due to the limited imaging depth of planar MR microcoils. These two conflicting requirements pose challenges to the use of cryo-cooling in MR microcoils. The use of a microfluidic based cryostat for localized cryo-cooling of MR microcoils is a step towards eliminating these constraints. The system presented here consists of planar receive-only coils with integrated cryo-cooling microfluidic channels underneath, and an imaging surface on top of the planar coils separated by a thin nitrogen gas gap. Polymer microfluidic channel structures fabricated through soft lithography processes were used to flow liquid nitrogen under the coils in order to cryo-cool the planar coils to liquid nitrogen temperature (−196°C). Two unique features of the cryo-cooling system minimize the distance between the coil and the sample: 1) The small dimension of the polymer microfluidic channel enables localized cooling of the planar coils, while minimizing thermal effects on the nearby imaging surface. 2) The imaging surface is separated from the cryo-cooled planar coil by a thin gap through which nitrogen gas flows to thermally insulate the imaging surface, keeping it above 0°C and preventing potential damage to biological samples. The localized cooling effect was validated by simulations, bench testing, and MR imaging experiments. Using this cryo-cooled planar coil system inside a 4.7 Tesla MR system resulted in an average image SNR enhancement of 1.47 ± 0.11 times relative to similar room-temperature coils. PMID:21603723

A novel method of the image processing on irregular triangular meshes

NASA Astrophysics Data System (ADS)

Vishnyakov, Sergey; Pekhterev, Vitaliy; Sokolova, Elizaveta

2018-04-01

The paper describes a novel method of the image processing based on irregular triangular meshes implementation. The triangular mesh is adaptive to the image content, least mean square linear approximation is proposed for the basic interpolation within the triangle. It is proposed to use triangular numbers to simplify using of the local (barycentric) coordinates for the further analysis - triangular element of the initial irregular mesh is to be represented through the set of the four equilateral triangles. This allows to use fast and simple pixels indexing in local coordinates, e.g. "for" or "while" loops for access to the pixels. Moreover, representation proposed allows to use discrete cosine transform of the simple "rectangular" symmetric form without additional pixels reordering (as it is used for shape-adaptive DCT forms). Furthermore, this approach leads to the simple form of the wavelet transform on triangular mesh. The results of the method application are presented. It is shown that advantage of the method proposed is a combination of the flexibility of the image-adaptive irregular meshes with the simple form of the pixel indexing in local triangular coordinates and the using of the common forms of the discrete transforms for triangular meshes. Method described is proposed for the image compression, pattern recognition, image quality improvement, image search and indexing. It also may be used as a part of video coding (intra-frame or inter-frame coding, motion detection).

Spiking Cortical Model Based Multimodal Medical Image Fusion by Combining Entropy Information with Weber Local Descriptor

PubMed Central

Zhang, Xuming; Ren, Jinxia; Huang, Zhiwen; Zhu, Fei

2016-01-01

Multimodal medical image fusion (MIF) plays an important role in clinical diagnosis and therapy. Existing MIF methods tend to introduce artifacts, lead to loss of image details or produce low-contrast fused images. To address these problems, a novel spiking cortical model (SCM) based MIF method has been proposed in this paper. The proposed method can generate high-quality fused images using the weighting fusion strategy based on the firing times of the SCM. In the weighting fusion scheme, the weight is determined by combining the entropy information of pulse outputs of the SCM with the Weber local descriptor operating on the firing mapping images produced from the pulse outputs. The extensive experiments on multimodal medical images show that compared with the numerous state-of-the-art MIF methods, the proposed method can preserve image details very well and avoid the introduction of artifacts effectively, and thus it significantly improves the quality of fused images in terms of human vision and objective evaluation criteria such as mutual information, edge preservation index, structural similarity based metric, fusion quality index, fusion similarity metric and standard deviation. PMID:27649190

Spiking Cortical Model Based Multimodal Medical Image Fusion by Combining Entropy Information with Weber Local Descriptor.

PubMed

Zhang, Xuming; Ren, Jinxia; Huang, Zhiwen; Zhu, Fei

2016-09-15

Multimodal medical image fusion (MIF) plays an important role in clinical diagnosis and therapy. Existing MIF methods tend to introduce artifacts, lead to loss of image details or produce low-contrast fused images. To address these problems, a novel spiking cortical model (SCM) based MIF method has been proposed in this paper. The proposed method can generate high-quality fused images using the weighting fusion strategy based on the firing times of the SCM. In the weighting fusion scheme, the weight is determined by combining the entropy information of pulse outputs of the SCM with the Weber local descriptor operating on the firing mapping images produced from the pulse outputs. The extensive experiments on multimodal medical images show that compared with the numerous state-of-the-art MIF methods, the proposed method can preserve image details very well and avoid the introduction of artifacts effectively, and thus it significantly improves the quality of fused images in terms of human vision and objective evaluation criteria such as mutual information, edge preservation index, structural similarity based metric, fusion quality index, fusion similarity metric and standard deviation.

Volume of interest CBCT and tube current modulation for image guidance using dynamic kV collimation

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

Parsons, David, E-mail: david.parsons@dal.ca, E-mail: james.robar@nshealth.ca; Robar, James L., E-mail: david.parsons@dal.ca, E-mail: james.robar@nshealth.ca

2016-04-15

Purpose: The focus of this work is the development of a novel blade collimation system enabling volume of interest (VOI) CBCT with tube current modulation using the kV image guidance source on a linear accelerator. Advantages of the system are assessed, particularly with regard to reduction and localization of dose and improvement of image quality. Methods: A four blade dynamic kV collimator was developed to track a VOI during a CBCT acquisition. The current prototype is capable of tracking an arbitrary volume defined by the treatment planner for subsequent CBCT guidance. During gantry rotation, the collimator tracks the VOI withmore » adjustment of position and dimension. CBCT image quality was investigated as a function of collimator dimension, while maintaining the same dose to the VOI, for a 22.2 cm diameter cylindrical water phantom with a 9 mm diameter bone insert centered on isocenter. Dose distributions were modeled using a dynamic BEAMnrc library and DOSXYZnrc. The resulting VOI dose distributions were compared to full-field CBCT distributions to quantify dose reduction and localization to the target volume. A novel method of optimizing x-ray tube current during CBCT acquisition was developed and assessed with regard to contrast-to-noise ratio (CNR) and imaging dose. Results: Measurements show that the VOI CBCT method using the dynamic blade system yields an increase in contrast-to-noise ratio by a factor of approximately 2.2. Depending upon the anatomical site, dose was reduced to 15%–80% of the full-field CBCT value along the central axis plane and down to less than 1% out of plane. The use of tube current modulation allowed for specification of a desired SNR within projection data. For approximately the same dose to the VOI, CNR was further increased by a factor of 1.2 for modulated VOI CBCT, giving a combined improvement of 2.6 compared to full-field CBCT. Conclusions: The present dynamic blade system provides significant improvements in CNR for the same imaging dose and localization of imaging dose to a predefined volume of interest. The approach is compatible with tube current modulation, allowing optimization of the imaging protocol.« less

3D skin surface reconstruction from a single image by merging global curvature and local texture using the guided filtering for 3D haptic palpation.

PubMed

Lee, K; Kim, M; Kim, K

2018-05-11

Skin surface evaluation has been studied using various imaging techniques. However, all these studies had limited impact because they were performed using visual exam only. To improve on this scenario with haptic feedback, we propose 3D reconstruction of the skin surface using a single image. Unlike extant 3D skin surface reconstruction algorithms, we utilize the local texture and global curvature regions, combining the results for reconstruction. The first entails the reconstruction of global curvature, achieved by bilateral filtering that removes noise on the surface while maintaining the edge (ie, furrow) to obtain the overall curvature. The second entails the reconstruction of local texture, representing the fine wrinkles of the skin, using an advanced form of bilateral filtering. The final image is then composed by merging the two reconstructed images. We tested the curvature reconstruction part by comparing the resulting curvatures with measured values from real phantom objects while local texture reconstruction was verified by measuring skin surface roughness. Then, we showed the reconstructed result of our proposed algorithm via the reconstruction of various real skin surfaces. The experimental results demonstrate that our approach is a promising technology to reconstruct an accurate skin surface with a single skin image. We proposed 3D skin surface reconstruction using only a single camera. We highlighted the utility of global curvature, which has not been considered important in the past. Thus, we proposed a new method for 3D reconstruction that can be used for 3D haptic palpation, dividing the concepts of local and global regions. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Adaptive non-local means on local principle neighborhood for noise/artifacts reduction in low-dose CT images.

PubMed

Zhang, Yuanke; Lu, Hongbing; Rong, Junyan; Meng, Jing; Shang, Junliang; Ren, Pinghong; Zhang, Junying

2017-09-01

Low-dose CT (LDCT) technique can reduce the x-ray radiation exposure to patients at the cost of degraded images with severe noise and artifacts. Non-local means (NLM) filtering has shown its potential in improving LDCT image quality. However, currently most NLM-based approaches employ a weighted average operation directly on all neighbor pixels with a fixed filtering parameter throughout the NLM filtering process, ignoring the non-stationary noise nature of LDCT images. In this paper, an adaptive NLM filtering scheme on local principle neighborhoods (PC-NLM) is proposed for structure-preserving noise/artifacts reduction in LDCT images. Instead of using neighboring patches directly, in the PC-NLM scheme, the principle component analysis (PCA) is first applied on local neighboring patches of the target patch to decompose the local patches into uncorrelated principle components (PCs), then a NLM filtering is used to regularize each PC of the target patch and finally the regularized components is transformed to get the target patch in image domain. Especially, in the NLM scheme, the filtering parameter is estimated adaptively from local noise level of the neighborhood as well as the signal-to-noise ratio (SNR) of the corresponding PC, which guarantees a "weaker" NLM filtering on PCs with higher SNR and a "stronger" filtering on PCs with lower SNR. The PC-NLM procedure is iteratively performed several times for better removal of the noise and artifacts, and an adaptive iteration strategy is developed to reduce the computational load by determining whether a patch should be processed or not in next round of the PC-NLM filtering. The effectiveness of the presented PC-NLM algorithm is validated by experimental phantom studies and clinical studies. The results show that it can achieve promising gain over some state-of-the-art methods in terms of artifact suppression and structure preservation. With the use of PCA on local neighborhoods to extract principal structural components, as well as adaptive NLM filtering on PCs of the target patch using filtering parameter estimated based on the local noise level and corresponding SNR, the proposed PC-NLM method shows its efficacy in preserving fine anatomical structures and suppressing noise/artifacts in LDCT images. © 2017 American Association of Physicists in Medicine.

Multi-view 3D echocardiography compounding based on feature consistency

NASA Astrophysics Data System (ADS)

Yao, Cheng; Simpson, John M.; Schaeffter, Tobias; Penney, Graeme P.

2011-09-01

Echocardiography (echo) is a widely available method to obtain images of the heart; however, echo can suffer due to the presence of artefacts, high noise and a restricted field of view. One method to overcome these limitations is to use multiple images, using the 'best' parts from each image to produce a higher quality 'compounded' image. This paper describes our compounding algorithm which specifically aims to reduce the effect of echo artefacts as well as improving the signal-to-noise ratio, contrast and extending the field of view. Our method weights image information based on a local feature coherence/consistency between all the overlapping images. Validation has been carried out using phantom, volunteer and patient datasets consisting of up to ten multi-view 3D images. Multiple sets of phantom images were acquired, some directly from the phantom surface, and others by imaging through hard and soft tissue mimicking material to degrade the image quality. Our compounding method is compared to the original, uncompounded echocardiography images, and to two basic statistical compounding methods (mean and maximum). Results show that our method is able to take a set of ten images, degraded by soft and hard tissue artefacts, and produce a compounded image of equivalent quality to images acquired directly from the phantom. Our method on phantom, volunteer and patient data achieves almost the same signal-to-noise improvement as the mean method, while simultaneously almost achieving the same contrast improvement as the maximum method. We show a statistically significant improvement in image quality by using an increased number of images (ten compared to five), and visual inspection studies by three clinicians showed very strong preference for our compounded volumes in terms of overall high image quality, large field of view, high endocardial border definition and low cavity noise.

Retrieving Coherent Receiver Function Images with Dense Arrays

NASA Astrophysics Data System (ADS)

Zhong, M.; Zhan, Z.

2016-12-01

Receiver functions highlight converted phases (e.g., Ps, PpPs, sP) and have been widely used to study seismic interfaces. With a dense array, receiver functions (RFs) at multiple stations form a RF image that can provide more robust/detailed constraints. However, due to noise in data, non-uniqueness of deconvolution, and local structures that cannot be detected across neighboring stations, traditional RF images are often noisy and hard to interpret. Previous attempts to enhance coherence by stacking RFs from multiple events or post-filtering the RF images have not produced satisfactory improvements. Here, we propose a new method to retrieve coherent RF images with dense arrays. We take advantage of the waveform coherency at neighboring stations and invert for a small number of coherent arrivals for their RFs. The new RF images contain only the coherent arrivals required to fit data well. Synthetic tests and preliminary applications on real data demonstrate that the new RF images are easier to interpret and improve our ability to infer Earth structures using receiver functions.

Multi-sparse dictionary colorization algorithm based on the feature classification and detail enhancement

NASA Astrophysics Data System (ADS)

Yan, Dan; Bai, Lianfa; Zhang, Yi; Han, Jing

2018-02-01

For the problems of missing details and performance of the colorization based on sparse representation, we propose a conceptual model framework for colorizing gray-scale images, and then a multi-sparse dictionary colorization algorithm based on the feature classification and detail enhancement (CEMDC) is proposed based on this framework. The algorithm can achieve a natural colorized effect for a gray-scale image, and it is consistent with the human vision. First, the algorithm establishes a multi-sparse dictionary classification colorization model. Then, to improve the accuracy rate of the classification, the corresponding local constraint algorithm is proposed. Finally, we propose a detail enhancement based on Laplacian Pyramid, which is effective in solving the problem of missing details and improving the speed of image colorization. In addition, the algorithm not only realizes the colorization of the visual gray-scale image, but also can be applied to the other areas, such as color transfer between color images, colorizing gray fusion images, and infrared images.

Nonlocal means-based speckle filtering for ultrasound images

PubMed Central

Coupé, Pierrick; Hellier, Pierre; Kervrann, Charles; Barillot, Christian

2009-01-01

In image processing, restoration is expected to improve the qualitative inspection of the image and the performance of quantitative image analysis techniques. In this paper, an adaptation of the Non Local (NL-) means filter is proposed for speckle reduction in ultrasound (US) images. Originally developed for additive white Gaussian noise, we propose to use a Bayesian framework to derive a NL-means filter adapted to a relevant ultrasound noise model. Quantitative results on synthetic data show the performances of the proposed method compared to well-established and state-of-the-art methods. Results on real images demonstrate that the proposed method is able to preserve accurately edges and structural details of the image. PMID:19482578

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

Meddens, Marjolein B. M.; Liu, Sheng; Finnegan, Patrick S.

Here, we have developed a method for performing light-sheet microscopy with a single high numerical aperture lens by integrating reflective side walls into a microfluidic chip. These 45° side walls generate light-sheet illumination by reflecting a vertical light-sheet into the focal plane of the objective. Light-sheet illumination of cells loaded in the channels increases image quality in diffraction limited imaging via reduction of out-of-focus background light. Single molecule super-resolution is also improved by the decreased background resulting in better localization precision and decreased photo-bleaching, leading to more accepted localizations overall and higher quality images. Moreover, 2D and 3D single moleculemore » super-resolution data can be acquired faster by taking advantage of the increased illumination intensities as compared to wide field, in the focused light-sheet.« less

Recent developments in imaging system assessment methodology, FROC analysis and the search model.

PubMed

Chakraborty, Dev P

2011-08-21

A frequent problem in imaging is assessing whether a new imaging system is an improvement over an existing standard. Observer performance methods, in particular the receiver operating characteristic (ROC) paradigm, are widely used in this context. In ROC analysis lesion location information is not used and consequently scoring ambiguities can arise in tasks, such as nodule detection, involving finding localized lesions. This paper reviews progress in the free-response ROC (FROC) paradigm in which the observer marks and rates suspicious regions and the location information is used to determine whether lesions were correctly localized. Reviewed are FROC data analysis, a search-model for simulating FROC data, predictions of the model and a method for estimating the parameters. The search model parameters are physically meaningful quantities that can guide system optimization.

Transabdominal contrast-enhanced ultrasound imaging of the prostate.

PubMed

Mischi, Massimo; Demi, Libertario; Smeenge, Martijn; Kuenen, Maarten P J; Postema, Arnoud W; de la Rosette, Jean J M C H; Wijkstra, Hessel

2015-04-01

Numerous age-related pathologies affect the prostate gland, the most menacing of which is prostate cancer (PCa). The diagnostic tools for prostate investigation are invasive, requiring biopsies when PCa is suspected. Novel dynamic contrast-enhanced ultrasound (DCE-US) imaging approaches have been proposed recently and appear promising for minimally invasive localization of PCa. Ultrasound imaging of the prostate is traditionally performed with a transrectal probe because the location of the prostate allows for high-resolution images using high-frequency transducers. However, DCE-US imaging requires lower frequencies to induce bubble resonance and, thus, improve contrast-to-tissue ratio. For this reason, in this study we investigate the feasibility of quantitative DCE-US imaging of the prostate via the abdomen. The study included 10 patients (age = 60.7 ± 5.7 y) referred for a needle biopsy study. After having given informed consent, patients underwent DCE-US with both transabdominal and transrectal probes. Time-intensity contrast curves were derived using both approaches and their model-fit quality was compared. Although further improvements are expected by optimization of the transabdominal settings, the results of transabdominal and transrectal DCE-US are closely comparable, confirming the feasibility of transabdominal DCE-US; transabdominal curve fitting revealed an average determination coefficient r(2) = 0.91 (r(2) > 0.75 for 78.6% of all prostate pixels) compared with r(2) = 0.91 (r(2) > 0.75 for 81.6% of all prostate pixels) by the transrectal approach. Replacing the transrectal approach with more acceptable transabdominal scanning for prostate investigation is feasible. This approach would improve patient comfort and represent a useful option for PCa localization and monitoring. Copyright © 2015 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

A fingerprint classification algorithm based on combination of local and global information

NASA Astrophysics Data System (ADS)

Liu, Chongjin; Fu, Xiang; Bian, Junjie; Feng, Jufu

2011-12-01

Fingerprint recognition is one of the most important technologies in biometric identification and has been wildly applied in commercial and forensic areas. Fingerprint classification, as the fundamental procedure in fingerprint recognition, can sharply decrease the quantity for fingerprint matching and improve the efficiency of fingerprint recognition. Most fingerprint classification algorithms are based on the number and position of singular points. Because the singular points detecting method only considers the local information commonly, the classification algorithms are sensitive to noise. In this paper, we propose a novel fingerprint classification algorithm combining the local and global information of fingerprint. Firstly we use local information to detect singular points and measure their quality considering orientation structure and image texture in adjacent areas. Furthermore the global orientation model is adopted to measure the reliability of singular points group. Finally the local quality and global reliability is weighted to classify fingerprint. Experiments demonstrate the accuracy and effectivity of our algorithm especially for the poor quality fingerprint images.

Gray-world-assumption-based illuminant color estimation using color gamuts with high and low chroma

NASA Astrophysics Data System (ADS)

Kawamura, Harumi; Yonemura, Shunichi; Ohya, Jun; Kojima, Akira

2013-02-01

A new approach is proposed for estimating illuminant colors from color images under an unknown scene illuminant. The approach is based on a combination of a gray-world-assumption-based illuminant color estimation method and a method using color gamuts. The former method, which is one we had previously proposed, improved on the original method that hypothesizes that the average of all the object colors in a scene is achromatic. Since the original method estimates scene illuminant colors by calculating the average of all the image pixel values, its estimations are incorrect when certain image colors are dominant. Our previous method improves on it by choosing several colors on the basis of an opponent-color property, which is that the average color of opponent colors is achromatic, instead of using all colors. However, it cannot estimate illuminant colors when there are only a few image colors or when the image colors are unevenly distributed in local areas in the color space. The approach we propose in this paper combines our previous method and one using high chroma and low chroma gamuts, which makes it possible to find colors that satisfy the gray world assumption. High chroma gamuts are used for adding appropriate colors to the original image and low chroma gamuts are used for narrowing down illuminant color possibilities. Experimental results obtained using actual images show that even if the image colors are localized in a certain area in the color space, the illuminant colors are accurately estimated, with smaller estimation error average than that generated in the conventional method.

Photoacoustic-based sO2 estimation through excised bovine prostate tissue with interstitial light delivery.

PubMed

Mitcham, Trevor; Taghavi, Houra; Long, James; Wood, Cayla; Fuentes, David; Stefan, Wolfgang; Ward, John; Bouchard, Richard

2017-09-01

Photoacoustic (PA) imaging is capable of probing blood oxygen saturation (sO 2 ), which has been shown to correlate with tissue hypoxia, a promising cancer biomarker. However, wavelength-dependent local fluence changes can compromise sO 2 estimation accuracy in tissue. This work investigates using PA imaging with interstitial irradiation and local fluence correction to assess precision and accuracy of sO 2 estimation of blood samples through ex vivo bovine prostate tissue ranging from 14% to 100% sO 2 . Study results for bovine blood samples at distances up to 20 mm from the irradiation source show that local fluence correction improved average sO 2 estimation error from 16.8% to 3.2% and maintained an average precision of 2.3% when compared to matched CO-oximeter sO 2 measurements. This work demonstrates the potential for future clinical translation of using fluence-corrected and interstitially driven PA imaging to accurately and precisely assess sO 2 at depth in tissue with high resolution.

Rotation and scale invariant shape context registration for remote sensing images with background variations

NASA Astrophysics Data System (ADS)

Jiang, Jie; Zhang, Shumei; Cao, Shixiang

2015-01-01

Multitemporal remote sensing images generally suffer from background variations, which significantly disrupt traditional region feature and descriptor abstracts, especially between pre and postdisasters, making registration by local features unreliable. Because shapes hold relatively stable information, a rotation and scale invariant shape context based on multiscale edge features is proposed. A multiscale morphological operator is adapted to detect edges of shapes, and an equivalent difference of Gaussian scale space is built to detect local scale invariant feature points along the detected edges. Then, a rotation invariant shape context with improved distance discrimination serves as a feature descriptor. For a distance shape context, a self-adaptive threshold (SAT) distance division coordinate system is proposed, which improves the discriminative property of the feature descriptor in mid-long pixel distances from the central point while maintaining it in shorter ones. To achieve rotation invariance, the magnitude of Fourier transform in one-dimension is applied to calculate angle shape context. Finally, the residual error is evaluated after obtaining thin-plate spline transformation between reference and sensed images. Experimental results demonstrate the robustness, efficiency, and accuracy of this automatic algorithm.

Direct vibro-elastography FEM inversion in Cartesian and cylindrical coordinate systems without the local homogeneity assumption

NASA Astrophysics Data System (ADS)

Honarvar, M.; Lobo, J.; Mohareri, O.; Salcudean, S. E.; Rohling, R.

2015-05-01

To produce images of tissue elasticity, the vibro-elastography technique involves applying a steady-state multi-frequency vibration to tissue, estimating displacements from ultrasound echo data, and using the estimated displacements in an inverse elasticity problem with the shear modulus spatial distribution as the unknown. In order to fully solve the inverse problem, all three displacement components are required. However, using ultrasound, the axial component of the displacement is measured much more accurately than the other directions. Therefore, simplifying assumptions must be used in this case. Usually, the equations of motion are transformed into a Helmholtz equation by assuming tissue incompressibility and local homogeneity. The local homogeneity assumption causes significant imaging artifacts in areas of varying elasticity. In this paper, we remove the local homogeneity assumption. In particular we introduce a new finite element based direct inversion technique in which only the coupling terms in the equation of motion are ignored, so it can be used with only one component of the displacement. Both Cartesian and cylindrical coordinate systems are considered. The use of multi-frequency excitation also allows us to obtain multiple measurements and reduce artifacts in areas where the displacement of one frequency is close to zero. The proposed method was tested in simulations and experiments against a conventional approach in which the local homogeneity is used. The results show significant improvements in elasticity imaging with the new method compared to previous methods that assumes local homogeneity. For example in simulations, the contrast to noise ratio (CNR) for the region with spherical inclusion increases from an average value of 1.5-17 after using the proposed method instead of the local inversion with homogeneity assumption, and similarly in the prostate phantom experiment, the CNR improved from an average value of 1.6 to about 20.

Study of functional infrared imaging for early detection of mucositis in locally advanced head and neck cancer treated with chemoradiotherapy

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

Cohen, Ezra E. W.; Ahmed, Omar; Kocherginsky, Masha

2013-10-01

Chemoradiotherapy (CRT) has led to improved efficacy in treating locally advanced squamous cell carcinoma of the head and neck (LA-SCCHN) but has led to almost universal in-field mucositis. Patients treated with the same regimen often have differences in mucositis occurrence and severity. Mucositis induced via radiation is known to represent an intense inflammatory response histologically. We hypothesized that patients destined to display severe mucocutaneous toxicity would demonstrate greater alterations in thermal intensity early in therapy than identically treated counterparts. This will allow identification of patients that will require more intensive supportive care using thermal imaging technology.

Cone-beam x-ray luminescence computed tomography based on x-ray absorption dosage.

PubMed

Liu, Tianshuai; Rong, Junyan; Gao, Peng; Zhang, Wenli; Liu, Wenlei; Zhang, Yuanke; Lu, Hongbing

2018-02-01

With the advances of x-ray excitable nanophosphors, x-ray luminescence computed tomography (XLCT) has become a promising hybrid imaging modality. In particular, a cone-beam XLCT (CB-XLCT) system has demonstrated its potential in in vivo imaging with the advantage of fast imaging speed over other XLCT systems. Currently, the imaging models of most XLCT systems assume that nanophosphors emit light based on the intensity distribution of x-ray within the object, not completely reflecting the nature of the x-ray excitation process. To improve the imaging quality of CB-XLCT, an imaging model that adopts an excitation model of nanophosphors based on x-ray absorption dosage is proposed in this study. To solve the ill-posed inverse problem, a reconstruction algorithm that combines the adaptive Tikhonov regularization method with the imaging model is implemented for CB-XLCT reconstruction. Numerical simulations and phantom experiments indicate that compared with the traditional forward model based on x-ray intensity, the proposed dose-based model could improve the image quality of CB-XLCT significantly in terms of target shape, localization accuracy, and image contrast. In addition, the proposed model behaves better in distinguishing closer targets, demonstrating its advantage in improving spatial resolution. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Initial Investigation of preclinical integrated SPECT and MR imaging.

PubMed

Hamamura, Mark J; Ha, Seunghoon; Roeck, Werner W; Wagenaar, Douglas J; Meier, Dirk; Patt, Bradley E; Nalcioglu, Orhan

2010-02-01

Single-photon emission computed tomography (SPECT) can provide specific functional information while magnetic resonance imaging (MRI) can provide high-spatial resolution anatomical information as well as complementary functional information. In this study, we utilized a dual modality SPECT/MRI (MRSPECT) system to investigate the integration of SPECT and MRI for improved image accuracy. The MRSPECT system consisted of a cadmium-zinc-telluride (CZT) nuclear radiation detector interfaced with a specialized radiofrequency (RF) coil that was placed within a whole-body 4 T MRI system. The importance of proper corrections for non-uniform detector sensitivity and Lorentz force effects was demonstrated. MRI data were utilized for attenuation correction (AC) of the nuclear projection data and optimized Wiener filtering of the SPECT reconstruction for improved image accuracy. Finally, simultaneous dual-imaging of a nude mouse was performed to demonstrated the utility of co-registration for accurate localization of a radioactive source.

Initial Investigation of Preclinical Integrated SPECT and MR Imaging

PubMed Central

Hamamura, Mark J.; Ha, Seunghoon; Roeck, Werner W.; Wagenaar, Douglas J.; Meier, Dirk; Patt, Bradley E.; Nalcioglu, Orhan

2014-01-01

Single-photon emission computed tomography (SPECT) can provide specific functional information while magnetic resonance imaging (MRI) can provide high-spatial resolution anatomical information as well as complementary functional information. In this study, we utilized a dual modality SPECT/MRI (MRSPECT) system to investigate the integration of SPECT and MRI for improved image accuracy. The MRSPECT system consisted of a cadmium-zinc-telluride (CZT) nuclear radiation detector interfaced with a specialized radiofrequency (RF) coil that was placed within a whole-body 4 T MRI system. The importance of proper corrections for non-uniform detector sensitivity and Lorentz force effects was demonstrated. MRI data were utilized for attenuation correction (AC) of the nuclear projection data and optimized Wiener filtering of the SPECT reconstruction for improved image accuracy. Finally, simultaneous dual-imaging of a nude mouse was performed to demonstrated the utility of co-registration for accurate localization of a radioactive source. PMID:20082527

Deep Learning Nuclei Detection in Digitized Histology Images by Superpixels

PubMed Central

Sornapudi, Sudhir; Stanley, Ronald Joe; Stoecker, William V.; Almubarak, Haidar; Long, Rodney; Antani, Sameer; Thoma, George; Zuna, Rosemary; Frazier, Shelliane R.

2018-01-01

Background: Advances in image analysis and computational techniques have facilitated automatic detection of critical features in histopathology images. Detection of nuclei is critical for squamous epithelium cervical intraepithelial neoplasia (CIN) classification into normal, CIN1, CIN2, and CIN3 grades. Methods: In this study, a deep learning (DL)-based nuclei segmentation approach is investigated based on gathering localized information through the generation of superpixels using a simple linear iterative clustering algorithm and training with a convolutional neural network. Results: The proposed approach was evaluated on a dataset of 133 digitized histology images and achieved an overall nuclei detection (object-based) accuracy of 95.97%, with demonstrated improvement over imaging-based and clustering-based benchmark techniques. Conclusions: The proposed DL-based nuclei segmentation Method with superpixel analysis has shown improved segmentation results in comparison to state-of-the-art methods. PMID:29619277

Recent technological advances in pediatric brain tumor surgery.

PubMed

Zebian, Bassel; Vergani, Francesco; Lavrador, José Pedro; Mukherjee, Soumya; Kitchen, William John; Stagno, Vita; Chamilos, Christos; Pettorini, Benedetta; Mallucci, Conor

2017-01-01

X-rays and ventriculograms were the first imaging modalities used to localize intracranial lesions including brain tumors as far back as the 1880s. Subsequent advances in preoperative radiological localization included computed tomography (CT; 1971) and MRI (1977). Since then, other imaging modalities have been developed for clinical application although none as pivotal as CT and MRI. Intraoperative technological advances include the microscope, which has allowed precise surgery under magnification and improved lighting, and the endoscope, which has improved the treatment of hydrocephalus and allowed biopsy and complete resection of intraventricular, pituitary and pineal region tumors through a minimally invasive approach. Neuronavigation, intraoperative MRI, CT and ultrasound have increased the ability of the neurosurgeon to perform safe and maximal tumor resection. This may be facilitated by the use of fluorescing agents, which help define the tumor margin, and intraoperative neurophysiological monitoring, which helps identify and protect eloquent brain.

Local Competition-Based Superpixel Segmentation Algorithm in Remote Sensing

PubMed Central

Liu, Jiayin; Tang, Zhenmin; Cui, Ying; Wu, Guoxing

2017-01-01

Remote sensing technologies have been widely applied in urban environments’ monitoring, synthesis and modeling. Incorporating spatial information in perceptually coherent regions, superpixel-based approaches can effectively eliminate the “salt and pepper” phenomenon which is common in pixel-wise approaches. Compared with fixed-size windows, superpixels have adaptive sizes and shapes for different spatial structures. Moreover, superpixel-based algorithms can significantly improve computational efficiency owing to the greatly reduced number of image primitives. Hence, the superpixel algorithm, as a preprocessing technique, is more and more popularly used in remote sensing and many other fields. In this paper, we propose a superpixel segmentation algorithm called Superpixel Segmentation with Local Competition (SSLC), which utilizes a local competition mechanism to construct energy terms and label pixels. The local competition mechanism leads to energy terms locality and relativity, and thus, the proposed algorithm is less sensitive to the diversity of image content and scene layout. Consequently, SSLC could achieve consistent performance in different image regions. In addition, the Probability Density Function (PDF), which is estimated by Kernel Density Estimation (KDE) with the Gaussian kernel, is introduced to describe the color distribution of superpixels as a more sophisticated and accurate measure. To reduce computational complexity, a boundary optimization framework is introduced to only handle boundary pixels instead of the whole image. We conduct experiments to benchmark the proposed algorithm with the other state-of-the-art ones on the Berkeley Segmentation Dataset (BSD) and remote sensing images. Results demonstrate that the SSLC algorithm yields the best overall performance, while the computation time-efficiency is still competitive. PMID:28604641

Local Competition-Based Superpixel Segmentation Algorithm in Remote Sensing.

PubMed

Liu, Jiayin; Tang, Zhenmin; Cui, Ying; Wu, Guoxing

2017-06-12

Remote sensing technologies have been widely applied in urban environments' monitoring, synthesis and modeling. Incorporating spatial information in perceptually coherent regions, superpixel-based approaches can effectively eliminate the "salt and pepper" phenomenon which is common in pixel-wise approaches. Compared with fixed-size windows, superpixels have adaptive sizes and shapes for different spatial structures. Moreover, superpixel-based algorithms can significantly improve computational efficiency owing to the greatly reduced number of image primitives. Hence, the superpixel algorithm, as a preprocessing technique, is more and more popularly used in remote sensing and many other fields. In this paper, we propose a superpixel segmentation algorithm called Superpixel Segmentation with Local Competition (SSLC), which utilizes a local competition mechanism to construct energy terms and label pixels. The local competition mechanism leads to energy terms locality and relativity, and thus, the proposed algorithm is less sensitive to the diversity of image content and scene layout. Consequently, SSLC could achieve consistent performance in different image regions. In addition, the Probability Density Function (PDF), which is estimated by Kernel Density Estimation (KDE) with the Gaussian kernel, is introduced to describe the color distribution of superpixels as a more sophisticated and accurate measure. To reduce computational complexity, a boundary optimization framework is introduced to only handle boundary pixels instead of the whole image. We conduct experiments to benchmark the proposed algorithm with the other state-of-the-art ones on the Berkeley Segmentation Dataset (BSD) and remote sensing images. Results demonstrate that the SSLC algorithm yields the best overall performance, while the computation time-efficiency is still competitive.

Image Guidance in External Beam Accelerated Partial Breast Irradiation: Comparison of Surrogates for the Lumpectomy Cavity

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

Hasan, Yasmin; Kim, Leonard; Martinez, Alvaro

Purpose: To compare localization of the lumpectomy cavity by using breast surface matching vs. clips for image-guided external beam accelerated partial breast irradiation. Methods and Materials: Twenty-seven patients with breast cancer with two computed tomography (CT) scans each had three CT registrations performed: (1) to bony anatomy, (2) to the center of mass (COM) of surgical clips, and (3) to the breast surface. The cavity COM was defined in both the initial and second CT scans after each type of registration, and distances between COMs ({delta}COM{sub Bone}, {delta}COM{sub Clips}, and {delta}COM{sub Surface}) were determined. Smaller {delta}COMs were interpreted as bettermore » localizations. Correlation coefficients were calculated for {delta}COM vs. several variables. Results: The {delta}COM{sub Bone} (mean, 7 {+-} 2 [SD] mm) increased with breast volume (r = 0.4; p = 0.02) and distance from the chest wall (r = 0.5; p = 0.003). Relative to bony registration, clip registration provided better localization ({delta}COM{sub Clips} < {delta}COM{sub Bone}) in 25 of 27 cases. Breast surface matching improved cavity localization ({delta}COM{sub Surface} < {delta}COM{sub Bone}) in 19 of 27 cases. Mean improvements ({delta}COM{sub Bone} - {delta}COM{sub ClipsorSurface}) were 4 {+-} 3 and 2 {+-} 4 mm, respectively. In terms of percentage of improvement ([{delta}COM{sub Bone} - {delta}COM{sub ClipsorSurface}]/{delta}COM{sub Bone}), only surface matching showed a correlation with breast volume. Clip localization outperformed surface registration for cavities located superior to the breast COM. Conclusions: Use of either breast surface or surgical clips as surrogates for the cavity results in improved localization in most patients compared with bony registration and may allow smaller planning target volume margins for external beam accelerated partial breast irradiation. Compared with surface registration, clip registration may be less sensitive to anatomic characteristics and therefore more broadly applicable.« less

Techniques for detection and localization of weak hippocampal and medial frontal sources using beamformers in MEG.

PubMed

Mills, Travis; Lalancette, Marc; Moses, Sandra N; Taylor, Margot J; Quraan, Maher A

2012-07-01

Magnetoencephalography provides precise information about the temporal dynamics of brain activation and is an ideal tool for investigating rapid cognitive processing. However, in many cognitive paradigms visual stimuli are used, which evoke strong brain responses (typically 40-100 nAm in V1) that may impede the detection of weaker activations of interest. This is particularly a concern when beamformer algorithms are used for source analysis, due to artefacts such as "leakage" of activation from the primary visual sources into other regions. We have previously shown (Quraan et al. 2011) that we can effectively reduce leakage patterns and detect weak hippocampal sources by subtracting the functional images derived from the experimental task and a control task with similar stimulus parameters. In this study we assess the performance of three different subtraction techniques. In the first technique we follow the same post-localization subtraction procedures as in our previous work. In the second and third techniques, we subtract the sensor data obtained from the experimental and control paradigms prior to source localization. Using simulated signals embedded in real data, we show that when beamformers are used, subtraction prior to source localization allows for the detection of weaker sources and higher localization accuracy. The improvement in localization accuracy exceeded 10 mm at low signal-to-noise ratios, and sources down to below 5 nAm were detected. We applied our techniques to empirical data acquired with two different paradigms designed to evoke hippocampal and frontal activations, and demonstrated our ability to detect robust activations in both regions with substantial improvements over image subtraction. We conclude that removal of the common-mode dominant sources through data subtraction prior to localization further improves the beamformer's ability to project the n-channel sensor-space data to reveal weak sources of interest and allows more accurate localization.

Compression of Encrypted Images Using Set Partitioning In Hierarchical Trees Algorithm

NASA Astrophysics Data System (ADS)

Sarika, G.; Unnithan, Harikuttan; Peter, Smitha

2011-10-01

When it is desired to transmit redundant data over an insecure channel, it is customary to encrypt the data. For encrypted real world sources such as images, the use of Markova properties in the slepian-wolf decoder does not work well for gray scale images. Here in this paper we propose a method of compression of an encrypted image. In the encoder section, the image is first encrypted and then it undergoes compression in resolution. The cipher function scrambles only the pixel values, but does not shuffle the pixel locations. After down sampling, each sub-image is encoded independently and the resulting syndrome bits are transmitted. The received image undergoes a joint decryption and decompression in the decoder section. By using the local statistics based on the image, it is recovered back. Here the decoder gets only lower resolution version of the image. In addition, this method provides only partial access to the current source at the decoder side, which improves the decoder's learning of the source statistics. The source dependency is exploited to improve the compression efficiency. This scheme provides better coding efficiency and less computational complexity.

Should all acromioclavicular joint injections be performed under image guidance?

PubMed

Javed, S; Sadozai, Z; Javed, A; Din, A; Schmitgen, G

2017-01-01

Steroid and local anaesthetic injection to the acromioclavicular joint (ACJ) is a very common diagnostic and therapeutic procedure, which is often performed in the outpatient department. However, it can be difficult to localize this joint because of its small size, presence of osteophytes and variable morphology in the population. We performed a study to determine whether the use of an image intensifier (X-ray guidance), in theatre, improves the accuracy of this injection. This was a prospective study carried out between March 2014 and March 2015. The injections were performed by two senior orthopaedic surgeons. First, we clinically palpated the ACJ and marked the area over this point as A. Then, with the use of a needle and an image intensifier in a single plane, we identified the actual location of the ACJ and marked this point as B. We measured the distance between A and B in millimetres (mm) and determined the accuracy of the injections. Further analysis taking into account the ACJ capsular attachments was also performed. In total, 45 patients and 50 injections were included in the study; five patients had repeated injections at different times. We found that only 12 injections (24%) were palpated to be correct with no discrepancies between A and B (95% confidence interval: 14-37%). For the remaining 38 injections (76%), the use of an image intensifier had significantly improved the accuracy of ACJ location ( p < 0.05). Taking the capsular attachments of the ACJ into consideration reduced the number of inaccurate injections to 27 (54%). We recommend the use of an image intensifier (or ultrasound guidance) to accurately determine the location of the ACJ for steroid and local anaesthetic injections. This prevents an injection into the wrong place, which can lead to wrong diagnosis and/or suboptimal treatment.

Evaluation of a 3D local multiresolution algorithm for the correction of partial volume effects in positron emission tomography.

PubMed

Le Pogam, Adrien; Hatt, Mathieu; Descourt, Patrice; Boussion, Nicolas; Tsoumpas, Charalampos; Turkheimer, Federico E; Prunier-Aesch, Caroline; Baulieu, Jean-Louis; Guilloteau, Denis; Visvikis, Dimitris

2011-09-01

Partial volume effects (PVEs) are consequences of the limited spatial resolution in emission tomography leading to underestimation of uptake in tissues of size similar to the point spread function (PSF) of the scanner as well as activity spillover between adjacent structures. Among PVE correction methodologies, a voxel-wise mutual multiresolution analysis (MMA) was recently introduced. MMA is based on the extraction and transformation of high resolution details from an anatomical image (MR/CT) and their subsequent incorporation into a low-resolution PET image using wavelet decompositions. Although this method allows creating PVE corrected images, it is based on a 2D global correlation model, which may introduce artifacts in regions where no significant correlation exists between anatomical and functional details. A new model was designed to overcome these two issues (2D only and global correlation) using a 3D wavelet decomposition process combined with a local analysis. The algorithm was evaluated on synthetic, simulated and patient images, and its performance was compared to the original approach as well as the geometric transfer matrix (GTM) method. Quantitative performance was similar to the 2D global model and GTM in correlated cases. In cases where mismatches between anatomical and functional information were present, the new model outperformed the 2D global approach, avoiding artifacts and significantly improving quality of the corrected images and their quantitative accuracy. A new 3D local model was proposed for a voxel-wise PVE correction based on the original mutual multiresolution analysis approach. Its evaluation demonstrated an improved and more robust qualitative and quantitative accuracy compared to the original MMA methodology, particularly in the absence of full correlation between anatomical and functional information.

Evaluation of a 3D local multiresolution algorithm for the correction of partial volume effects in positron emission tomography

PubMed Central

Le Pogam, Adrien; Hatt, Mathieu; Descourt, Patrice; Boussion, Nicolas; Tsoumpas, Charalampos; Turkheimer, Federico E.; Prunier-Aesch, Caroline; Baulieu, Jean-Louis; Guilloteau, Denis; Visvikis, Dimitris

2011-01-01

Purpose Partial volume effects (PVE) are consequences of the limited spatial resolution in emission tomography leading to under-estimation of uptake in tissues of size similar to the point spread function (PSF) of the scanner as well as activity spillover between adjacent structures. Among PVE correction methodologies, a voxel-wise mutual multi-resolution analysis (MMA) was recently introduced. MMA is based on the extraction and transformation of high resolution details from an anatomical image (MR/CT) and their subsequent incorporation into a low resolution PET image using wavelet decompositions. Although this method allows creating PVE corrected images, it is based on a 2D global correlation model which may introduce artefacts in regions where no significant correlation exists between anatomical and functional details. Methods A new model was designed to overcome these two issues (2D only and global correlation) using a 3D wavelet decomposition process combined with a local analysis. The algorithm was evaluated on synthetic, simulated and patient images, and its performance was compared to the original approach as well as the geometric transfer matrix (GTM) method. Results Quantitative performance was similar to the 2D global model and GTM in correlated cases. In cases where mismatches between anatomical and functional information were present the new model outperformed the 2D global approach, avoiding artefacts and significantly improving quality of the corrected images and their quantitative accuracy. Conclusions A new 3D local model was proposed for a voxel-wise PVE correction based on the original mutual multi-resolution analysis approach. Its evaluation demonstrated an improved and more robust qualitative and quantitative accuracy compared to the original MMA methodology, particularly in the absence of full correlation between anatomical and functional information. PMID:21978037

SU-D-12A-02: DeTECT, a Method to Enhance Soft Tissue Contrast From Mega Voltage CT

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

Sheng, K; Gou, S; Qi, S

Purpose: MVCT images have been used on TomoTherapy system to align patients based on bony anatomies but its usefulness for soft tissue registration, delineation and adaptive radiation therapy is severely limited due to minimal photoelectric interaction and prominent presence of noise resulting from low detector quantum efficiency of megavoltage x-rays. We aim to utilize a non-local means denoising method and texture analysis to recover the soft tissue information for MVCT. Methods: A block matching 3D (BM3D) algorithm was adapted to reduce the noise while keeping the texture information of the MVCT images. BM3D is an imaging denoising algorithm developed frommore » non-local means methods. BM3D additionally creates 3D groups by stacking 2D patches by the order of similarity. 3D denoising operation is then performed. The resultant 3D group is inversely transformed back to 2D images. In this study, BM3D was applied to MVCT images of a CT quality phantom, a head and neck and a prostate patient. Following denoising, imaging texture was enhanced to create the denoised and texture enhanced CT (DeTECT). Results: The original MVCT images show prevalent noise and poor soft tissue contrast. By applying BM3D denoising and texture enhancement, all MVCT images show remarkable improvements. For the phantom, the contrast to noise ratio for the low contrast plug was improved from 2.2 to 13.1 without compromising line pair conspicuity. For the head and neck patient, the lymph nodes and vein in the carotid space inconspicuous in the original MVCT image becomes highly visible in DeTECT. For the prostate patient, the boundary between the bladder and the prostate in the original MVCT is successfully recovered. Both results are visually validated by kVCT images of the corresponding patients. Conclusion: DeTECT showed the promise to drastically improve the soft tissue contrast of MVCT for image guided radiotherapy and adaptive radiotherapy.« less

SYMPOSIUM ON MULTIMODALITY CARDIOVASCULAR MOLECULAR IMAGING IMAGING TECHNOLOGY - PART 2

PubMed Central

de Kemp, Robert A.; Epstein, Frederick H.; Catana, Ciprian; Tsui, Benjamin M.W.; Ritman, Erik L.

2013-01-01

Rationale The ability to trace or identify specific molecules within a specific anatomic location provides insight into metabolic pathways, tissue components and tracing of solute transport mechanisms. With the increasing use of small animals for research such imaging must have sufficiently high spatial resolution to allow anatomic localization as well as sufficient specificity and sensitivity to provide an accurate description of the molecular distribution and concentration. Methods Imaging methods based on electromagnetic radiation, such as PET, SPECT, MRI and CT, are increasingly applicable due to recent advances in novel scanner hardware, image reconstruction software and availability of novel molecules which have enhanced sensitivity in these methodologies. Results Micro-PET has been advanced by development of detector arrays that provide higher resolution and positron emitting elements that allow new molecular tracers to be labeled. Micro-MRI has been improved in terms of spatial resolution and sensitivity by increased magnet field strength and development of special purpose coils and associated scan protocols. Of particular interest is the associated ability to image local mechanical function and solute transport processes which can be directly related to the molecular information. This is further strengthened by the synergistic integration of the PET with MRI. Micro-SPECT has been improved by use of coded aperture imaging approaches as well as image reconstruction algorithms which can better deal with the photon limited scan data. The limited spatial resolution can be partially overcome by integrating the SPECT with CT. Micro-CT by itself provides exquisite spatial resolution of anatomy, but recent developments of high spatial resolution photon counting and spectrally-sensitive imaging arrays, combined with x-ray optical devices, have promise for actual molecular identification by virtue of the chemical bond lengths of molecules, especially of bio-polymers. Conclusion With the increasing use of small animals for evaluating new clinical imaging techniques as well as providing increased insights into patho-physiological phenomena, the availability of improved detection systems, scanning protocols and associated software, the repertoire of molecular imaging is greatly increased in sensitivity and specificity. PMID:20457793

An Automated Blur Detection Method for Histological Whole Slide Imaging

PubMed Central

Moles Lopez, Xavier; D'Andrea, Etienne; Barbot, Paul; Bridoux, Anne-Sophie; Rorive, Sandrine; Salmon, Isabelle; Debeir, Olivier; Decaestecker, Christine

2013-01-01

Whole slide scanners are novel devices that enable high-resolution imaging of an entire histological slide. Furthermore, the imaging is achieved in only a few minutes, which enables image rendering of large-scale studies involving multiple immunohistochemistry biomarkers. Although whole slide imaging has improved considerably, locally poor focusing causes blurred regions of the image. These artifacts may strongly affect the quality of subsequent analyses, making a slide review process mandatory. This tedious and time-consuming task requires the scanner operator to carefully assess the virtual slide and to manually select new focus points. We propose a statistical learning method that provides early image quality feedback and automatically identifies regions of the image that require additional focus points. PMID:24349343

Study of a high-resolution PET system using a Silicon detector probe

NASA Astrophysics Data System (ADS)

Brzeziński, K.; Oliver, J. F.; Gillam, J.; Rafecas, M.

2014-10-01

A high-resolution silicon detector probe, in coincidence with a conventional PET scanner, is expected to provide images of higher quality than those achievable using the scanner alone. Spatial resolution should improve due to the finer pixelization of the probe detector, while increased sensitivity in the probe vicinity is expected to decrease noise. A PET-probe prototype is being developed utilizing this principle. The system includes a probe consisting of ten layers of silicon detectors, each a 80 × 52 array of 1 × 1 × 1 mm3 pixels, to be operated in coincidence with a modern clinical PET scanner. Detailed simulation studies of this system have been performed to assess the effect of the additional probe information on the quality of the reconstructed images. A grid of point sources was simulated to study the contribution of the probe to the system resolution at different locations over the field of view (FOV). A resolution phantom was used to demonstrate the effect on image resolution for two probe positions. A homogeneous source distribution with hot and cold regions was used to demonstrate that the localized improvement in resolution does not come at the expense of the overall quality of the image. Since the improvement is constrained to an area close to the probe, breast imaging is proposed as a potential application for the novel geometry. In this sense, a simplified breast phantom, adjacent to heart and torso compartments, was simulated and the effect of the probe on lesion detectability, through measurements of the local contrast recovery coefficient-to-noise ratio (CNR), was observed. The list-mode ML-EM algorithm was used for image reconstruction in all cases. As expected, the point spread function of the PET-probe system was found to be non-isotropic and vary with position, offering improvement in specific regions. Increase in resolution, of factors of up to 2, was observed in the region close to the probe. Images of the resolution phantom showed visible improvement in resolution when including the probe in the simulations. The image quality study demonstrated that contrast and spill-over ratio in other areas of the FOV were not sacrificed for this enhancement. The CNR study performed on the breast phantom indicates increased lesion detectability provided by the probe.

An improved level set method for brain MR images segmentation and bias correction.

PubMed

Chen, Yunjie; Zhang, Jianwei; Macione, Jim

2009-10-01

Intensity inhomogeneities cause considerable difficulty in the quantitative analysis of magnetic resonance (MR) images. Thus, bias field estimation is a necessary step before quantitative analysis of MR data can be undertaken. This paper presents a variational level set approach to bias correction and segmentation for images with intensity inhomogeneities. Our method is based on an observation that intensities in a relatively small local region are separable, despite of the inseparability of the intensities in the whole image caused by the overall intensity inhomogeneity. We first define a localized K-means-type clustering objective function for image intensities in a neighborhood around each point. The cluster centers in this objective function have a multiplicative factor that estimates the bias within the neighborhood. The objective function is then integrated over the entire domain to define the data term into the level set framework. Our method is able to capture bias of quite general profiles. Moreover, it is robust to initialization, and thereby allows fully automated applications. The proposed method has been used for images of various modalities with promising results.

Human brain imaging at 9.4 T using a tunable patch antenna for transmission.

PubMed

Hoffmann, Jens; Shajan, G; Budde, Juliane; Scheffler, Klaus; Pohmann, Rolf

2013-05-01

For human brain imaging at ultrahigh fields, the traveling wave concept can provide a more uniform B1+ field over a larger field of view with improved patient comfort compared to conventional volume coils. It suffers, however, from limited transmit efficiency and receive sensitivity and is not readily applicable in systems where the radiofrequency shield is too narrow to allow for unattenuated wave propagation. Here, the near field of a capacitively adjustable patch antenna for excitation is combined with a receive-only array at 9.4 T. The antenna is designed in compact size and placed in close proximity to the subject to improve the transmit efficiency in narrow bores. Experimental and numerical comparisons to conventional microstrip arrays reveal improved B1+ homogeneity and longitudinal coverage, but at the cost of elevated local specific absorption rate. High-resolution functional and anatomical images demonstrate the use of this setup for in vivo human brain imaging at 9.4 T. Copyright © 2012 Wiley Periodicals, Inc.

Image enhancement using the hypothesis selection filter: theory and application to JPEG decoding.

PubMed

Wong, Tak-Shing; Bouman, Charles A; Pollak, Ilya

2013-03-01

We introduce the hypothesis selection filter (HSF) as a new approach for image quality enhancement. We assume that a set of filters has been selected a priori to improve the quality of a distorted image containing regions with different characteristics. At each pixel, HSF uses a locally computed feature vector to predict the relative performance of the filters in estimating the corresponding pixel intensity in the original undistorted image. The prediction result then determines the proportion of each filter used to obtain the final processed output. In this way, the HSF serves as a framework for combining the outputs of a number of different user selected filters, each best suited for a different region of an image. We formulate our scheme in a probabilistic framework where the HSF output is obtained as the Bayesian minimum mean square error estimate of the original image. Maximum likelihood estimates of the model parameters are determined from an offline fully unsupervised training procedure that is derived from the expectation-maximization algorithm. To illustrate how to apply the HSF and to demonstrate its potential, we apply our scheme as a post-processing step to improve the decoding quality of JPEG-encoded document images. The scheme consistently improves the quality of the decoded image over a variety of image content with different characteristics. We show that our scheme results in quantitative improvements over several other state-of-the-art JPEG decoding methods.

Restoration of MRI data for intensity non-uniformities using local high order intensity statistics

PubMed Central

Hadjidemetriou, Stathis; Studholme, Colin; Mueller, Susanne; Weiner, Michael; Schuff, Norbert

2008-01-01

MRI at high magnetic fields (>3.0 T) is complicated by strong inhomogeneous radio-frequency fields, sometimes termed the “bias field”. These lead to non-biological intensity non-uniformities across the image. They can complicate further image analysis such as registration and tissue segmentation. Existing methods for intensity uniformity restoration have been optimized for 1.5 T, but they are less effective for 3.0 T MRI, and not at all satisfactory for higher fields. Also, many of the existing restoration algorithms require a brain template or use a prior atlas, which can restrict their practicalities. In this study an effective intensity uniformity restoration algorithm has been developed based on non-parametric statistics of high order local intensity co-occurrences. These statistics are restored with a non-stationary Wiener filter. The algorithm also assumes a smooth non-uniformity and is stable. It does not require a prior atlas and is robust to variations in anatomy. In geriatric brain imaging it is robust to variations such as enlarged ventricles and low contrast to noise ratio. The co-occurrence statistics improve robustness to whole head images with pronounced non-uniformities present in high field acquisitions. Its significantly improved performance and lower time requirements have been demonstrated by comparing it to the very commonly used N3 algorithm on BrainWeb MR simulator images as well as on real 4 T human head images. PMID:18621568

Invited Review Article: Gas puff imaging diagnostics of edge plasma turbulence in magnetic fusion devices

DOE PAGES

Zweben, S. J.; Terry, J. L.; Stotler, D. P.; ...

2017-04-27

Gas puff imaging (GPI) is a diagnostic of plasma turbulence which uses a puff of neutral gas at the plasma edge to increase the local visible light emission for improved space-time resolution of plasma fluctuations. This paper reviews gas puff imaging diagnostics of edge plasma turbulence in magnetic fusion research, with a focus on the instrumentation, diagnostic cross-checks, and interpretation issues. The gas puff imaging hardware, optics, and detectors are described for about 10 GPI systems implemented over the past similar to 15 years. Comparison of GPI results with other edge turbulence diagnostic results is described, and many common featuresmore » are observed. Here, several issues in the interpretation of GPI measurements are discussed, and potential improvements in hardware and modeling are suggested.« less

Invited Review Article: Gas puff imaging diagnostics of edge plasma turbulence in magnetic fusion devices

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

Zweben, S. J.; Terry, J. L.; Stotler, D. P.

Gas puff imaging (GPI) is a diagnostic of plasma turbulence which uses a puff of neutral gas at the plasma edge to increase the local visible light emission for improved space-time resolution of plasma fluctuations. This paper reviews gas puff imaging diagnostics of edge plasma turbulence in magnetic fusion research, with a focus on the instrumentation, diagnostic cross-checks, and interpretation issues. The gas puff imaging hardware, optics, and detectors are described for about 10 GPI systems implemented over the past similar to 15 years. Comparison of GPI results with other edge turbulence diagnostic results is described, and many common featuresmore » are observed. Here, several issues in the interpretation of GPI measurements are discussed, and potential improvements in hardware and modeling are suggested.« less

A web-based solution for 3D medical image visualization

NASA Astrophysics Data System (ADS)

Hou, Xiaoshuai; Sun, Jianyong; Zhang, Jianguo

2015-03-01

In this presentation, we present a web-based 3D medical image visualization solution which enables interactive large medical image data processing and visualization over the web platform. To improve the efficiency of our solution, we adopt GPU accelerated techniques to process images on the server side while rapidly transferring images to the HTML5 supported web browser on the client side. Compared to traditional local visualization solution, our solution doesn't require the users to install extra software or download the whole volume dataset from PACS server. By designing this web-based solution, it is feasible for users to access the 3D medical image visualization service wherever the internet is available.

Performance of different reflectance and diffuse optical imaging tomographic approaches in fluorescence molecular imaging of small animals

NASA Astrophysics Data System (ADS)

Dinten, Jean-Marc; Petié, Philippe; da Silva, Anabela; Boutet, Jérôme; Koenig, Anne; Hervé, Lionel; Berger, Michel; Laidevant, Aurélie; Rizo, Philippe

2006-03-01

Optical imaging of fluorescent probes is an essential tool for investigation of molecular events in small animals for drug developments. In order to get localization and quantification information of fluorescent labels, CEA-LETI has developed efficient approaches in classical reflectance imaging as well as in diffuse optical tomographic imaging with continuous and temporal signals. This paper presents an overview of the different approaches investigated and their performances. High quality fluorescence reflectance imaging is obtained thanks to the development of an original "multiple wavelengths" system. The uniformity of the excitation light surface area is better than 15%. Combined with the use of adapted fluorescent probes, this system enables an accurate detection of pathological tissues, such as nodules, beneath the animal's observed area. Performances for the detection of ovarian nodules on a nude mouse are shown. In order to investigate deeper inside animals and get 3D localization, diffuse optical tomography systems are being developed for both slab and cylindrical geometries. For these two geometries, our reconstruction algorithms are based on analytical expression of light diffusion. Thanks to an accurate introduction of light/matter interaction process in the algorithms, high quality reconstructions of tumors in mice have been obtained. Reconstruction of lung tumors on mice are presented. By the use of temporal diffuse optical imaging, localization and quantification performances can be improved at the price of a more sophisticated acquisition system and more elaborate information processing methods. Such a system based on a pulsed laser diode and a time correlated single photon counting system has been set up. Performances of this system for localization and quantification of fluorescent probes are presented.

Crowd-sourced pictures geo-localization method based on street view images and 3D reconstruction

NASA Astrophysics Data System (ADS)

Cheng, Liang; Yuan, Yi; Xia, Nan; Chen, Song; Chen, Yanming; Yang, Kang; Ma, Lei; Li, Manchun

2018-07-01

People are increasingly becoming accustomed to taking photos of everyday life in modern cities and uploading them on major photo-sharing social media sites. These sites contain numerous pictures, but some have incomplete or blurred location information. The geo-localization of crowd-sourced pictures enriches the information contained therein, and is applicable to activities such as urban construction, urban landscape analysis, and crime tracking. However, geo-localization faces huge technical challenges. This paper proposes a method for large-scale geo-localization of crowd-sourced pictures. Our approach uses structured, organized Street View images as a reference dataset and employs a three-step strategy of coarse geo-localization by image retrieval, selecting reliable matches by image registration, and fine geo-localization by 3D reconstruction to attach geographic tags to pictures from unidentified sources. In study area, 3D reconstruction based on close-range photogrammetry is used to restore the 3D geographical information of the crowd-sourced pictures, resulting in the proposed method improving the median error from 256.7 m to 69.0 m, and the percentage of the geo-localized query pictures under a 50 m error from 17.2% to 43.2% compared with the previous method. Another discovery using the proposed method is that, in respect of the causes of reconstruction error, closer distances from the cameras to the main objects in query pictures tend to produce lower errors and the component of error parallel to the road makes a more significant contribution to the Total Error. The proposed method is not limited to small areas, and could be expanded to cities and larger areas owing to its flexible parameters.

Fast Recall for Complex-Valued Hopfield Neural Networks with Projection Rules.

PubMed

Kobayashi, Masaki

2017-01-01

Many models of neural networks have been extended to complex-valued neural networks. A complex-valued Hopfield neural network (CHNN) is a complex-valued version of a Hopfield neural network. Complex-valued neurons can represent multistates, and CHNNs are available for the storage of multilevel data, such as gray-scale images. The CHNNs are often trapped into the local minima, and their noise tolerance is low. Lee improved the noise tolerance of the CHNNs by detecting and exiting the local minima. In the present work, we propose a new recall algorithm that eliminates the local minima. We show that our proposed recall algorithm not only accelerated the recall but also improved the noise tolerance through computer simulations.

Underwater sonar image detection: A combination of non-local spatial information and quantum-inspired shuffled frog leaping algorithm.

PubMed

Wang, Xingmei; Liu, Shu; Liu, Zhipeng

2017-01-01

This paper proposes a combination of non-local spatial information and quantum-inspired shuffled frog leaping algorithm to detect underwater objects in sonar images. Specifically, for the first time, the problem of inappropriate filtering degree parameter which commonly occurs in non-local spatial information and seriously affects the denoising performance in sonar images, was solved with the method utilizing a novel filtering degree parameter. Then, a quantum-inspired shuffled frog leaping algorithm based on new search mechanism (QSFLA-NSM) is proposed to precisely and quickly detect sonar images. Each frog individual is directly encoded by real numbers, which can greatly simplify the evolution process of the quantum-inspired shuffled frog leaping algorithm (QSFLA). Meanwhile, a fitness function combining intra-class difference with inter-class difference is adopted to evaluate frog positions more accurately. On this basis, recurring to an analysis of the quantum-behaved particle swarm optimization (QPSO) and the shuffled frog leaping algorithm (SFLA), a new search mechanism is developed to improve the searching ability and detection accuracy. At the same time, the time complexity is further reduced. Finally, the results of comparative experiments using the original sonar images, the UCI data sets and the benchmark functions demonstrate the effectiveness and adaptability of the proposed method.

Underwater sonar image detection: A combination of non-local spatial information and quantum-inspired shuffled frog leaping algorithm

PubMed Central

Liu, Zhipeng

2017-01-01

This paper proposes a combination of non-local spatial information and quantum-inspired shuffled frog leaping algorithm to detect underwater objects in sonar images. Specifically, for the first time, the problem of inappropriate filtering degree parameter which commonly occurs in non-local spatial information and seriously affects the denoising performance in sonar images, was solved with the method utilizing a novel filtering degree parameter. Then, a quantum-inspired shuffled frog leaping algorithm based on new search mechanism (QSFLA-NSM) is proposed to precisely and quickly detect sonar images. Each frog individual is directly encoded by real numbers, which can greatly simplify the evolution process of the quantum-inspired shuffled frog leaping algorithm (QSFLA). Meanwhile, a fitness function combining intra-class difference with inter-class difference is adopted to evaluate frog positions more accurately. On this basis, recurring to an analysis of the quantum-behaved particle swarm optimization (QPSO) and the shuffled frog leaping algorithm (SFLA), a new search mechanism is developed to improve the searching ability and detection accuracy. At the same time, the time complexity is further reduced. Finally, the results of comparative experiments using the original sonar images, the UCI data sets and the benchmark functions demonstrate the effectiveness and adaptability of the proposed method. PMID:28542266

Improved spatial regression analysis of diffusion tensor imaging for lesion detection during longitudinal progression of multiple sclerosis in individual subjects

NASA Astrophysics Data System (ADS)

Liu, Bilan; Qiu, Xing; Zhu, Tong; Tian, Wei; Hu, Rui; Ekholm, Sven; Schifitto, Giovanni; Zhong, Jianhui

2016-03-01

Subject-specific longitudinal DTI study is vital for investigation of pathological changes of lesions and disease evolution. Spatial Regression Analysis of Diffusion tensor imaging (SPREAD) is a non-parametric permutation-based statistical framework that combines spatial regression and resampling techniques to achieve effective detection of localized longitudinal diffusion changes within the whole brain at individual level without a priori hypotheses. However, boundary blurring and dislocation limit its sensitivity, especially towards detecting lesions of irregular shapes. In the present study, we propose an improved SPREAD (dubbed improved SPREAD, or iSPREAD) method by incorporating a three-dimensional (3D) nonlinear anisotropic diffusion filtering method, which provides edge-preserving image smoothing through a nonlinear scale space approach. The statistical inference based on iSPREAD was evaluated and compared with the original SPREAD method using both simulated and in vivo human brain data. Results demonstrated that the sensitivity and accuracy of the SPREAD method has been improved substantially by adapting nonlinear anisotropic filtering. iSPREAD identifies subject-specific longitudinal changes in the brain with improved sensitivity, accuracy, and enhanced statistical power, especially when the spatial correlation is heterogeneous among neighboring image pixels in DTI.

Tumor Control Outcomes After Hypofractionated and Single-Dose Stereotactic Image-Guided Intensity-Modulated Radiotherapy for Extracranial Metastases From Renal Cell Carcinoma

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

Zelefsky, Michael J., E-mail: zelefskm@mskcc.org; Greco, Carlo; Motzer, Robert

2012-04-01

Purpose: To report tumor local progression-free outcomes after treatment with single-dose, image-guided, intensity-modulated radiotherapy and hypofractionated regimens for extracranial metastases from renal cell primary tumors. Patients and Methods: Between 2004 and 2010, 105 lesions from renal cell carcinoma were treated with either single-dose, image-guided, intensity-modulated radiotherapy to a prescription dose of 18-24 Gy (median, 24) or hypofractionation (three or five fractions) with a prescription dose of 20-30 Gy. The median follow-up was 12 months (range, 1-48). Results: The overall 3-year actuarial local progression-free survival for all lesions was 44%. The 3-year local progression-free survival for those who received a highmore » single-dose (24 Gy; n = 45), a low single-dose (<24 Gy; n = 14), or hypofractionation regimens (n = 46) was 88%, 21%, and 17%, respectively (high single dose vs. low single dose, p = .001; high single dose vs. hypofractionation, p < .001). Multivariate analysis revealed the following variables were significant predictors of improved local progression-free survival: 24 Gy dose compared with a lower dose (p = .009) and a single dose vs. hypofractionation (p = .008). Conclusion: High single-dose, image-guided, intensity-modulated radiotherapy is a noninvasive procedure resulting in high probability of local tumor control for metastatic renal cell cancer generally considered radioresistant according to the classic radiobiologic ranking.« less

Reliable structural information from multiscale decomposition with the Mellor-Brady filter

NASA Astrophysics Data System (ADS)

Szilágyi, Tünde; Brady, Michael

2009-08-01

Image-based medical diagnosis typically relies on the (poorly reproducible) subjective classification of textures in order to differentiate between diseased and healthy pathology. Clinicians claim that significant benefits would arise from quantitative measures to inform clinical decision making. The first step in generating such measures is to extract local image descriptors - from noise corrupted and often spatially and temporally coarse resolution medical signals - that are invariant to illumination, translation, scale and rotation of the features. The Dual-Tree Complex Wavelet Transform (DT-CWT) provides a wavelet multiresolution analysis (WMRA) tool e.g. in 2D with good properties, but has limited rotational selectivity. Also, it requires computationally-intensive steering due to the inherently 1D operations performed. The monogenic signal, which is defined in n >= 2D with the Riesz transform gives excellent orientation information without the need for steering. Recent work has suggested the Monogenic Riesz-Laplace wavelet transform as a possible tool for integrating these two concepts into a coherent mathematical framework. We have found that the proposed construction suffers from a lack of rotational invariance and is not optimal for retrieving local image descriptors. In this paper we show: 1. Local frequency and local phase from the monogenic signal are not equivalent, especially in the phase congruency model of a "feature", and so they are not interchangeable for medical image applications. 2. The accuracy of local phase computation may be improved by estimating the denoising parameters while maximizing a new measure of "featureness".

An embedded face-classification system for infrared images on an FPGA

NASA Astrophysics Data System (ADS)

Soto, Javier E.; Figueroa, Miguel

2014-10-01

We present a face-classification architecture for long-wave infrared (IR) images implemented on a Field Programmable Gate Array (FPGA). The circuit is fast, compact and low power, can recognize faces in real time and be embedded in a larger image-processing and computer vision system operating locally on an IR camera. The algorithm uses Local Binary Patterns (LBP) to perform feature extraction on each IR image. First, each pixel in the image is represented as an LBP pattern that encodes the similarity between the pixel and its neighbors. Uniform LBP codes are then used to reduce the number of patterns to 59 while preserving more than 90% of the information contained in the original LBP representation. Then, the image is divided into 64 non-overlapping regions, and each region is represented as a 59-bin histogram of patterns. Finally, the algorithm concatenates all 64 regions to create a 3,776-bin spatially enhanced histogram. We reduce the dimensionality of this histogram using Linear Discriminant Analysis (LDA), which improves clustering and enables us to store an entire database of 53 subjects on-chip. During classification, the circuit applies LBP and LDA to each incoming IR image in real time, and compares the resulting feature vector to each pattern stored in the local database using the Manhattan distance. We implemented the circuit on a Xilinx Artix-7 XC7A100T FPGA and tested it with the UCHThermalFace database, which consists of 28 81 x 150-pixel images of 53 subjects in indoor and outdoor conditions. The circuit achieves a 98.6% hit ratio, trained with 16 images and tested with 12 images of each subject in the database. Using a 100 MHz clock, the circuit classifies 8,230 images per second, and consumes only 309mW.

Dual-modality imaging with a ultrasound-gamma device for oncology

NASA Astrophysics Data System (ADS)

Polito, C.; Pellegrini, R.; Cinti, M. N.; De Vincentis, G.; Lo Meo, S.; Fabbri, A.; Bennati, P.; Cencelli, V. Orsolini; Pani, R.

2018-06-01

Recently, dual-modality systems have been developed, aimed to correlate anatomical and functional information, improving disease localization and helping oncological or surgical treatments. Moreover, due to the growing interest in handheld detectors for preclinical trials or small animal imaging, in this work a new dual modality integrated device, based on a Ultrasounds probe and a small Field of View Single Photon Emission gamma camera, is proposed.

Dendrimer probes for enhanced photostability and localization in fluorescence imaging.

PubMed

Kim, Younghoon; Kim, Sung Hoon; Tanyeri, Melikhan; Katzenellenbogen, John A; Schroeder, Charles M

2013-04-02

Recent advances in fluorescence microscopy have enabled high-resolution imaging and tracking of single proteins and biomolecules in cells. To achieve high spatial resolutions in the nanometer range, bright and photostable fluorescent probes are critically required. From this view, there is a strong need for development of advanced fluorescent probes with molecular-scale dimensions for fluorescence imaging. Polymer-based dendrimer nanoconjugates hold strong potential to serve as versatile fluorescent probes due to an intrinsic capacity for tailored spectral properties such as brightness and emission wavelength. In this work, we report a new, to our knowledge, class of molecular probes based on dye-conjugated dendrimers for fluorescence imaging and single-molecule fluorescence microscopy. We engineered fluorescent dendritic nanoprobes (FDNs) to contain multiple organic dyes and reactive groups for target-specific biomolecule labeling. The photophysical properties of dye-conjugated FDNs (Cy5-FDNs and Cy3-FDNs) were characterized using single-molecule fluorescence microscopy, which revealed greatly enhanced photostability, increased probe brightness, and improved localization precision in high-resolution fluorescence imaging compared to single organic dyes. As proof-of-principle demonstration, Cy5-FDNs were used to assay single-molecule nucleic acid hybridization and for immunofluorescence imaging of microtubules in cytoskeletal networks. In addition, Cy5-FDNs were used as reporter probes in a single-molecule protein pull-down assay to characterize antibody binding and target protein capture. In all cases, the photophysical properties of FDNs resulted in enhanced fluorescence imaging via improved brightness and/or photostability. Copyright © 2013 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Characterization and improvement of highly inclined optical sheet microscopy

NASA Astrophysics Data System (ADS)

Vignolini, T.; Curcio, V.; Gardini, L.; Capitanio, M.; Pavone, F. S.

2018-02-01

Highly Inclined and Laminated Optical sheet (HILO) microscopy is an optical technique that employs a highly inclined laser beam to illuminate the sample with a thin sheet of light that can be scanned through the sample volume1 . HILO is an efficient illumination technique when applied to fluorescence imaging of thick samples owing to the confined illumination volume that allows high contrast imaging while retaining deep scanning capability in a wide-field configuration. The restricted illumination volume is crucial to limit background fluorescence originating from portions of the sample far from the focal plane, especially in applications such as single molecule localization and super-resolution imaging2-4. Despite its widespread use, current literature lacks comprehensive reports of the actual advantages of HILO in these kinds of microscopies. Here, we thoroughly characterize the propagation of a highly inclined beam through fluorescently labeled samples and implement appropriate beam shaping for optimal application to single molecule and super-resolution imaging. We demonstrate that, by reducing the beam size along the refracted axis only, the excitation volume is consequently reduced while maintaining a field of view suitable for single cell imaging. We quantify the enhancement in signal-tobackground ratio with respect to the standard HILO technique and apply our illumination method to dSTORM superresolution imaging of the actin and vimentin cytoskeleton. We define the conditions to achieve localization precisions comparable to state-of-the-art reports, obtain a significant improvement in the image contrast, and enhanced plane selectivity within the sample volume due to the further confinement of the inclined beam.

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs.

PubMed

Chen, Liang-Chieh; Papandreou, George; Kokkinos, Iasonas; Murphy, Kevin; Yuille, Alan L

2018-04-01

In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First, we highlight convolution with upsampled filters, or 'atrous convolution', as a powerful tool in dense prediction tasks. Atrous convolution allows us to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural Networks. It also allows us to effectively enlarge the field of view of filters to incorporate larger context without increasing the number of parameters or the amount of computation. Second, we propose atrous spatial pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales. Third, we improve the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models. The commonly deployed combination of max-pooling and downsampling in DCNNs achieves invariance but has a toll on localization accuracy. We overcome this by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF), which is shown both qualitatively and quantitatively to improve localization performance. Our proposed "DeepLab" system sets the new state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching 79.7 percent mIOU in the test set, and advances the results on three other datasets: PASCAL-Context, PASCAL-Person-Part, and Cityscapes. All of our code is made publicly available online.

Joint detection and localization of multiple anatomical landmarks through learning

NASA Astrophysics Data System (ADS)

Dikmen, Mert; Zhan, Yiqiang; Zhou, Xiang Sean

2008-03-01

Reliable landmark detection in medical images provides the essential groundwork for successful automation of various open problems such as localization, segmentation, and registration of anatomical structures. In this paper, we present a learning-based system to jointly detect (is it there?) and localize (where?) multiple anatomical landmarks in medical images. The contributions of this work exist in two aspects. First, this method takes the advantage from the learning scenario that is able to automatically extract the most distinctive features for multi-landmark detection. Therefore, it is easily adaptable to detect arbitrary landmarks in various kinds of imaging modalities, e.g., CT, MRI and PET. Second, the use of multi-class/cascaded classifier architecture in different phases of the detection stage combined with robust features that are highly efficient in terms of computation time enables a seemingly real time performance, with very high localization accuracy. This method is validated on CT scans of different body sections, e.g., whole body scans, chest scans and abdominal scans. Aside from improved robustness (due to the exploitation of spatial correlations), it gains a run time efficiency in landmark detection. It also shows good scalability performance under increasing number of landmarks.

A New Era of Image Guidance with Magnetic Resonance-guided Radiation Therapy for Abdominal and Thoracic Malignancies

PubMed Central

Paliwal, Bhudatt; Hill, Patrick; Bayouth, John E; Geurts, Mark W; Baschnagel, Andrew M; Bradley, Kristin A; Harari, Paul M; Rosenberg, Stephen; Brower, Jeffrey V; Wojcieszynski, Andrzej P; Hullett, Craig; Bayliss, R A; Labby, Zacariah E; Bassetti, Michael F

2018-01-01

Magnetic resonance-guided radiation therapy (MRgRT) offers advantages for image guidance for radiotherapy treatments as compared to conventional computed tomography (CT)-based modalities. The superior soft tissue contrast of magnetic resonance (MR) enables an improved visualization of the gross tumor and adjacent normal tissues in the treatment of abdominal and thoracic malignancies. Online adaptive capabilities, coupled with advanced motion management of real-time tracking of the tumor, directly allow for high-precision inter-/intrafraction localization. The primary aim of this case series is to describe MR-based interventions for localizing targets not well-visualized with conventional image-guided technologies. The abdominal and thoracic sites of the lung, kidney, liver, and gastric targets are described to illustrate the technological advancement of MR-guidance in radiotherapy. PMID:29872602

Matching CCD images to a stellar catalog using locality-sensitive hashing

NASA Astrophysics Data System (ADS)

Liu, Bo; Yu, Jia-Zong; Peng, Qing-Yu

2018-02-01

The usage of a subset of observed stars in a CCD image to find their corresponding matched stars in a stellar catalog is an important issue in astronomical research. Subgraph isomorphic-based algorithms are the most widely used methods in star catalog matching. When more subgraph features are provided, the CCD images are recognized better. However, when the navigation feature database is large, the method requires more time to match the observing model. To solve this problem, this study investigates further and improves subgraph isomorphic matching algorithms. We present an algorithm based on a locality-sensitive hashing technique, which allocates quadrilateral models in the navigation feature database into different hash buckets and reduces the search range to the bucket in which the observed quadrilateral model is located. Experimental results indicate the effectivity of our method.

Single objective light-sheet microscopy for high-speed whole-cell 3D super-resolution

PubMed Central

Meddens, Marjolein B. M.; Liu, Sheng; Finnegan, Patrick S.; Edwards, Thayne L.; James, Conrad D.; Lidke, Keith A.

2016-01-01

We have developed a method for performing light-sheet microscopy with a single high numerical aperture lens by integrating reflective side walls into a microfluidic chip. These 45° side walls generate light-sheet illumination by reflecting a vertical light-sheet into the focal plane of the objective. Light-sheet illumination of cells loaded in the channels increases image quality in diffraction limited imaging via reduction of out-of-focus background light. Single molecule super-resolution is also improved by the decreased background resulting in better localization precision and decreased photo-bleaching, leading to more accepted localizations overall and higher quality images. Moreover, 2D and 3D single molecule super-resolution data can be acquired faster by taking advantage of the increased illumination intensities as compared to wide field, in the focused light-sheet. PMID:27375939

Low-noise heterodyne receiver for electron cyclotron emission imaging and microwave imaging reflectometry

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

Tobias, B., E-mail: bjtobias@pppl.gov; Domier, C. W.; Luhmann, N. C.

2016-11-15

The critical component enabling electron cyclotron emission imaging (ECEI) and microwave imaging reflectometry (MIR) to resolve 2D and 3D electron temperature and density perturbations is the heterodyne imaging array that collects and downconverts radiated emission and/or reflected signals (50–150 GHz) to an intermediate frequency (IF) band (e.g. 0.1–18 GHz) that can be transmitted by a shielded coaxial cable for further filtering and detection. New circuitry has been developed for this task, integrating gallium arsenide (GaAs) monolithic microwave integrated circuits (MMICs) mounted on a liquid crystal polymer (LCP) substrate. The improved topology significantly increases electromagnetic shielding from out-of-band interference, leads tomore » 10× improvement in the signal-to-noise ratio, and dramatic cost savings through integration. The current design, optimized for reflectometry and edge radiometry on mid-sized tokamaks, has demonstrated >20 dB conversion gain in upper V-band (60-75 GHz). Implementation of the circuit in a multi-channel electron cyclotron emission imaging (ECEI) array will improve the diagnosis of edge-localized modes and fluctuations of the high-confinement, or H-mode, pedestal.« less

Low-noise heterodyne receiver for electron cyclotron emission imaging and microwave imaging reflectometry

DOE PAGES

Tobias, B.; Domier, C. W.; Luhmann, Jr., N. C.; ...

2016-07-25

The critical component enabling electron cyclotron emission imaging (ECEI) and microwave imaging reflectometry (MIR) to resolve 2D and 3D electron temperature and density perturbations is the heterodyne imaging array that collects and downconverts radiated emission and/or reflected signals (50-150 GHz) to an intermediate frequency (IF) band (e.g. 0.1-18 GHz) that can be transmitted by a shielded coaxial cable for further filtering and detection. New circuitry has been developed for this task, integrating gallium arsenide (GaAs) monolithic microwave integrated circuits (MMICs) mounted on a liquid crystal polymer (LCP) substrate. The improved topology significantly increases electromagnetic shielding from out-of-band interference, leads tomore » 10x improvement in the signal-to-noise ratio, and dramatic cost savings through integration. The current design, optimized for reflectometry and edge radiometry on mid-sized tokamaks, has demonstrated >20 dB conversion gain in upper V-band (60-75 GHz). As a result, implementation of the circuit in a multi-channel electron cyclotron emission imaging (ECEI) array will improve the diagnosis of edge-localized modes and fluctuations of the high-confinement, or H-mode, pedestal.« less

Short-term change detection for UAV video

NASA Astrophysics Data System (ADS)

Saur, Günter; Krüger, Wolfgang

2012-11-01

In the last years, there has been an increased use of unmanned aerial vehicles (UAV) for video reconnaissance and surveillance. An important application in this context is change detection in UAV video data. Here we address short-term change detection, in which the time between observations ranges from several minutes to a few hours. We distinguish this task from video motion detection (shorter time scale) and from long-term change detection, based on time series of still images taken between several days, weeks, or even years. Examples for relevant changes we are looking for are recently parked or moved vehicles. As a pre-requisite, a precise image-to-image registration is needed. Images are selected on the basis of the geo-coordinates of the sensor's footprint and with respect to a certain minimal overlap. The automatic imagebased fine-registration adjusts the image pair to a common geometry by using a robust matching approach to handle outliers. The change detection algorithm has to distinguish between relevant and non-relevant changes. Examples for non-relevant changes are stereo disparity at 3D structures of the scene, changed length of shadows, and compression or transmission artifacts. To detect changes in image pairs we analyzed image differencing, local image correlation, and a transformation-based approach (multivariate alteration detection). As input we used color and gradient magnitude images. To cope with local misalignment of image structures we extended the approaches by a local neighborhood search. The algorithms are applied to several examples covering both urban and rural scenes. The local neighborhood search in combination with intensity and gradient magnitude differencing clearly improved the results. Extended image differencing performed better than both the correlation based approach and the multivariate alternation detection. The algorithms are adapted to be used in semi-automatic workflows for the ABUL video exploitation system of Fraunhofer IOSB, see Heinze et. al. 2010.1 In a further step we plan to incorporate more information from the video sequences to the change detection input images, e.g., by image enhancement or by along-track stereo which are available in the ABUL system.

Clinical applications of image guided-intensity modulated radiation therapy (IG-IMRT) for conformal avoidance of normal tissue

NASA Astrophysics Data System (ADS)

Gutierrez, Alonso Navar

2007-12-01

Recent improvements in imaging technology and radiation delivery have led to the development of advanced treatment techniques in radiotherapy which have opened the door for novel therapeutic approaches to improve the efficacy of radiation cancer treatments. Among these advances is image-guided, intensity modulated radiation therapy (IG-IMRT), in which imaging is incorporated to aid in inter-/intra-fractional target localization and to ensure accurate delivery of precise and highly conformal dose distributions. In principle, clinical implementation of IG-IMRT should improve normal tissue sparing and permit effective biological dose escalation thus widening the radiation therapeutic window and lead to increases in survival through improved local control of primary neoplastic diseases. Details of the development of three clinical applications made possible solely with IG-IMRT radiation delivery techniques are presented: (1) Laparoscopically implanted tissue expander radiotherapy (LITE-RT) has been developed to enhance conformal avoidance of normal tissue during the treatment of intra-abdominopelvic cancers. LITE-RT functions by geometrically displacing surrounding normal tissue and isolating the target volume through the interfractional inflation of a custom-shaped tissue expander throughout the course of treatment. (2) The unique delivery geometry of helical tomotherapy, a novel form of IG-IMRT, enables the delivery of composite treatment plan m which whole brain radiotherapy (WBRT) with hippocampal avoidance, hypothesized to reduce the risk of memory function decline and improve the patient's quality of life, and simultaneously integrated boost to multiple brain metastases to improve intracranial tumor control is achieved. (3) Escalation of biological dose to targets through integrated, selective subvolume boosts have been shown to efficiently increase tumor dose without significantly increasing normal tissue dose. Helical tomotherapy was used to investigate the feasibility of delivering a simultaneously integrated subvolume boost to canine nasal tumors and was found to dramatically increase estimated 1-year tumor control probability (TCP) without increasing the dose to the eyes, so as to preserve vision, and to the brain, so as to prevent neuropathy.

New cardiac cameras: single-photon emission CT and PET.

PubMed

Slomka, Piotr J; Berman, Daniel S; Germano, Guido

2014-07-01

Nuclear cardiology instrumentation has evolved significantly in the recent years. Concerns about radiation dose and long acquisition times have propelled developments of dedicated high-efficiency cardiac SPECT scanners. Novel collimator designs, such as multipinhole or locally focusing collimators arranged in geometries that are optimized for cardiac imaging, have been implemented to enhance photon-detection sensitivity. Some of these new SPECT scanners use solid-state photon detectors instead of photomultipliers to improve image quality and to reduce the scanner footprint. These new SPECT devices allow dramatic up to 7-fold reduction in acquisition times or similar reduction in radiation dose. In addition, new hardware for photon attenuation correction allowing ultralow radiation doses has been offered by some vendors. To mitigate photon attenuation artifacts for the new SPECT scanners not equipped with attenuation correction hardware, 2-position (upright-supine or prone-supine) imaging has been proposed. PET hardware developments have been primarily driven by the requirements of oncologic imaging, but cardiac imaging can benefit from improved PET image quality and improved sensitivity of 3D systems. The time-of-flight reconstruction combined with resolution recovery techniques is now implemented by all major PET vendors. These new methods improve image contrast and image resolution and reduce image noise. High-sensitivity 3D PET without interplane septa allows reduced radiation dose for cardiac perfusion imaging. Simultaneous PET/MR hybrid system has been developed. Solid-state PET detectors with avalanche photodiodes or digital silicon photomultipliers have been introduced, and they offer improved imaging characteristics and reduced sensitivity to electromagnetic MR fields. Higher maximum count rate of the new PET detectors allows routine first-pass Rb-82 imaging, with 3D PET acquisition enabling clinical utilization of dynamic imaging with myocardial flow measurements for this tracer. The availability of high-end CT component in most PET/CT configurations enables hybrid multimodality cardiac imaging protocols with calcium scoring or CT angiography or both. Copyright © 2014. Published by Elsevier Inc.

Combination of image descriptors for the exploration of cultural photographic collections

NASA Astrophysics Data System (ADS)

Bhowmik, Neelanjan; Gouet-Brunet, Valérie; Bloch, Gabriel; Besson, Sylvain

2017-01-01

The rapid growth of image digitization and collections in recent years makes it challenging and burdensome to organize, categorize, and retrieve similar images from voluminous collections. Content-based image retrieval (CBIR) is immensely convenient in this context. A considerable number of local feature detectors and descriptors are present in the literature of CBIR. We propose a model to anticipate the best feature combinations for image retrieval-related applications. Several spatial complementarity criteria of local feature detectors are analyzed and then engaged in a regression framework to find the optimal combination of detectors for a given dataset and are better adapted for each given image; the proposed model is also useful to optimally fix some other parameters, such as the k in k-nearest neighbor retrieval. Three public datasets of various contents and sizes are employed to evaluate the proposal, which is legitimized by improving the quality of retrieval notably facing classical approaches. Finally, the proposed image search engine is applied to the cultural photographic collections of a French museum, where it demonstrates its added value for the exploration and promotion of these contents at different levels from their archiving up to their exhibition in or ex situ.

A fast non-local means algorithm based on integral image and reconstructed similar kernel

NASA Astrophysics Data System (ADS)

Lin, Zheng; Song, Enmin

2018-03-01

Image denoising is one of the essential methods in digital image processing. The non-local means (NLM) denoising approach is a remarkable denoising technique. However, its time complexity of the computation is high. In this paper, we design a fast NLM algorithm based on integral image and reconstructed similar kernel. First, the integral image is introduced in the traditional NLM algorithm. In doing so, it reduces a great deal of repetitive operations in the parallel processing, which will greatly improves the running speed of the algorithm. Secondly, in order to amend the error of the integral image, we construct a similar window resembling the Gaussian kernel in the pyramidal stacking pattern. Finally, in order to eliminate the influence produced by replacing the Gaussian weighted Euclidean distance with Euclidean distance, we propose a scheme to construct a similar kernel with a size of 3 x 3 in a neighborhood window which will reduce the effect of noise on a single pixel. Experimental results demonstrate that the proposed algorithm is about seventeen times faster than the traditional NLM algorithm, yet produce comparable results in terms of Peak Signal-to- Noise Ratio (the PSNR increased 2.9% in average) and perceptual image quality.

SPEQTACLE: An automated generalized fuzzy C-means algorithm for tumor delineation in PET

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

Lapuyade-Lahorgue, Jérôme; Visvikis, Dimitris; Hatt, Mathieu, E-mail: hatt@univ-brest.fr

Purpose: Accurate tumor delineation in positron emission tomography (PET) images is crucial in oncology. Although recent methods achieved good results, there is still room for improvement regarding tumors with complex shapes, low signal-to-noise ratio, and high levels of uptake heterogeneity. Methods: The authors developed and evaluated an original clustering-based method called spatial positron emission quantification of tumor—Automatic Lp-norm estimation (SPEQTACLE), based on the fuzzy C-means (FCM) algorithm with a generalization exploiting a Hilbertian norm to more accurately account for the fuzzy and non-Gaussian distributions of PET images. An automatic and reproducible estimation scheme of the norm on an image-by-image basismore » was developed. Robustness was assessed by studying the consistency of results obtained on multiple acquisitions of the NEMA phantom on three different scanners with varying acquisition parameters. Accuracy was evaluated using classification errors (CEs) on simulated and clinical images. SPEQTACLE was compared to another FCM implementation, fuzzy local information C-means (FLICM) and fuzzy locally adaptive Bayesian (FLAB). Results: SPEQTACLE demonstrated a level of robustness similar to FLAB (variability of 14% ± 9% vs 14% ± 7%, p = 0.15) and higher than FLICM (45% ± 18%, p < 0.0001), and improved accuracy with lower CE (14% ± 11%) over both FLICM (29% ± 29%) and FLAB (22% ± 20%) on simulated images. Improvement was significant for the more challenging cases with CE of 17% ± 11% for SPEQTACLE vs 28% ± 22% for FLAB (p = 0.009) and 40% ± 35% for FLICM (p < 0.0001). For the clinical cases, SPEQTACLE outperformed FLAB and FLICM (15% ± 6% vs 37% ± 14% and 30% ± 17%, p < 0.004). Conclusions: SPEQTACLE benefitted from the fully automatic estimation of the norm on a case-by-case basis. This promising approach will be extended to multimodal images and multiclass estimation in future developments.« less

Employing temporal self-similarity across the entire time domain in computed tomography reconstruction

PubMed Central

Kazantsev, D.; Van Eyndhoven, G.; Lionheart, W. R. B.; Withers, P. J.; Dobson, K. J.; McDonald, S. A.; Atwood, R.; Lee, P. D.

2015-01-01

There are many cases where one needs to limit the X-ray dose, or the number of projections, or both, for high frame rate (fast) imaging. Normally, it improves temporal resolution but reduces the spatial resolution of the reconstructed data. Fortunately, the redundancy of information in the temporal domain can be employed to improve spatial resolution. In this paper, we propose a novel regularizer for iterative reconstruction of time-lapse computed tomography. The non-local penalty term is driven by the available prior information and employs all available temporal data to improve the spatial resolution of each individual time frame. A high-resolution prior image from the same or a different imaging modality is used to enhance edges which remain stationary throughout the acquisition time while dynamic features tend to be regularized spatially. Effective computational performance together with robust improvement in spatial and temporal resolution makes the proposed method a competitive tool to state-of-the-art techniques. PMID:25939621

Recent Trends in Soft Tissue Infection Imaging

PubMed Central

Petruzzi, Nicholas; Shanthly, Nylla; Thakur, Mathew

2009-01-01

This article discusses the current techniques and future directions of infection imaging with particular attention to respiratory, CNS, abdominal, and postoperative infections. The agents currently in use localize to areas of infection and inflammation. An infection specific imaging agent would greatly improve the utility of scintigraphy in imaging occult infections. The superior spatial resolution of 18F-FDG PET and its lack of reliance on a functional immune system, gives this agent certain advantages over the other radiopharmaceuticals. In respiratory infection imaging, an important advancement would be the ability to quantitatively delineate lung inflammation, allowing one to monitor the therapeutic response in a variety of conditions. Current studies suggest PET should be considered the most accurate quantitative method. Scintigraphy has much to offer in localizing abdominal infection as well as inflammation. We may begin to see a gradual increase in the usage of FDG PET in detecting occult abdominal infections. Commonly used modalities for imaging inflammatory bowel disease are scintigraphy with 111In-oxine/99mTc-HMPAO labeled autologous white blood cells. The literature on CNS infection imaging is relatively scarce. Few clinical studies have been performed and numerous new agents have been developed for this use with varying results. Further studies are needed to more clearly delineate the future direction of this field. In evaluating the post-operative spine, 99mTc-ciprofloxacin SPECT was reported to be >80% sensitive in patients more than 6 months post-surgery. FDG PET has also been suggested for this purpose and may play a larger role than originally thought. It appears PET/CT is gaining support, especially in imaging those with fever of unknown origin or nonfunctional immune systems. While an infection specific agent is lacking, the development of one would greatly advance our ability to detect, localize, and quantify infections. Overall, imaging such an agent via SPECT/CT or PET/CT will pave the way for greater clinical reliability in the localization of infection. PMID:19187804

Combined electroencephalography-functional magnetic resonance imaging and electrical source imaging improves localization of pediatric focal epilepsy.

PubMed

Centeno, Maria; Tierney, Tim M; Perani, Suejen; Shamshiri, Elhum A; St Pier, Kelly; Wilkinson, Charlotte; Konn, Daniel; Vulliemoz, Serge; Grouiller, Frédéric; Lemieux, Louis; Pressler, Ronit M; Clark, Christopher A; Cross, J Helen; Carmichael, David W

2017-08-01

Surgical treatment in epilepsy is effective if the epileptogenic zone (EZ) can be correctly localized and characterized. Here we use simultaneous electroencephalography-functional magnetic resonance imaging (EEG-fMRI) data to derive EEG-fMRI and electrical source imaging (ESI) maps. Their yield and their individual and combined ability to (1) localize the EZ and (2) predict seizure outcome were then evaluated. Fifty-three children with drug-resistant epilepsy underwent EEG-fMRI. Interictal discharges were mapped using both EEG-fMRI hemodynamic responses and ESI. A single localization was derived from each individual test (EEG-fMRI global maxima [GM]/ESI maximum) and from the combination of both maps (EEG-fMRI/ESI spatial intersection). To determine the localization accuracy and its predictive performance, the individual and combined test localizations were compared to the presumed EZ and to the postsurgical outcome. Fifty-two of 53 patients had significant maps: 47 of 53 for EEG-fMRI, 44 of 53 for ESI, and 34 of 53 for both. The EZ was well characterized in 29 patients; 26 had an EEG-fMRI GM localization that was correct in 11, 22 patients had ESI localization that was correct in 17, and 12 patients had combined EEG-fMRI and ESI that was correct in 11. Seizure outcome following resection was correctly predicted by EEG-fMRI GM in 8 of 20 patients, and by the ESI maximum in 13 of 16. The combined EEG-fMRI/ESI region entirely predicted outcome in 9 of 9 patients, including 3 with no lesion visible on MRI. EEG-fMRI combined with ESI provides a simple unbiased localization that may predict surgery better than each individual test, including in MRI-negative patients. Ann Neurol 2017;82:278-287. © 2017 American Neurological Association.

Local image variance of 7 Tesla SWI is a new technique for preoperative characterization of diffusely infiltrating gliomas: correlation with tumour grade and IDH1 mutational status.

PubMed

Grabner, Günther; Kiesel, Barbara; Wöhrer, Adelheid; Millesi, Matthias; Wurzer, Aygül; Göd, Sabine; Mallouhi, Ammar; Knosp, Engelbert; Marosi, Christine; Trattnig, Siegfried; Wolfsberger, Stefan; Preusser, Matthias; Widhalm, Georg

2017-04-01

To investigate the value of local image variance (LIV) as a new technique for quantification of hypointense microvascular susceptibility-weighted imaging (SWI) structures at 7 Tesla for preoperative glioma characterization. Adult patients with neuroradiologically suspected diffusely infiltrating gliomas were prospectively recruited and 7 Tesla SWI was performed in addition to standard imaging. After tumour segmentation, quantification of intratumoural SWI hypointensities was conducted by the SWI-LIV technique. Following surgery, the histopathological tumour grade and isocitrate dehydrogenase 1 (IDH1)-R132H mutational status was determined and SWI-LIV values were compared between low-grade gliomas (LGG) and high-grade gliomas (HGG), IDH1-R132H negative and positive tumours, as well as gliomas with significant and non-significant contrast-enhancement (CE) on MRI. In 30 patients, 9 LGG and 21 HGG were diagnosed. The calculation of SWI-LIV values was feasible in all tumours. Significantly higher mean SWI-LIV values were found in HGG compared to LGG (92.7 versus 30.8; p < 0.0001), IDH1-R132H negative compared to IDH1-R132H positive gliomas (109.9 versus 38.3; p < 0.0001) and tumours with significant CE compared to non-significant CE (120.1 versus 39.0; p < 0.0001). Our data indicate that 7 Tesla SWI-LIV might improve preoperative characterization of diffusely infiltrating gliomas and thus optimize patient management by quantification of hypointense microvascular structures. • 7 Tesla local image variance helps to quantify hypointense susceptibility-weighted imaging structures. • SWI-LIV is significantly increased in high-grade and IDH1-R132H negative gliomas. • SWI-LIV is a promising technique for improved preoperative glioma characterization. • Preoperative management of diffusely infiltrating gliomas will be optimized.

Image thumbnails that represent blur and noise.

PubMed

Samadani, Ramin; Mauer, Timothy A; Berfanger, David M; Clark, James H

2010-02-01

The information about the blur and noise of an original image is lost when a standard image thumbnail is generated by filtering and subsampling. Image browsing becomes difficult since the standard thumbnails do not distinguish between high-quality and low-quality originals. In this paper, an efficient algorithm with a blur-generating component and a noise-generating component preserves the local blur and the noise of the originals. The local blur is rapidly estimated using a scale-space expansion of the standard thumbnail and subsequently used to apply a space-varying blur to the thumbnail. The noise is estimated and rendered by using multirate signal transformations that allow most of the processing to occur at the lower spatial sampling rate of the thumbnail. The new thumbnails provide a quick, natural way for users to identify images of good quality. A subjective evaluation shows the new thumbnails are more representative of their originals for blurry images. The noise generating component improves the results for noisy images, but degrades the results for textured images. The blur generating component of the new thumbnails may always be used to advantage. The decision to use the noise generating component of the new thumbnails should be based on testing with the particular image mix expected for the application.

Application of oral contrast media in coregistered positron emission tomography-CT.

PubMed

Dizendorf, Elena V; Treyer, Valerie; Von Schulthess, Gustav K; Hany, Thomas F

2002-08-01

Coregistration of positron emission tomography (PET) and CT images results in significantly improved localization of abnormal FDG uptake compared with PET images alone. For delineation of intestinal structures, application of oral contrast media is a standard procedure in CT. The influence of oral contrast agents in PET imaging using CT data for attenuation correction was evaluated in a comparative study on an in-line PET-CT system. Sixty patients referred for PET-CT were evaluated in two groups. One group of 30 patients received oral Gastrografin 45 min before data acquisition. The second group received no contrast medium. PET images were reconstructed, using CT data for attenuation correction. Image analysis was performed by two reviewers in consensus, using a 4-point scale comparing FDG-uptake in the gastrointestinal tract in PET images of both groups. Furthermore, correlation of FDG uptake and localization of contrast media in the intestinal tract in CT images were determined. No significant difference in FDG uptake in PET images in all regions of the gastrointestinal tract except the ascending colon was seen in both groups. No correlation was found in the location of increased FDG uptake and contrast media in the CT images. An oral contrast agent can be used for coregistered PET-CT without the introduction of artifacts in PET.

Reconstructed Image Spatial Resolution of Multiple Coincidences Compton Imager

NASA Astrophysics Data System (ADS)

Andreyev, Andriy; Sitek, Arkadiusz; Celler, Anna

2010-02-01

We study the multiple coincidences Compton imager (MCCI) which is based on a simultaneous acquisition of several photons emitted in cascade from a single nuclear decay. Theoretically, this technique should provide a major improvement in localization of a single radioactive source as compared to a standard Compton camera. In this work, we investigated the performance and limitations of MCCI using Monte Carlo computer simulations. Spatial resolutions of the reconstructed point source have been studied as a function of the MCCI parameters, including geometrical dimensions and detector characteristics such as materials, energy and spatial resolutions.

Contour detection improved by context-adaptive surround suppression.

PubMed

Sang, Qiang; Cai, Biao; Chen, Hao

2017-01-01

Recently, many image processing applications have taken advantage of a psychophysical and neurophysiological mechanism, called "surround suppression" to extract object contour from a natural scene. However, these traditional methods often adopt a single suppression model and a fixed input parameter called "inhibition level", which needs to be manually specified. To overcome these drawbacks, we propose a novel model, called "context-adaptive surround suppression", which can automatically control the effect of surround suppression according to image local contextual features measured by a surface estimator based on a local linear kernel. Moreover, a dynamic suppression method and its stopping mechanism are introduced to avoid manual intervention. The proposed algorithm is demonstrated and validated by a broad range of experimental results.

Intraoperative Near-Infrared Optical Imaging Can Localize Gadolinium-Enhancing Gliomas During Surgery

PubMed Central

Lee, John Y-K.; Thawani, Jayesh P.; Pierce, John; Zeh, Ryan; Martinez-Lage, Maria; Chanin, Michelle; Venegas, Ollin; Nims, Sarah; Learned, Kim; Keating, Jane; Singhal, Sunil

2016-01-01

Background Although real-time localization of gliomas has improved with intraoperative image guidance systems, these tools are limited by brain shift, surgical cavity deformation, and expense. Objective To propose a novel method to perform near-infrared (NIR) imaging during glioma resections based on preclinical and clinical investigations, in order to localize tumors and to potentially identify residual disease. Methods Fifteen patients were identified and administered an FDA-approved, NIR contrast agent (Second Window indocyanine green [ICG], 5 mg/kg) prior to surgical resection. An NIR camera was utilized to localize the tumor prior to resection and to visualize surgical margins following resection. Neuropathology and MR imaging data were used to assess the accuracy and precision of NIR-fluorescence in identifying tumor tissue. Results NIR visualization of 15 gliomas (10 glioblastoma multiforme, 1 anaplastic astrocytoma, 2 low grade astrocytoma, 1 juvenile pilocytic astrocytoma, and 1 ganglioglioma) was performed 22.7 hours (mean) after intravenous injection of ICG. During surgery, 12/15 tumors were visualized with the NIR camera. The mean signal-to-background ratio was 9.5 ± 0.8 and fluorescence was noted through the dura to a maximum parenchymal depth of 13 mm. The best predictor of positive fluorescence was enhancement on T1-weighted imaging; this correlated with SBR (P = .03). Non-enhancing tumors did not demonstrate NIR fluorescence. Using pathology as the gold standard, the technique demonstrated a sensitivity of 98% and specificity of 45% to identify tumor in gadolinium-enhancing specimens (n = 71). Conclusion Using Second Window ICG, gadolinium-enhancing tumors can be localized through brain parenchyma intraoperatively. Its utility for margin detection is promising but limited by lower specificity. PMID:27741220

SU-E-T-669: Radiosurgery Failure for Trigeminal Neuralgia: A Study of Radiographic Spatial Fidelity

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

Howe, J; Spalding, A

Purpose: Management of Trigeminal Neuralgia with radiosurgery is well established, but often met with limited success. Recent advancements in imaging afford improvements in target localization for radiosurgery. Methods: A Trigeminal Neuralgia radiosurgery specific protocol was established for MR enhancement of the trigeminal nerve using a CISS scan with slice spacing of 0.7mm. Computed Tomography simulation was performed using axial slices on a 40 slice CT with slice spacing of 0.6mm. These datasets were registered using a mutual information algorithm and localized in a stereotactic coordinate system. Image registration between the MR and CT was evaluated for each patient by amore » Medical Physicist to ensure accuracy. The dorsal root entry zone target was defined on the CISS MR by a Neurosurgeon and dose calculations performed on the localized CT. Treatment plans were reviewed and approved by a Radiation Oncologist and Neurosurgeon. Image guided radiosurgery was delivered using positioning tolerance of 0.5mm and 1°. Eight patients with Trigeminal Neuralgia were treated with this protocol. Results: Seven patients reported a favorable response to treatment with average Barrow Neurological Index pain score of four before treatment and one following treatment. Only one patient had a BNI>1 following treatment and review of the treatment plan revealed that the CISS MR was registered to the CT via a low resolution (5mm slice spacing) T2 MR. All other patients had CISS MR registered directly with the localized CT. This patient was retreated 6 months later using direct registration between CISS MR and localized CT and subsequently responded to treatment with a BNI of one. Conclusion: Frameless radiosurgery offers an effective solution to Trigeminal Neuralgia management provided appropriate technology and imaging protocols (utilizing submillimeter imaging) are established and maintained.« less

Three-dimensional localization and optical imaging of objects in turbid media with independent component analysis.

PubMed

Xu, M; Alrubaiee, M; Gayen, S K; Alfano, R R

2005-04-01

A new approach for optical imaging and localization of objects in turbid media that makes use of the independent component analysis (ICA) from information theory is demonstrated. Experimental arrangement realizes a multisource illumination of a turbid medium with embedded objects and a multidetector acquisition of transmitted light on the medium boundary. The resulting spatial diversity and multiple angular observations provide robust data for three-dimensional localization and characterization of absorbing and scattering inhomogeneities embedded in a turbid medium. ICA of the perturbations in the spatial intensity distribution on the medium boundary sorts out the embedded objects, and their locations are obtained from Green's function analysis based on any appropriate light propagation model. Imaging experiments were carried out on two highly scattering samples of thickness approximately 50 times the transport mean-free path of the respective medium. One turbid medium had two embedded absorptive objects, and the other had four scattering objects. An independent component separation of the signal, in conjunction with diffusive photon migration theory, was used to locate the embedded inhomogeneities. In both cases, improved lateral and axial localizations of the objects over the result obtained by use of common photon migration reconstruction algorithms were achieved. The approach is applicable to different medium geometries, can be used with any suitable photon propagation model, and is amenable to near-real-time imaging applications.

Improving cerebellar segmentation with statistical fusion

NASA Astrophysics Data System (ADS)

Plassard, Andrew J.; Yang, Zhen; Prince, Jerry L.; Claassen, Daniel O.; Landman, Bennett A.

2016-03-01

The cerebellum is a somatotopically organized central component of the central nervous system well known to be involved with motor coordination and increasingly recognized roles in cognition and planning. Recent work in multiatlas labeling has created methods that offer the potential for fully automated 3-D parcellation of the cerebellar lobules and vermis (which are organizationally equivalent to cortical gray matter areas). This work explores the trade offs of using different statistical fusion techniques and post hoc optimizations in two datasets with distinct imaging protocols. We offer a novel fusion technique by extending the ideas of the Selective and Iterative Method for Performance Level Estimation (SIMPLE) to a patch-based performance model. We demonstrate the effectiveness of our algorithm, Non- Local SIMPLE, for segmentation of a mixed population of healthy subjects and patients with severe cerebellar anatomy. Under the first imaging protocol, we show that Non-Local SIMPLE outperforms previous gold-standard segmentation techniques. In the second imaging protocol, we show that Non-Local SIMPLE outperforms previous gold standard techniques but is outperformed by a non-locally weighted vote with the deeper population of atlases available. This work advances the state of the art in open source cerebellar segmentation algorithms and offers the opportunity for routinely including cerebellar segmentation in magnetic resonance imaging studies that acquire whole brain T1-weighted volumes with approximately 1 mm isotropic resolution.

Improvement in the workflow efficiency of treating non-emergency outpatients by using a WLAN-based real-time location system in a level I trauma center.

PubMed

Stübig, Timo; Suero, Eduardo; Zeckey, Christian; Min, William; Janzen, Laura; Citak, Musa; Krettek, Christian; Hüfner, Tobias; Gaulke, Ralph

2013-01-01

Patient localization can improve workflow in outpatient settings, which might lead to lower costs. The existing wireless local area network (WLAN) architecture in many hospitals opens up the possibility of adopting real-time patient tracking systems for capturing and processing position data; once captured, these data can be linked with clinical patient data. To analyze the effect of a WLAN-based real-time patient localization system for tracking outpatients in our level I trauma center. Outpatients from April to August 2009 were included in the study, which was performed in two different stages. In phase I, patient tracking was performed with the real-time location system, but acquired data were not displayed to the personnel. In phase II tracking, the acquired data were automatically collected and displayed. Total treatment time was the primary outcome parameter. Statistical analysis was performed using multiple linear regression, with the significance level set at 0.05. Covariates included sex, age, type of encounter, prioritization, treatment team, number of residents, and radiographic imaging. 1045 patients were included in our study (540 in phase I and 505 in phase 2). An overall improvement of efficiency, as determined by a significantly decreased total treatment time (23.7%) from phase I to phase II, was noted. Additionally, significantly lower treatment times were noted for phase II patients even when other factors were considered (increased numbers of residents, the addition of imaging diagnostics, and comparison among various localization zones). WLAN-based real-time patient localization systems can reduce process inefficiencies associated with manual patient identification and tracking.

Improvement in the workflow efficiency of treating non-emergency outpatients by using a WLAN-based real-time location system in a level I trauma center

PubMed Central

Stübig, Timo; Suero, Eduardo; Zeckey, Christian; Min, William; Janzen, Laura; Citak, Musa; Krettek, Christian; Hüfner, Tobias; Gaulke, Ralph

2013-01-01

Background Patient localization can improve workflow in outpatient settings, which might lead to lower costs. The existing wireless local area network (WLAN) architecture in many hospitals opens up the possibility of adopting real-time patient tracking systems for capturing and processing position data; once captured, these data can be linked with clinical patient data. Objective To analyze the effect of a WLAN-based real-time patient localization system for tracking outpatients in our level I trauma center. Methods Outpatients from April to August 2009 were included in the study, which was performed in two different stages. In phase I, patient tracking was performed with the real-time location system, but acquired data were not displayed to the personnel. In phase II tracking, the acquired data were automatically collected and displayed. Total treatment time was the primary outcome parameter. Statistical analysis was performed using multiple linear regression, with the significance level set at 0.05. Covariates included sex, age, type of encounter, prioritization, treatment team, number of residents, and radiographic imaging. Results/discussion 1045 patients were included in our study (540 in phase I and 505 in phase 2). An overall improvement of efficiency, as determined by a significantly decreased total treatment time (23.7%) from phase I to phase II, was noted. Additionally, significantly lower treatment times were noted for phase II patients even when other factors were considered (increased numbers of residents, the addition of imaging diagnostics, and comparison among various localization zones). Conclusions WLAN-based real-time patient localization systems can reduce process inefficiencies associated with manual patient identification and tracking. PMID:23676246

A Learning Based Fiducial-driven Registration Scheme for Evaluating Laser Ablation Changes in Neurological Disorders.

PubMed

Wan, Tao; Bloch, B Nicolas; Danish, Shabbar; Madabhushi, Anant

2014-11-20

In this work, we present a novel learning based fiducial driven registration (LeFiR) scheme which utilizes a point matching technique to identify the optimal configuration of landmarks to better recover deformation between a target and a moving image. Moreover, we employ the LeFiR scheme to model the localized nature of deformation introduced by a new treatment modality - laser induced interstitial thermal therapy (LITT) for treating neurological disorders. Magnetic resonance (MR) guided LITT has recently emerged as a minimally invasive alternative to craniotomy for local treatment of brain diseases (such as glioblastoma multiforme (GBM), epilepsy). However, LITT is currently only practised as an investigational procedure world-wide due to lack of data on longer term patient outcome following LITT. There is thus a need to quantitatively evaluate treatment related changes between post- and pre-LITT in terms of MR imaging markers. In order to validate LeFiR, we tested the scheme on a synthetic brain dataset (SBD) and in two real clinical scenarios for treating GBM and epilepsy with LITT. Four experiments under different deformation profiles simulating localized ablation effects of LITT on MRI were conducted on 286 pairs of SBD images. The training landmark configurations were obtained through 2000 iterations of registration where the points with consistently best registration performance were selected. The estimated landmarks greatly improved the quality metrics compared to a uniform grid (UniG) placement scheme, a speeded-up robust features (SURF) based method, and a scale-invariant feature transform (SIFT) based method as well as a generic free-form deformation (FFD) approach. The LeFiR method achieved average 90% improvement in recovering the local deformation compared to 82% for the uniform grid placement, 62% for the SURF based approach, and 16% for the generic FFD approach. On the real GBM and epilepsy data, the quantitative results showed that LeFiR outperformed UniG by 28% improvement in average.

Real-time ultrasound transducer localization in fluoroscopy images by transfer learning from synthetic training data.

PubMed

Heimann, Tobias; Mountney, Peter; John, Matthias; Ionasec, Razvan

2014-12-01

The fusion of image data from trans-esophageal echography (TEE) and X-ray fluoroscopy is attracting increasing interest in minimally-invasive treatment of structural heart disease. In order to calculate the needed transformation between both imaging systems, we employ a discriminative learning (DL) based approach to localize the TEE transducer in X-ray images. The successful application of DL methods is strongly dependent on the available training data, which entails three challenges: (1) the transducer can move with six degrees of freedom meaning it requires a large number of images to represent its appearance, (2) manual labeling is time consuming, and (3) manual labeling has inherent errors. This paper proposes to generate the required training data automatically from a single volumetric image of the transducer. In order to adapt this system to real X-ray data, we use unlabeled fluoroscopy images to estimate differences in feature space density and correct covariate shift by instance weighting. Two approaches for instance weighting, probabilistic classification and Kullback-Leibler importance estimation (KLIEP), are evaluated for different stages of the proposed DL pipeline. An analysis on more than 1900 images reveals that our approach reduces detection failures from 7.3% in cross validation on the test set to zero and improves the localization error from 1.5 to 0.8mm. Due to the automatic generation of training data, the proposed system is highly flexible and can be adapted to any medical device with minimal efforts. Copyright © 2014 Elsevier B.V. All rights reserved.

Fourier Domain Iterative Approach to Optical Sectioning of 3d Translucent Objects for Ophthalmology Purposes

NASA Astrophysics Data System (ADS)

Razguli, A. V.; Iroshnikov, N. G.; Larichev, A. V.; Romanenko, T. E.; Goncharov, A. S.

2017-05-01

In this paper we deal with the problem of optical sectioning. This is a post processing step while investigating of 3D translucent medical objects based on rapid refocusing of the imaging system by the adaptive optics technique. Each image, captured in focal plane, can be represented as the sum of in-focus true section and out-of-focus images of the neighboring sections of the depth that are undesirable in the subsequent reconstruction of 3D object. The problem of optical sectioning under consideration is to elaborate a robust approach capable of obtaining a stack of cross section images purified from such distortions. For a typical sectioning statement arising in ophthalmology we propose a local iterative method in Fourier spectral plane. Compared to the non-local constant parameter selection for the whole spectral domain, the method demonstrates both improved sectioning results and a good level of scalability when implemented on multi-core CPUs.

Advanced Demonstration of Motion Correction for Ship-to-Ship Passive Inspections

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

Ziock, Klaus-Peter; Boehnen, Chris Bensing; Ernst, Joseph

2013-09-30

Passive radiation detection is a key tool for detecting illicit nuclear materials. In maritime applications it is most effective against small vessels where attenuation is of less concern. Passive imaging provides: discrimination between localized (threat) and distributed (non-threat) sources, removal of background fluctuations due to nearby shorelines and structures, source localization to an individual craft in crowded waters, and background subtracted spectra. Unfortunately, imaging methods cannot be easily applied in ship-to-ship inspections because relative motion of the vessels blurs the results over many pixels, significantly reducing sensitivity. This is particularly true for the smaller water craft where passive inspections aremore » most valuable. In this project we performed tests and improved the performance of an instrument (developed earlier under, “Motion Correction for Ship-to-Ship Passive Inspections”) that uses automated tracking of a target vessel in visible-light images to generate a 3D radiation map of the target vessel from data obtained using a gamma-ray imager.« less

DIRBoost-an algorithm for boosting deformable image registration: application to lung CT intra-subject registration.

PubMed

Muenzing, Sascha E A; van Ginneken, Bram; Viergever, Max A; Pluim, Josien P W

2014-04-01

We introduce a boosting algorithm to improve on existing methods for deformable image registration (DIR). The proposed DIRBoost algorithm is inspired by the theory on hypothesis boosting, well known in the field of machine learning. DIRBoost utilizes a method for automatic registration error detection to obtain estimates of local registration quality. All areas detected as erroneously registered are subjected to boosting, i.e. undergo iterative registrations by employing boosting masks on both the fixed and moving image. We validated the DIRBoost algorithm on three different DIR methods (ANTS gSyn, NiftyReg, and DROP) on three independent reference datasets of pulmonary image scan pairs. DIRBoost reduced registration errors significantly and consistently on all reference datasets for each DIR algorithm, yielding an improvement of the registration accuracy by 5-34% depending on the dataset and the registration algorithm employed. Copyright © 2014 Elsevier B.V. All rights reserved.

Recent advances in MRI technology: Implications for image quality and patient safety

PubMed Central

Sobol, Wlad T.

2012-01-01

Recent advances in MRI technology are presented, with emphasis on how this new technology impacts clinical operations (better image quality, faster exam times, and improved throughput). In addition, implications for patient safety are discussed with emphasis on the risk of patient injury due to either high local specific absorption rate (SAR) or large cumulative energy doses delivered during long exam times. Patient comfort issues are examined as well. PMID:23961024

Final contract report : real-time EMS helicopter video feasibility study

DOT National Transportation Integrated Search

2001-11-01

The purpose of this project was to determine whether the use of ground-based video imaging by local rescue squad personnel and Pegasus medical staff is technically and organizationally feasible as a tool to improve pre-hospital care provided to crash...

FTY720-loaded poly(DL-lactide-co-glycolide) electrospun scaffold significantly increases microvessel density over 7 days in streptozotocin-induced diabetic C57b16/J mice: preliminary results.

PubMed

Bowers, D T; Chhabra, P; Langman, L; Botchwey, E A; Brayman, K L

2011-11-01

Nanofiber scaffolds could improve islet transplant success by physically mimicking the shape of extracellular matrix and by acting as a drug-delivery vehicle. Scaffolds implanted in alternate transplant sites must be prevascularized or very quickly vascularized following transplantation to prevent hypoxia-induced islet necrosis. The local release of the S1P prodrug FTY720 induces diameter enlargement and increases in length density. The objective of this preliminary study was to evaluate length and diameter differences between diabetic and nondiabetic animals implanted with FTY720-containing electrospun scaffolds using intravital imaging of dorsal skinfold window chambers. Electrospun mats of randomly oriented fibers we created from polymer solutions of PLAGA (50:50 LA:GA) with and without FTY720 loaded at a ratio of 1:200 (FTY720:PLAGA by wt). The implanted fiber mats were 4 mm in diameter and ∼0.2 mm thick. Increases in length density and vessel diameter were assessed by automated analysis of images over 7 days in RAVE, a Matlab program. Image analysis of repeated measures of microvessel metrics demonstrated a significant increase in the length density from day 0 to day 7 in the moderately diabetic animals of this preliminary study (P < .05). Furthermore, significant differences in length density at day 0 and day 3 were found between recently STZ-induced moderately diabetic and nondiabetic animals in response to FTY720 local release (P < .05, Student t test). Driving the islet revascularization process using local release of factors, such as FTY720, from biodegradable polymers makes an attractive system for the improvement of islet transplant success. Preliminary study results suggest that a recently induced moderately diabetic state may potentiate the mechanism by which local release of FTY720 from polymer fibers increases length density of microvessels. Therefore, local release of S1P receptor-targeted drugs is under further investigation for improvement of transplanted islet function. Copyright © 2011. Published by Elsevier Inc.

Iterative deep convolutional encoder-decoder network for medical image segmentation.

PubMed

Jung Uk Kim; Hak Gu Kim; Yong Man Ro

2017-07-01

In this paper, we propose a novel medical image segmentation using iterative deep learning framework. We have combined an iterative learning approach and an encoder-decoder network to improve segmentation results, which enables to precisely localize the regions of interest (ROIs) including complex shapes or detailed textures of medical images in an iterative manner. The proposed iterative deep convolutional encoder-decoder network consists of two main paths: convolutional encoder path and convolutional decoder path with iterative learning. Experimental results show that the proposed iterative deep learning framework is able to yield excellent medical image segmentation performances for various medical images. The effectiveness of the proposed method has been proved by comparing with other state-of-the-art medical image segmentation methods.

Preprocessing with image denoising and histogram equalization for endoscopy image analysis using texture analysis.

PubMed

Hiroyasu, Tomoyuki; Hayashinuma, Katsutoshi; Ichikawa, Hiroshi; Yagi, Nobuaki

2015-08-01

A preprocessing method for endoscopy image analysis using texture analysis is proposed. In a previous study, we proposed a feature value that combines a co-occurrence matrix and a run-length matrix to analyze the extent of early gastric cancer from images taken with narrow-band imaging endoscopy. However, the obtained feature value does not identify lesion zones correctly due to the influence of noise and halation. Therefore, we propose a new preprocessing method with a non-local means filter for de-noising and contrast limited adaptive histogram equalization. We have confirmed that the pattern of gastric mucosa in images can be improved by the proposed method. Furthermore, the lesion zone is shown more correctly by the obtained color map.

Sub-pattern based multi-manifold discriminant analysis for face recognition

NASA Astrophysics Data System (ADS)

Dai, Jiangyan; Guo, Changlu; Zhou, Wei; Shi, Yanjiao; Cong, Lin; Yi, Yugen

2018-04-01

In this paper, we present a Sub-pattern based Multi-manifold Discriminant Analysis (SpMMDA) algorithm for face recognition. Unlike existing Multi-manifold Discriminant Analysis (MMDA) approach which is based on holistic information of face image for recognition, SpMMDA operates on sub-images partitioned from the original face image and then extracts the discriminative local feature from the sub-images separately. Moreover, the structure information of different sub-images from the same face image is considered in the proposed method with the aim of further improve the recognition performance. Extensive experiments on three standard face databases (Extended YaleB, CMU PIE and AR) demonstrate that the proposed method is effective and outperforms some other sub-pattern based face recognition methods.

A level set method for cupping artifact correction in cone-beam CT

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

Xie, Shipeng; Li, Haibo; Ge, Qi

2015-08-15

Purpose: To reduce cupping artifacts and improve the contrast-to-noise ratio in cone-beam computed tomography (CBCT). Methods: A level set method is proposed to reduce cupping artifacts in the reconstructed image of CBCT. The authors derive a local intensity clustering property of the CBCT image and define a local clustering criterion function of the image intensities in a neighborhood of each point. This criterion function defines an energy in terms of the level set functions, which represent a segmentation result and the cupping artifacts. The cupping artifacts are estimated as a result of minimizing this energy. Results: The cupping artifacts inmore » CBCT are reduced by an average of 90%. The results indicate that the level set-based algorithm is practical and effective for reducing the cupping artifacts and preserving the quality of the reconstructed image. Conclusions: The proposed method focuses on the reconstructed image without requiring any additional physical equipment, is easily implemented, and provides cupping correction through a single-scan acquisition. The experimental results demonstrate that the proposed method successfully reduces the cupping artifacts.« less

Thyroid Carcinoma

PubMed

Ahmed, Najeeb; Niyaz, Kashif; Borakati, Aditya; Marafi, Fahad; Birk, Rubinder; Usmani, Sharjeel

2018-02-26

Differentiated thyroid cancer (DTC) has a good prognosis overall; however, lifelong follow-up is required for many cases. Radioiodine planar imaging with iodine-123 (I-123) or radioiodine-131 (I-131) remains the standard in the follow-up after initial surgery and ablation of residual thyroid tissue using I-131 therapy. Radioiodine imaging is also used in risk-stratifying and for staging of thyroid cancer, and in long-term follow-up. Unfortunately, the lack of anatomical detail on planar gamma camera imaging and superimposition of areas presenting with increased radioiodine uptake can make accurate diagnosis and localization of radioiodine-avid metastatic disease challenging, leading to false positive results and potentially to over-treatment of patients. Hybrid SPECT/CT allows precise anatomical localization and superior characterization of foci of increased tracer uptake when compared to planar imaging. This, in turn, allows the differentiation of pathological and physiological uptake, increasing the accuracy of image interpretation and ultimately improving the accuracy of DTC staging and subsequent patient management. In this review, we look at the unique and emerging role that SPECT/CT plays in the management of DTC, illustrated by examples from our own clinical practice. Creative Commons Attribution License

Super-resolution reconstruction of MR image with a novel residual learning network algorithm

NASA Astrophysics Data System (ADS)

Shi, Jun; Liu, Qingping; Wang, Chaofeng; Zhang, Qi; Ying, Shihui; Xu, Haoyu

2018-04-01

Spatial resolution is one of the key parameters of magnetic resonance imaging (MRI). The image super-resolution (SR) technique offers an alternative approach to improve the spatial resolution of MRI due to its simplicity. Convolutional neural networks (CNN)-based SR algorithms have achieved state-of-the-art performance, in which the global residual learning (GRL) strategy is now commonly used due to its effectiveness for learning image details for SR. However, the partial loss of image details usually happens in a very deep network due to the degradation problem. In this work, we propose a novel residual learning-based SR algorithm for MRI, which combines both multi-scale GRL and shallow network block-based local residual learning (LRL). The proposed LRL module works effectively in capturing high-frequency details by learning local residuals. One simulated MRI dataset and two real MRI datasets have been used to evaluate our algorithm. The experimental results show that the proposed SR algorithm achieves superior performance to all of the other compared CNN-based SR algorithms in this work.

Bubble-generating nano-lipid carriers for ultrasound/CT imaging-guided efficient tumor therapy.

PubMed

Zhang, Nan; Li, Jia; Hou, Ruirui; Zhang, Jiangnan; Wang, Pei; Liu, Xinyang; Zhang, Zhenzhong

2017-12-20

Ideal therapeutic effectiveness of chemotherapy is obtained only when tumor cells are exposed to a maximal drug concentration, which is often hindered by dose-limiting toxicity. We designed a bubble-generating liposomal delivery system by introducing ammonium bicarbonate and gold nanorods into folic acid-conjugated liposomes to allow both multimodal imaging and the local release of drug (doxorubicin) with hyperthermia. The key component, ammonium bicarbonate, allows a controlled, rapid release of doxorubicin to provide an effective drug concentration in the tumor microenvironment. An in vitro temperature-triggered drug release study showed that cumulative release improved more than two-fold. In addition, in vitro and in vivo studies indicated that local heat treatment or ultrasonic cavitation enhanced the therapeutic efficiency greatly. The delivery system could also serve as an excellent contrast agent to allow ultrasonic imaging and computerized tomography imaging simultaneously to further achieve the aim of accurate diagnostics. Results of this study showed that this versatile bubble-generating liposome is a promising system to provide optimal therapeutic effects that are guided by multimodal imaging. Copyright © 2017 Elsevier B.V. All rights reserved.

Localization and Classification of Paddy Field Pests using a Saliency Map and Deep Convolutional Neural Network.

PubMed

Liu, Ziyi; Gao, Junfeng; Yang, Guoguo; Zhang, Huan; He, Yong

2016-02-11

We present a pipeline for the visual localization and classification of agricultural pest insects by computing a saliency map and applying deep convolutional neural network (DCNN) learning. First, we used a global contrast region-based approach to compute a saliency map for localizing pest insect objects. Bounding squares containing targets were then extracted, resized to a fixed size, and used to construct a large standard database called Pest ID. This database was then utilized for self-learning of local image features which were, in turn, used for classification by DCNN. DCNN learning optimized the critical parameters, including size, number and convolutional stride of local receptive fields, dropout ratio and the final loss function. To demonstrate the practical utility of using DCNN, we explored different architectures by shrinking depth and width, and found effective sizes that can act as alternatives for practical applications. On the test set of paddy field images, our architectures achieved a mean Accuracy Precision (mAP) of 0.951, a significant improvement over previous methods.

Localization and Classification of Paddy Field Pests using a Saliency Map and Deep Convolutional Neural Network

PubMed Central

Liu, Ziyi; Gao, Junfeng; Yang, Guoguo; Zhang, Huan; He, Yong

2016-01-01

We present a pipeline for the visual localization and classification of agricultural pest insects by computing a saliency map and applying deep convolutional neural network (DCNN) learning. First, we used a global contrast region-based approach to compute a saliency map for localizing pest insect objects. Bounding squares containing targets were then extracted, resized to a fixed size, and used to construct a large standard database called Pest ID. This database was then utilized for self-learning of local image features which were, in turn, used for classification by DCNN. DCNN learning optimized the critical parameters, including size, number and convolutional stride of local receptive fields, dropout ratio and the final loss function. To demonstrate the practical utility of using DCNN, we explored different architectures by shrinking depth and width, and found effective sizes that can act as alternatives for practical applications. On the test set of paddy field images, our architectures achieved a mean Accuracy Precision (mAP) of 0.951, a significant improvement over previous methods. PMID:26864172

3D tumor localization through real-time volumetric x-ray imaging for lung cancer radiotherapy.

PubMed

Li, Ruijiang; Lewis, John H; Jia, Xun; Gu, Xuejun; Folkerts, Michael; Men, Chunhua; Song, William Y; Jiang, Steve B

2011-05-01

To evaluate an algorithm for real-time 3D tumor localization from a single x-ray projection image for lung cancer radiotherapy. Recently, we have developed an algorithm for reconstructing volumetric images and extracting 3D tumor motion information from a single x-ray projection [Li et al., Med. Phys. 37, 2822-2826 (2010)]. We have demonstrated its feasibility using a digital respiratory phantom with regular breathing patterns. In this work, we present a detailed description and a comprehensive evaluation of the improved algorithm. The algorithm was improved by incorporating respiratory motion prediction. The accuracy and efficiency of using this algorithm for 3D tumor localization were then evaluated on (1) a digital respiratory phantom, (2) a physical respiratory phantom, and (3) five lung cancer patients. These evaluation cases include both regular and irregular breathing patterns that are different from the training dataset. For the digital respiratory phantom with regular and irregular breathing, the average 3D tumor localization error is less than 1 mm which does not seem to be affected by amplitude change, period change, or baseline shift. On an NVIDIA Tesla C1060 graphic processing unit (GPU) card, the average computation time for 3D tumor localization from each projection ranges between 0.19 and 0.26 s, for both regular and irregular breathing, which is about a 10% improvement over previously reported results. For the physical respiratory phantom, an average tumor localization error below 1 mm was achieved with an average computation time of 0.13 and 0.16 s on the same graphic processing unit (GPU) card, for regular and irregular breathing, respectively. For the five lung cancer patients, the average tumor localization error is below 2 mm in both the axial and tangential directions. The average computation time on the same GPU card ranges between 0.26 and 0.34 s. Through a comprehensive evaluation of our algorithm, we have established its accuracy in 3D tumor localization to be on the order of 1 mm on average and 2 mm at 95 percentile for both digital and physical phantoms, and within 2 mm on average and 4 mm at 95 percentile for lung cancer patients. The results also indicate that the accuracy is not affected by the breathing pattern, be it regular or irregular. High computational efficiency can be achieved on GPU, requiring 0.1-0.3 s for each x-ray projection.

Time-resolved 3D MR velocity mapping at 3T: improved navigator-gated assessment of vascular anatomy and blood flow.

PubMed

Markl, Michael; Harloff, Andreas; Bley, Thorsten A; Zaitsev, Maxim; Jung, Bernd; Weigang, Ernst; Langer, Mathias; Hennig, Jürgen; Frydrychowicz, Alex

2007-04-01

To evaluate an improved image acquisition and data-processing strategy for assessing aortic vascular geometry and 3D blood flow at 3T. In a study with five normal volunteers and seven patients with known aortic pathology, prospectively ECG-gated cine three-dimensional (3D) MR velocity mapping with improved navigator gating, real-time adaptive k-space ordering and dynamic adjustment of the navigator acceptance criteria was performed. In addition to morphological information and three-directional blood flow velocities, phase-contrast (PC)-MRA images were derived from the same data set, which permitted 3D isosurface rendering of vascular boundaries in combination with visualization of blood-flow patterns. Analysis of navigator performance and image quality revealed improved scan efficiencies of 63.6%+/-10.5% and temporal resolution (<50 msec) compared to previous implementations. Semiquantitative evaluation of image quality by three independent observers demonstrated excellent general image appearance with moderate blurring and minor ghosting artifacts. Results from volunteer and patient examinations illustrate the potential of the improved image acquisition and data-processing strategy for identifying normal and pathological blood-flow characteristics. Navigator-gated time-resolved 3D MR velocity mapping at 3T in combination with advanced data processing is a powerful tool for performing detailed assessments of global and local blood-flow characteristics in the aorta to describe or exclude vascular alterations. Copyright (c) 2007 Wiley-Liss, Inc.

Dual-modality imaging of function and physiology

NASA Astrophysics Data System (ADS)

Hasegawa, Bruce H.; Iwata, Koji; Wong, Kenneth H.; Wu, Max C.; Da Silva, Angela; Tang, Hamilton R.; Barber, William C.; Hwang, Andrew B.; Sakdinawat, Anne E.

2002-04-01

Dual-modality imaging is a technique where computed tomography or magnetic resonance imaging is combined with positron emission tomography or single-photon computed tomography to acquire structural and functional images with an integrated system. The data are acquired during a single procedure with the patient on a table viewed by both detectors to facilitate correlation between the structural and function images. The resulting data can be useful for localization for more specific diagnosis of disease. In addition, the anatomical information can be used to compensate the correlated radionuclide data for physical perturbations such as photon attenuation, scatter radiation, and partial volume errors. Thus, dual-modality imaging provides a priori information that can be used to improve both the visual quality and the quantitative accuracy of the radionuclide images. Dual-modality imaging systems also are being developed for biological research that involves small animals. The small-animal dual-modality systems offer advantages for measurements that currently are performed invasively using autoradiography and tissue sampling. By acquiring the required data noninvasively, dual-modality imaging has the potential to allow serial studies in a single animal, to perform measurements with fewer animals, and to improve the statistical quality of the data.

Matching forensic sketches to mug shot photos.

PubMed

Klare, Brendan F; Li, Zhifeng; Jain, Anil K

2011-03-01

The problem of matching a forensic sketch to a gallery of mug shot images is addressed in this paper. Previous research in sketch matching only offered solutions to matching highly accurate sketches that were drawn while looking at the subject (viewed sketches). Forensic sketches differ from viewed sketches in that they are drawn by a police sketch artist using the description of the subject provided by an eyewitness. To identify forensic sketches, we present a framework called local feature-based discriminant analysis (LFDA). In LFDA, we individually represent both sketches and photos using SIFT feature descriptors and multiscale local binary patterns (MLBP). Multiple discriminant projections are then used on partitioned vectors of the feature-based representation for minimum distance matching. We apply this method to match a data set of 159 forensic sketches against a mug shot gallery containing 10,159 images. Compared to a leading commercial face recognition system, LFDA offers substantial improvements in matching forensic sketches to the corresponding face images. We were able to further improve the matching performance using race and gender information to reduce the target gallery size. Additional experiments demonstrate that the proposed framework leads to state-of-the-art accuracys when matching viewed sketches.

Nobel Prize Recipient Eric Betzig Presents Lecture on Efforts to Improve High-Resolution Microscopy | Poster

Cancer.gov

Eric Betzig, Ph.D., a 2014 recipient of the Nobel Prize in Chemistry and a scientist at Janelia Research Campus (JRC), Howard Hughes Medical Institute, in Ashburn, Va., visited NCI at Frederick on Sept. 10 to present a Distinguished Scientist lecture and discuss the latest high-resolution microscopy techniques. Betzig co-invented photoactivation localization microscopy (PALM) in collaboration with scientists at NIH. PALM achieves 10-fold improvement in spatial resolution of cells, going from the resolution limit of approximately 250 nm in standard optical microscopy down to approximately 20 nm, thus producing a so-called “super-resolution” image. Spatial resolution refers to the clarity of an image or, in other words, the smallest details that can be observed from an image.

High-speed spectral domain optical coherence tomography using non-uniform fast Fourier transform

PubMed Central

Chan, Kenny K. H.; Tang, Shuo

2010-01-01

The useful imaging range in spectral domain optical coherence tomography (SD-OCT) is often limited by the depth dependent sensitivity fall-off. Processing SD-OCT data with the non-uniform fast Fourier transform (NFFT) can improve the sensitivity fall-off at maximum depth by greater than 5dB concurrently with a 30 fold decrease in processing time compared to the fast Fourier transform with cubic spline interpolation method. NFFT can also improve local signal to noise ratio (SNR) and reduce image artifacts introduced in post-processing. Combined with parallel processing, NFFT is shown to have the ability to process up to 90k A-lines per second. High-speed SD-OCT imaging is demonstrated at camera-limited 100 frames per second on an ex-vivo squid eye. PMID:21258551

Functional imaging of photovoltaic materials

NASA Astrophysics Data System (ADS)

Leite, Marina

For the past two decades, extensive efforts have been made to increase the short-circuit current (Jsc) of non-epitaxial solar cells to achieve higher efficiency devices. Yet, improvements in the overall device performance are still limited by the open-circuit voltage (Voc). We address this critical limiting factor of all promising materials for photovoltaics by realizing a novel nanoscale imaging platform with unprecedented spatial resolution (<100 nm), based on a variant of Kelvin-probe force microscopy. We mapped the local Voc of a variety of inorganic materials, and measured local changes >150 mV in CIGS, not resolved by conventional electrical measurements. To identify the origin of the instability frequently observed in perovskite solar cells, we leveraged our recently developed method to scan one frame in 16 seconds to spatially and temporally resolve their photo-voltage. Surprisingly, we observed local and reversible changes in the Voc of the devices upon post-illumination treatments. Our innovative functional imaging is non destructive and can be applied to other optoelectronic devices, such as LEDs and photodetectors. The author acknowledge APS and NSF (Award # 16-10833) for funding.

Segmentation of acute pyelonephritis area on kidney SPECT images using binary shape analysis

NASA Astrophysics Data System (ADS)

Wu, Chia-Hsiang; Sun, Yung-Nien; Chiu, Nan-Tsing

1999-05-01

Acute pyelonephritis is a serious disease in children that may result in irreversible renal scarring. The ability to localize the site of urinary tract infection and the extent of acute pyelonephritis has considerable clinical importance. In this paper, we are devoted to segment the acute pyelonephritis area from kidney SPECT images. A two-step algorithm is proposed. First, the original images are translated into binary versions by automatic thresholding. Then the acute pyelonephritis areas are located by finding convex deficiencies in the obtained binary images. This work gives important diagnosis information for physicians and improves the quality of medical care for children acute pyelonephritis disease.

The pre-image problem in kernel methods.

PubMed

Kwok, James Tin-yau; Tsang, Ivor Wai-hung

2004-11-01

In this paper, we address the problem of finding the pre-image of a feature vector in the feature space induced by a kernel. This is of central importance in some kernel applications, such as on using kernel principal component analysis (PCA) for image denoising. Unlike the traditional method which relies on nonlinear optimization, our proposed method directly finds the location of the pre-image based on distance constraints in the feature space. It is noniterative, involves only linear algebra and does not suffer from numerical instability or local minimum problems. Evaluations on performing kernel PCA and kernel clustering on the USPS data set show much improved performance.

Image restoration by minimizing zero norm of wavelet frame coefficients

NASA Astrophysics Data System (ADS)

Bao, Chenglong; Dong, Bin; Hou, Likun; Shen, Zuowei; Zhang, Xiaoqun; Zhang, Xue

2016-11-01

In this paper, we propose two algorithms, namely the extrapolated proximal iterative hard thresholding (EPIHT) algorithm and the EPIHT algorithm with line-search, for solving the {{\\ell }}0-norm regularized wavelet frame balanced approach for image restoration. Under the theoretical framework of Kurdyka-Łojasiewicz property, we show that the sequences generated by the two algorithms converge to a local minimizer with linear convergence rate. Moreover, extensive numerical experiments on sparse signal reconstruction and wavelet frame based image restoration problems including CT reconstruction, image deblur, demonstrate the improvement of {{\\ell }}0-norm based regularization models over some prevailing ones, as well as the computational efficiency of the proposed algorithms.

Self-localization for an autonomous mobile robot based on an omni-directional vision system

NASA Astrophysics Data System (ADS)

Chiang, Shu-Yin; Lin, Kuang-Yu; Chia, Tsorng-Lin

2013-12-01

In this study, we designed an autonomous mobile robot based on the rules of the Federation of International Robotsoccer Association (FIRA) RoboSot category, integrating the techniques of computer vision, real-time image processing, dynamic target tracking, wireless communication, self-localization, motion control, path planning, and control strategy to achieve the contest goal. The self-localization scheme of the mobile robot is based on the algorithms featured in the images from its omni-directional vision system. In previous works, we used the image colors of the field goals as reference points, combining either dual-circle or trilateration positioning of the reference points to achieve selflocalization of the autonomous mobile robot. However, because the image of the game field is easily affected by ambient light, positioning systems exclusively based on color model algorithms cause errors. To reduce environmental effects and achieve the self-localization of the robot, the proposed algorithm is applied in assessing the corners of field lines by using an omni-directional vision system. Particularly in the mid-size league of the RobotCup soccer competition, selflocalization algorithms based on extracting white lines from the soccer field have become increasingly popular. Moreover, white lines are less influenced by light than are the color model of the goals. Therefore, we propose an algorithm that transforms the omni-directional image into an unwrapped transformed image, enhancing the extraction features. The process is described as follows: First, radical scan-lines were used to process omni-directional images, reducing the computational load and improving system efficiency. The lines were radically arranged around the center of the omni-directional camera image, resulting in a shorter computational time compared with the traditional Cartesian coordinate system. However, the omni-directional image is a distorted image, which makes it difficult to recognize the position of the robot. Therefore, image transformation was required to implement self-localization. Second, we used an approach to transform the omni-directional images into panoramic images. Hence, the distortion of the white line can be fixed through the transformation. The interest points that form the corners of the landmark were then located using the features from accelerated segment test (FAST) algorithm. In this algorithm, a circle of sixteen pixels surrounding the corner candidate is considered and is a high-speed feature detector in real-time frame rate applications. Finally, the dual-circle, trilateration, and cross-ratio projection algorithms were implemented in choosing the corners obtained from the FAST algorithm and localizing the position of the robot. The results demonstrate that the proposed algorithm is accurate, exhibiting a 2-cm position error in the soccer field measuring 600 cm2 x 400 cm2.

Flame-Vortex Interactions in Microgravity to Improve Models of Turbulent Combustion

NASA Technical Reports Server (NTRS)

Driscoll, James F.

1999-01-01

A unique flame-vortex interaction experiment is being operated in microgravity in order to obtain fundamental data to assess the Theory of Flame Stretch which will be used to improve models of turbulent combustion. The experiment provides visual images of the physical process by which an individual eddy in a turbulent flow increases the flame surface area, changes the local flame propagation speed, and can extinguish the reaction. The high quality microgravity images provide benchmark data that are free from buoyancy effects. Results are used to assess Direct Numerical Simulations of Dr. K. Kailasanath at NRL, which were run for the same conditions.

DSA Image Blood Vessel Skeleton Extraction Based on Anti-concentration Diffusion and Level Set Method

NASA Astrophysics Data System (ADS)

Xu, Jing; Wu, Jian; Feng, Daming; Cui, Zhiming

Serious types of vascular diseases such as carotid stenosis, aneurysm and vascular malformation may lead to brain stroke, which are the third leading cause of death and the number one cause of disability. In the clinical practice of diagnosis and treatment of cerebral vascular diseases, how to do effective detection and description of the vascular structure of two-dimensional angiography sequence image that is blood vessel skeleton extraction has been a difficult study for a long time. This paper mainly discussed two-dimensional image of blood vessel skeleton extraction based on the level set method, first do the preprocessing to the DSA image, namely uses anti-concentration diffusion model for the effective enhancement and uses improved Otsu local threshold segmentation technology based on regional division for the image binarization, then vascular skeleton extraction based on GMM (Group marching method) with fast sweeping theory was actualized. Experiments show that our approach not only improved the time complexity, but also make a good extraction results.

Multi-focus image fusion based on window empirical mode decomposition

NASA Astrophysics Data System (ADS)

Qin, Xinqiang; Zheng, Jiaoyue; Hu, Gang; Wang, Jiao

2017-09-01

In order to improve multi-focus image fusion quality, a novel fusion algorithm based on window empirical mode decomposition (WEMD) is proposed. This WEMD is an improved form of bidimensional empirical mode decomposition (BEMD), due to its decomposition process using the adding window principle, effectively resolving the signal concealment problem. We used WEMD for multi-focus image fusion, and formulated different fusion rules for bidimensional intrinsic mode function (BIMF) components and the residue component. For fusion of the BIMF components, the concept of the Sum-modified-Laplacian was used and a scheme based on the visual feature contrast adopted; when choosing the residue coefficients, a pixel value based on the local visibility was selected. We carried out four groups of multi-focus image fusion experiments and compared objective evaluation criteria with other three fusion methods. The experimental results show that the proposed fusion approach is effective and performs better at fusing multi-focus images than some traditional methods.

Automated Geo/Co-Registration of Multi-Temporal Very-High-Resolution Imagery.

PubMed

Han, Youkyung; Oh, Jaehong

2018-05-17

For time-series analysis using very-high-resolution (VHR) multi-temporal satellite images, both accurate georegistration to the map coordinates and subpixel-level co-registration among the images should be conducted. However, applying well-known matching methods, such as scale-invariant feature transform and speeded up robust features for VHR multi-temporal images, has limitations. First, they cannot be used for matching an optical image to heterogeneous non-optical data for georegistration. Second, they produce a local misalignment induced by differences in acquisition conditions, such as acquisition platform stability, the sensor's off-nadir angle, and relief displacement of the considered scene. Therefore, this study addresses the problem by proposing an automated geo/co-registration framework for full-scene multi-temporal images acquired from a VHR optical satellite sensor. The proposed method comprises two primary steps: (1) a global georegistration process, followed by (2) a fine co-registration process. During the first step, two-dimensional multi-temporal satellite images are matched to three-dimensional topographic maps to assign the map coordinates. During the second step, a local analysis of registration noise pixels extracted between the multi-temporal images that have been mapped to the map coordinates is conducted to extract a large number of well-distributed corresponding points (CPs). The CPs are finally used to construct a non-rigid transformation function that enables minimization of the local misalignment existing among the images. Experiments conducted on five Kompsat-3 full scenes confirmed the effectiveness of the proposed framework, showing that the georegistration performance resulted in an approximately pixel-level accuracy for most of the scenes, and the co-registration performance further improved the results among all combinations of the georegistered Kompsat-3 image pairs by increasing the calculated cross-correlation values.

Interventional Molecular Imaging.

PubMed

Solomon, Stephen B; Cornelis, Francois

2016-04-01

Although molecular imaging has had a dramatic impact on diagnostic imaging, it has only recently begun to be integrated into interventional procedures. Its significant impact is attributed to its ability to provide noninvasive, physiologic information that supplements conventional morphologic imaging. The four major interventional opportunities for molecular imaging are, first, to provide guidance to localize a target; second, to provide tissue analysis to confirm that the target has been reached; third, to provide in-room, posttherapy assessment; and fourth, to deliver targeted therapeutics. With improved understanding and application of(18)F-FDG, as well as the addition of new molecular probes beyond(18)F-FDG, the future holds significant promise for the expansion of molecular imaging into the realm of interventional procedures. © 2016 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

Determination of skeleton and sign map for phase obtaining from a single ESPI image

NASA Astrophysics Data System (ADS)

Yang, Xia; Yu, Qifeng; Fu, Sihua

2009-06-01

A robust method of determining the sign map and skeletons for ESPI images is introduced in this paper. ESPI images have high speckle noise which makes it difficult to obtain the fringe information, especially from a single image. To overcome the effects of high speckle noise, local directional computing windows are designed according to the fringe directions. Then by calculating the gradients from the filtered image in directional windows, sign map and good skeletons can be determined robustly. Based on the sign map, single image phase-extracting methods such as quadrature transform can be improved. And based on skeletons, fringe phases can be obtained directly by normalization methods. Experiments show that this new method is robust and effective for extracting phase from a single ESPI fringe image.

LASER BIOLOGY AND MEDICINE: Combination of fluorescence imaging and local spectrophotometry in fluorescence diagnostics of early cancer of larynx and bronchi

NASA Astrophysics Data System (ADS)

Sokolov, Vladimir V.; Filonenko, E. V.; Telegina, L. V.; Boulgakova, N. N.; Smirnov, V. V.

2002-11-01

The results of comparative studies of autofluorescence and 5-ALA-induced fluorescence of protoporphyrin IX, used in the diagnostics of early cancer of larynx and bronchi, are presented. The autofluorescence and 5-ALA-induced fluorescence images of larynx and bronchial tissues are analysed during the endoscopic study. The method of local spectrophotometry is used to verify findings obtained from fluorescence images. It is shown that such a combined approach can be efficiently used to improve the diagnostics of precancer and early cancer, to detect a primary multiple tumours, as well as for the diagnostics of a residual tumour or an early recurrence after the endoscopic, surgery or X-ray treatment. The developed approach allows one to minimise the number of false-positive results and to reduce the number of biopsies, which are commonly used in the white-light bronchoscopy search for occult cancerous loci.

Locally adaptive vector quantization: Data compression with feature preservation

NASA Technical Reports Server (NTRS)

Cheung, K. M.; Sayano, M.

1992-01-01

A study of a locally adaptive vector quantization (LAVQ) algorithm for data compression is presented. This algorithm provides high-speed one-pass compression and is fully adaptable to any data source and does not require a priori knowledge of the source statistics. Therefore, LAVQ is a universal data compression algorithm. The basic algorithm and several modifications to improve performance are discussed. These modifications are nonlinear quantization, coarse quantization of the codebook, and lossless compression of the output. Performance of LAVQ on various images using irreversible (lossy) coding is comparable to that of the Linde-Buzo-Gray algorithm, but LAVQ has a much higher speed; thus this algorithm has potential for real-time video compression. Unlike most other image compression algorithms, LAVQ preserves fine detail in images. LAVQ's performance as a lossless data compression algorithm is comparable to that of Lempel-Ziv-based algorithms, but LAVQ uses far less memory during the coding process.

Single objective light-sheet microscopy for high-speed whole-cell 3D super-resolution

DOE PAGES

Meddens, Marjolein B. M.; Liu, Sheng; Finnegan, Patrick S.; ...

2016-01-01

Here, we have developed a method for performing light-sheet microscopy with a single high numerical aperture lens by integrating reflective side walls into a microfluidic chip. These 45° side walls generate light-sheet illumination by reflecting a vertical light-sheet into the focal plane of the objective. Light-sheet illumination of cells loaded in the channels increases image quality in diffraction limited imaging via reduction of out-of-focus background light. Single molecule super-resolution is also improved by the decreased background resulting in better localization precision and decreased photo-bleaching, leading to more accepted localizations overall and higher quality images. Moreover, 2D and 3D single moleculemore » super-resolution data can be acquired faster by taking advantage of the increased illumination intensities as compared to wide field, in the focused light-sheet.« less

Boosting CNN performance for lung texture classification using connected filtering

NASA Astrophysics Data System (ADS)

Tarando, Sebastián. Roberto; Fetita, Catalin; Kim, Young-Wouk; Cho, Hyoun; Brillet, Pierre-Yves

2018-02-01

Infiltrative lung diseases describe a large group of irreversible lung disorders requiring regular follow-up with CT imaging. Quantifying the evolution of the patient status imposes the development of automated classification tools for lung texture. This paper presents an original image pre-processing framework based on locally connected filtering applied in multiresolution, which helps improving the learning process and boost the performance of CNN for lung texture classification. By removing the dense vascular network from images used by the CNN for lung classification, locally connected filters provide a better discrimination between different lung patterns and help regularizing the classification output. The approach was tested in a preliminary evaluation on a 10 patient database of various lung pathologies, showing an increase of 10% in true positive rate (on average for all the cases) with respect to the state of the art cascade of CNNs for this task.

Is choline PET useful for identifying intraprostatic tumour lesions? A literature review.

PubMed

Chan, Joachim; Syndikus, Isabel; Mahmood, Shelan; Bell, Lynn; Vinjamuri, Sobhan

2015-09-01

More than 80% of patients with intermediate-risk or high-risk localized prostate cancer are cured with radiation doses of 74-78 Gy, but high doses increase the risk for late bowel and bladder toxicity among long-term survivors. Dose painting, defined as dose escalation to areas in the prostate containing the tumour, rather than to the whole gland, minimizes dose to normal tissues and hence toxicity. It requires accurate identification of the location and size of these lesions, for which functional MRI is the current gold standard. Many studies have assessed the use of choline PET in staging newly diagnosed patients. This review will discuss important imaging variables affecting the accuracy of choline PET scans, how choline PET contributes to tumour identification and is used in radiotherapy planning and how PET can improve the patient pathway involving prostate radiotherapy. In summary, the available literature shows that the accuracy of choline PET improves with higher tracer doses and delayed imaging (although the optimal uptake time is unclear), and tumour identification by MRI is improved by the addition of PET imaging. We propose future research with prolonged choline uptake time and multiphase imaging, which may further improve accuracy.

Reliability of MEG source imaging of anterior temporal spikes: analysis of an intracranially characterized spike focus.

PubMed

Wennberg, Richard; Cheyne, Douglas

2014-05-01

To assess the reliability of MEG source imaging (MSI) of anterior temporal spikes through detailed analysis of the localization and orientation of source solutions obtained for a large number of spikes that were separately confirmed by intracranial EEG to be focally generated within a single, well-characterized spike focus. MSI was performed on 64 identical right anterior temporal spikes from an anterolateral temporal neocortical spike focus. The effects of different volume conductors (sphere and realistic head model), removal of noise with low frequency filters (LFFs) and averaging multiple spikes were assessed in terms of the reliability of the source solutions. MSI of single spikes resulted in scattered dipole source solutions that showed reasonable reliability for localization at the lobar level, but only for solutions with a goodness-of-fit exceeding 80% using a LFF of 3 Hz. Reliability at a finer level of intralobar localization was limited. Spike averaging significantly improved the reliability of source solutions and averaging 8 or more spikes reduced dependency on goodness-of-fit and data filtering. MSI performed on topographically identical individual spikes from an intracranially defined classical anterior temporal lobe spike focus was limited by low reliability (i.e., scattered source solutions) in terms of fine, sublobar localization within the ipsilateral temporal lobe. Spike averaging significantly improved reliability. MSI performed on individual anterior temporal spikes is limited by low reliability. Reduction of background noise through spike averaging significantly improves the reliability of MSI solutions. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

[Experience of Fusion image guided system in endonasal endoscopic surgery].

PubMed

Wen, Jingying; Zhen, Hongtao; Shi, Lili; Cao, Pingping; Cui, Yonghua

2015-08-01

To review endonasal endoscopic surgeries aided by Fusion image guided system, and to explore the application value of Fusion image guided system in endonasal endoscopic surgeries. Retrospective research. Sixty cases of endonasal endoscopic surgeries aided by Fusion image guided system were analysed including chronic rhinosinusitis with polyp (n = 10), fungus sinusitis (n = 5), endoscopic optic nerve decompression (n = 16), inverted papilloma of the paranasal sinus (n = 9), ossifying fibroma of sphenoid bone (n = 1), malignance of the paranasal sinus (n = 9), cerebrospinal fluid leak (n = 5), hemangioma of orbital apex (n = 2) and orbital reconstruction (n = 3). Sixty cases of endonasal endoscopic surgeries completed successfully without any complications. Fusion image guided system can help to identify the ostium of paranasal sinus, lamina papyracea and skull base. Fused CT-CTA images, or fused MR-MRA images can help to localize the optic nerve or internal carotid arteiy . Fused CT-MR images can help to detect the range of the tumor. It spent (7.13 ± 1.358) minutes for image guided system to do preoperative preparation and the surgical navigation accuracy reached less than 1mm after proficient. There was no device localization problem because of block or head set loosed. Fusion image guided system make endonasal endoscopic surgery to be a true microinvasive and exact surgery. It spends less preoperative preparation time, has high surgical navigation accuracy, improves the surgical safety and reduces the surgical complications.

Fundus image fusion in EYEPLAN software: An evaluation of a novel technique for ocular melanoma radiation treatment planning

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

Daftari, Inder K.; Mishra, Kavita K.; O'Brien, Joan M.

Purpose: The purpose of this study is to evaluate a novel approach for treatment planning using digital fundus image fusion in EYEPLAN for proton beam radiation therapy (PBRT) planning for ocular melanoma. The authors used a prototype version of EYEPLAN software, which allows for digital registration of high-resolution fundus photographs. The authors examined the improvement in tumor localization by replanning with the addition of fundus photo superimposition in patients with macular area tumors. Methods: The new version of EYEPLAN (v3.05) software allows for the registration of fundus photographs as a background image. This is then used in conjunction with clinicalmore » examination, tantalum marker clips, surgeon's mapping, and ultrasound to draw the tumor contour accurately. In order to determine if the fundus image superimposition helps in tumor delineation and treatment planning, the authors identified 79 patients with choroidal melanoma in the macular location that were treated with PBRT. All patients were treated to a dose of 56 GyE in four fractions. The authors reviewed and replanned all 79 macular melanoma cases with superimposition of pretreatment and post-treatment fundus imaging in the new EYEPLAN software. For patients with no local failure, the authors analyzed whether fundus photograph fusion accurately depicted and confirmed tumor volumes as outlined in the original treatment plan. For patients with local failure, the authors determined whether the addition of the fundus photograph might have benefited in terms of more accurate tumor volume delineation. Results: The mean follow-up of patients was 33.6{+-}23 months. Tumor growth was seen in six eyes of the 79 macular lesions. All six patients were marginal failures or tumor miss in the region of dose fall-off, including one patient with both in-field recurrence as well as marginal. Among the six recurrences, three were managed by enucleation and one underwent retreatment with proton therapy. Three patients developed distant metastasis and all three patients have since died. The replanning of six patients with their original fundus photograph superimposed showed that in four cases, the treatment field adequately covered the tumor volume. In the other two patients, the overlaid fundus photographs indicated the area of marginal miss. The replanning with the fundus photograph showed improved tumor coverage in these two macular lesions. For the remaining patients without local failure, replanning with fundus photograph superimposition confirmed the tumor volume as drawn in the original treatment plan. Conclusions: Local control was excellent in patients receiving 56 GyE of PBRT for uveal melanomas in the macular region, which traditionally can be more difficult to control. Posterior lesions are better defined with the additional use of fundus image since they can be difficult to mark surgically. In one-third of treatment failing patients, the superposition of the fundus photograph would have clearly allowed improved localization of tumor. The current practice standard is to use the superimposition of the fundus photograph in addition to the surgeon's clinical and clip mapping of the tumor and ultrasound measurement to draw the tumor volume.« less

Advanced biologically plausible algorithms for low-level image processing

NASA Astrophysics Data System (ADS)

Gusakova, Valentina I.; Podladchikova, Lubov N.; Shaposhnikov, Dmitry G.; Markin, Sergey N.; Golovan, Alexander V.; Lee, Seong-Whan

1999-08-01

At present, in computer vision, the approach based on modeling the biological vision mechanisms is extensively developed. However, up to now, real world image processing has no effective solution in frameworks of both biologically inspired and conventional approaches. Evidently, new algorithms and system architectures based on advanced biological motivation should be developed for solution of computational problems related to this visual task. Basic problems that should be solved for creation of effective artificial visual system to process real world imags are a search for new algorithms of low-level image processing that, in a great extent, determine system performance. In the present paper, the result of psychophysical experiments and several advanced biologically motivated algorithms for low-level processing are presented. These algorithms are based on local space-variant filter, context encoding visual information presented in the center of input window, and automatic detection of perceptually important image fragments. The core of latter algorithm are using local feature conjunctions such as noncolinear oriented segment and composite feature map formation. Developed algorithms were integrated into foveal active vision model, the MARR. It is supposed that proposed algorithms may significantly improve model performance while real world image processing during memorizing, search, and recognition.

Strength and coherence of binocular rivalry depends on shared stimulus complexity.

PubMed

Alais, David; Melcher, David

2007-01-01

Presenting incompatible images to the eyes results in alternations of conscious perception, a phenomenon known as binocular rivalry. We examined rivalry using either simple stimuli (oriented gratings) or coherent visual objects (faces, houses etc). Two rivalry characteristics were measured: Depth of rivalry suppression and coherence of alternations. Rivalry between coherent visual objects exhibits deep suppression and coherent rivalry, whereas rivalry between gratings exhibits shallow suppression and piecemeal rivalry. Interestingly, rivalry between a simple and a complex stimulus displays the same characteristics (shallow and piecemeal) as rivalry between two simple stimuli. Thus, complex stimuli fail to rival globally unless the fellow stimulus is also global. We also conducted a face adaptation experiment. Adaptation to rivaling faces improved subsequent face discrimination (as expected), but adaptation to a rivaling face/grating pair did not. To explain this, we suggest rivalry must be an early and local process (at least initially), instigated by the failure of binocular fusion, which can then become globally organized by feedback from higher-level areas when both rivalry stimuli are global, so that rivalry tends to oscillate coherently. These globally assembled images then flow through object processing areas, with the dominant image gaining in relative strength in a form of 'biased competition', therefore accounting for the deeper suppression of global images. In contrast, when only one eye receives a global image, local piecemeal suppression from the fellow eye overrides the organizing effects of global feedback to prevent coherent image formation. This indicates the primacy of local over global processes in rivalry.

Characterization of dynamic changes of current source localization based on spatiotemporal fMRI constrained EEG source imaging

NASA Astrophysics Data System (ADS)

Nguyen, Thinh; Potter, Thomas; Grossman, Robert; Zhang, Yingchun

2018-06-01

Objective. Neuroimaging has been employed as a promising approach to advance our understanding of brain networks in both basic and clinical neuroscience. Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) represent two neuroimaging modalities with complementary features; EEG has high temporal resolution and low spatial resolution while fMRI has high spatial resolution and low temporal resolution. Multimodal EEG inverse methods have attempted to capitalize on these properties but have been subjected to localization error. The dynamic brain transition network (DBTN) approach, a spatiotemporal fMRI constrained EEG source imaging method, has recently been developed to address these issues by solving the EEG inverse problem in a Bayesian framework, utilizing fMRI priors in a spatial and temporal variant manner. This paper presents a computer simulation study to provide a detailed characterization of the spatial and temporal accuracy of the DBTN method. Approach. Synthetic EEG data were generated in a series of computer simulations, designed to represent realistic and complex brain activity at superficial and deep sources with highly dynamical activity time-courses. The source reconstruction performance of the DBTN method was tested against the fMRI-constrained minimum norm estimates algorithm (fMRIMNE). The performances of the two inverse methods were evaluated both in terms of spatial and temporal accuracy. Main results. In comparison with the commonly used fMRIMNE method, results showed that the DBTN method produces results with increased spatial and temporal accuracy. The DBTN method also demonstrated the capability to reduce crosstalk in the reconstructed cortical time-course(s) induced by neighboring regions, mitigate depth bias and improve overall localization accuracy. Significance. The improved spatiotemporal accuracy of the reconstruction allows for an improved characterization of complex neural activity. This improvement can be extended to any subsequent brain connectivity analyses used to construct the associated dynamic brain networks.

Improved quantification for local regions of interest in preclinical PET imaging

NASA Astrophysics Data System (ADS)

Cal-González, J.; Moore, S. C.; Park, M.-A.; Herraiz, J. L.; Vaquero, J. J.; Desco, M.; Udias, J. M.

2015-09-01

In Positron Emission Tomography, there are several causes of quantitative inaccuracy, such as partial volume or spillover effects. The impact of these effects is greater when using radionuclides that have a large positron range, e.g. 68Ga and 124I, which have been increasingly used in the clinic. We have implemented and evaluated a local projection algorithm (LPA), originally evaluated for SPECT, to compensate for both partial-volume and spillover effects in PET. This method is based on the use of a high-resolution CT or MR image, co-registered with a PET image, which permits a high-resolution segmentation of a few tissues within a volume of interest (VOI) centered on a region within which tissue-activity values need to be estimated. The additional boundary information is used to obtain improved activity estimates for each tissue within the VOI, by solving a simple inversion problem. We implemented this algorithm for the preclinical Argus PET/CT scanner and assessed its performance using the radionuclides 18F, 68Ga and 124I. We also evaluated and compared the results obtained when it was applied during the iterative reconstruction, as well as after the reconstruction as a postprocessing procedure. In addition, we studied how LPA can help to reduce the ‘spillover contamination’, which causes inaccurate quantification of lesions in the immediate neighborhood of large, ‘hot’ sources. Quantification was significantly improved by using LPA, which provided more accurate ratios of lesion-to-background activity concentration for hot and cold regions. For 18F, the contrast was improved from 3.0 to 4.0 in hot lesions (when the true ratio was 4.0) and from 0.16 to 0.06 in cold lesions (true ratio  =  0.0), when using the LPA postprocessing. Furthermore, activity values estimated within the VOI using LPA during reconstruction were slightly more accurate than those obtained by post-processing, while also visually improving the image contrast and uniformity within the VOI.

Improved quantification for local regions of interest in preclinical PET imaging

PubMed Central

Cal-González, J.; Moore, S. C.; Park, M.-A.; Herraiz, J. L.; Vaquero, J. J.; Desco, M.; Udias, J. M.

2015-01-01

In Positron Emission Tomography, there are several causes of quantitative inaccuracy, such as partial volume or spillover effects. The impact of these effects is greater when using radionuclides that have a large positron range, e.g., 68Ga and 124I, which have been increasingly used in the clinic. We have implemented and evaluated a local projection algorithm (LPA), originally evaluated for SPECT, to compensate for both partial-volume and spillover effects in PET. This method is based on the use of a high-resolution CT or MR image, co-registered with a PET image, which permits a high-resolution segmentation of a few tissues within a volume of interest (VOI) centered on a region within which tissue-activity values need to be estimated. The additional boundary information is used to obtain improved activity estimates for each tissue within the VOI, by solving a simple inversion problem. We implemented this algorithm for the preclinical Argus PET/CT scanner and assessed its performance using the radionuclides 18F, 68Ga and 124I. We also evaluated and compared the results obtained when it was applied during the iterative reconstruction, as well as after the reconstruction as a postprocessing procedure. In addition, we studied how LPA can help to reduce the “spillover contamination”, which causes inaccurate quantification of lesions in the immediate neighborhood of large, “hot” sources. Quantification was significantly improved by using LPA, which provided more accurate ratios of lesion-to-background activity concentration for hot and cold regions. For 18F, the contrast was improved from 3.0 to 4.0 in hot lesions (when the true ratio was 4.0) and from 0.16 to 0.06 in cold lesions (true ratio = 0.0), when using the LPA postprocessing. Furthermore, activity values estimated within the VOI using LPA during reconstruction were slightly more accurate than those obtained by post-processing, while also visually improving the image contrast and uniformity within the VOI. PMID:26334312

Automated segmentation of 3D anatomical structures on CT images by using a deep convolutional network based on end-to-end learning approach

NASA Astrophysics Data System (ADS)

Zhou, Xiangrong; Takayama, Ryosuke; Wang, Song; Zhou, Xinxin; Hara, Takeshi; Fujita, Hiroshi

2017-02-01

We have proposed an end-to-end learning approach that trained a deep convolutional neural network (CNN) for automatic CT image segmentation, which accomplished a voxel-wised multiple classification to directly map each voxel on 3D CT images to an anatomical label automatically. The novelties of our proposed method were (1) transforming the anatomical structures segmentation on 3D CT images into a majority voting of the results of 2D semantic image segmentation on a number of 2D-slices from different image orientations, and (2) using "convolution" and "deconvolution" networks to achieve the conventional "coarse recognition" and "fine extraction" functions which were integrated into a compact all-in-one deep CNN for CT image segmentation. The advantage comparing to previous works was its capability to accomplish real-time image segmentations on 2D slices of arbitrary CT-scan-range (e.g. body, chest, abdomen) and produced correspondingly-sized output. In this paper, we propose an improvement of our proposed approach by adding an organ localization module to limit CT image range for training and testing deep CNNs. A database consisting of 240 3D CT scans and a human annotated ground truth was used for training (228 cases) and testing (the remaining 12 cases). We applied the improved method to segment pancreas and left kidney regions, respectively. The preliminary results showed that the accuracies of the segmentation results were improved significantly (pancreas was 34% and kidney was 8% increased in Jaccard index from our previous results). The effectiveness and usefulness of proposed improvement for CT image segmentations were confirmed.

Correlation coefficient based supervised locally linear embedding for pulmonary nodule recognition.

PubMed

Wu, Panpan; Xia, Kewen; Yu, Hengyong

2016-11-01

Dimensionality reduction techniques are developed to suppress the negative effects of high dimensional feature space of lung CT images on classification performance in computer aided detection (CAD) systems for pulmonary nodule detection. An improved supervised locally linear embedding (SLLE) algorithm is proposed based on the concept of correlation coefficient. The Spearman's rank correlation coefficient is introduced to adjust the distance metric in the SLLE algorithm to ensure that more suitable neighborhood points could be identified, and thus to enhance the discriminating power of embedded data. The proposed Spearman's rank correlation coefficient based SLLE (SC(2)SLLE) is implemented and validated in our pilot CAD system using a clinical dataset collected from the publicly available lung image database consortium and image database resource initiative (LICD-IDRI). Particularly, a representative CAD system for solitary pulmonary nodule detection is designed and implemented. After a sequential medical image processing steps, 64 nodules and 140 non-nodules are extracted, and 34 representative features are calculated. The SC(2)SLLE, as well as SLLE and LLE algorithm, are applied to reduce the dimensionality. Several quantitative measurements are also used to evaluate and compare the performances. Using a 5-fold cross-validation methodology, the proposed algorithm achieves 87.65% accuracy, 79.23% sensitivity, 91.43% specificity, and 8.57% false positive rate, on average. Experimental results indicate that the proposed algorithm outperforms the original locally linear embedding and SLLE coupled with the support vector machine (SVM) classifier. Based on the preliminary results from a limited number of nodules in our dataset, this study demonstrates the great potential to improve the performance of a CAD system for nodule detection using the proposed SC(2)SLLE. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Micro axial tomography: A miniaturized, versatile stage device to overcome resolution anisotropy in fluorescence light microscopy

NASA Astrophysics Data System (ADS)

Staier, Florian; Eipel, Heinz; Matula, Petr; Evsikov, Alexei V.; Kozubek, Michal; Cremer, Christoph; Hausmann, Michael

2011-09-01

With the development of novel fluorescence techniques, high resolution light microscopy has become a challenging technique for investigations of the three-dimensional (3D) micro-cosmos in cells and sub-cellular components. So far, all fluorescence microscopes applied for 3D imaging in biosciences show a spatially anisotropic point spread function resulting in an anisotropic optical resolution or point localization precision. To overcome this shortcoming, micro axial tomography was suggested which allows object tilting on the microscopic stage and leads to an improvement in localization precision and spatial resolution. Here, we present a miniaturized device which can be implemented in a motor driven microscope stage. The footprint of this device corresponds to a standard microscope slide. A special glass fiber can manually be adjusted in the object space of the microscope lens. A stepwise fiber rotation can be controlled by a miniaturized stepping motor incorporated into the device. By means of a special mounting device, test particles were fixed onto glass fibers, optically localized with high precision, and automatically rotated to obtain views from different perspective angles under which distances of corresponding pairs of objects were determined. From these angle dependent distance values, the real 3D distance was calculated with a precision in the ten nanometer range (corresponding here to an optical resolution of 10-30 nm) using standard microscopic equipment. As a proof of concept, the spindle apparatus of a mature mouse oocyte was imaged during metaphase II meiotic arrest under different perspectives. Only very few images registered under different rotation angles are sufficient for full 3D reconstruction. The results indicate the principal advantage of the micro axial tomography approach for many microscopic setups therein and also those of improved resolutions as obtained by high precision localization determination.

Diffusion tensor imaging for anatomical localization of cranial nerves and cranial nerve nuclei in pontine lesions: initial experiences with 3T-MRI.

PubMed

Ulrich, Nils H; Ahmadli, Uzeyir; Woernle, Christoph M; Alzarhani, Yahea A; Bertalanffy, Helmut; Kollias, Spyros S

2014-11-01

With continuous refinement of neurosurgical techniques and higher resolution in neuroimaging, the management of pontine lesions is constantly improving. Among pontine structures with vital functions that are at risk of being damaged by surgical manipulation, cranial nerves (CN) and cranial nerve nuclei (CNN) such as CN V, VI, and VII are critical. Pre-operative localization of the intrapontine course of CN and CNN should be beneficial for surgical outcomes. Our objective was to accurately localize CN and CNN in patients with intra-axial lesions in the pons using diffusion tensor imaging (DTI) and estimate its input in surgical planning for avoiding unintended loss of their function during surgery. DTI of the pons obtained pre-operatively on a 3Tesla MR scanner was analyzed prospectively for the accurate localization of CN and CNN V, VI and VII in seven patients with intra-axial lesions in the pons. Anatomical sections in the pons were used to estimate abnormalities on color-coded fractional anisotropy maps. Imaging abnormalities were correlated with CN symptoms before and after surgery. The course of CN and the area of CNN were identified using DTI pre- and post-operatively. Clinical associations between post-operative improvements and the corresponding CN area of the pons were demonstrated. Our results suggest that pre- and post-operative DTI allows identification of key anatomical structures in the pons and enables estimation of their involvement by pathology. It may predict clinical outcome and help us to better understand the involvement of the intrinsic anatomy by pathological processes. Copyright © 2014 Elsevier Ltd. All rights reserved.

Setting local rank constraints by orthogonal projections for image resolution analysis: application to the determination of a low dose pharmaceutical compound.

PubMed

Boiret, Mathieu; de Juan, Anna; Gorretta, Nathalie; Ginot, Yves-Michel; Roger, Jean-Michel

2015-09-10

Raman chemical imaging provides chemical and spatial information about pharmaceutical drug product. By using resolution methods on acquired spectra, the objective is to calculate pure spectra and distribution maps of image compounds. With multivariate curve resolution-alternating least squares, constraints are used to improve the performance of the resolution and to decrease the ambiguity linked to the final solution. Non negativity and spatial local rank constraints have been identified as the most powerful constraints to be used. In this work, an alternative method to set local rank constraints is proposed. The method is based on orthogonal projections pretreatment. For each drug product compound, raw Raman spectra are orthogonally projected to a basis including all the variability from the formulation compounds other than the product of interest. Presence or absence of the compound of interest is obtained by observing the correlations between the orthogonal projected spectra and a pure spectrum orthogonally projected to the same basis. By selecting an appropriate threshold, maps of presence/absence of compounds can be set up for all the product compounds. This method appears as a powerful approach to identify a low dose compound within a pharmaceutical drug product. The maps of presence/absence of compounds can be used as local rank constraints in resolution methods, such as multivariate curve resolution-alternating least squares process in order to improve the resolution of the system. The method proposed is particularly suited for pharmaceutical systems, where the identity of all compounds in the formulations is known and, therefore, the space of interferences can be well defined. Copyright © 2015 Elsevier B.V. All rights reserved.

Enhancement of morphological and vascular features in OCT images using a modified Bayesian residual transform

PubMed Central

Tan, Bingyao; Wong, Alexander; Bizheva, Kostadinka

2018-01-01

A novel image processing algorithm based on a modified Bayesian residual transform (MBRT) was developed for the enhancement of morphological and vascular features in optical coherence tomography (OCT) and OCT angiography (OCTA) images. The MBRT algorithm decomposes the original OCT image into multiple residual images, where each image presents information at a unique scale. Scale selective residual adaptation is used subsequently to enhance morphological features of interest, such as blood vessels and tissue layers, and to suppress irrelevant image features such as noise and motion artefacts. The performance of the proposed MBRT algorithm was tested on a series of cross-sectional and enface OCT and OCTA images of retina and brain tissue that were acquired in-vivo. Results show that the MBRT reduces speckle noise and motion-related imaging artefacts locally, thus improving significantly the contrast and visibility of morphological features in the OCT and OCTA images. PMID:29760996

A sparsity-based simplification method for segmentation of spectral-domain optical coherence tomography images

NASA Astrophysics Data System (ADS)

Meiniel, William; Gan, Yu; Olivo-Marin, Jean-Christophe; Angelini, Elsa

2017-08-01

Optical coherence tomography (OCT) has emerged as a promising image modality to characterize biological tissues. With axio-lateral resolutions at the micron-level, OCT images provide detailed morphological information and enable applications such as optical biopsy and virtual histology for clinical needs. Image enhancement is typically required for morphological segmentation, to improve boundary localization, rather than enrich detailed tissue information. We propose to formulate image enhancement as an image simplification task such that tissue layers are smoothed while contours are enhanced. For this purpose, we exploit a Total Variation sparsity-based image reconstruction, inspired by the Compressed Sensing (CS) theory, but specialized for images with structures arranged in layers. We demonstrate the potential of our approach on OCT human heart and retinal images for layers segmentation. We also compare our image enhancement capabilities to the state-of-the-art denoising techniques.

From heuristic optimization to dictionary learning: a review and comprehensive comparison of image denoising algorithms.

PubMed

Shao, Ling; Yan, Ruomei; Li, Xuelong; Liu, Yan

2014-07-01

Image denoising is a well explored topic in the field of image processing. In the past several decades, the progress made in image denoising has benefited from the improved modeling of natural images. In this paper, we introduce a new taxonomy based on image representations for a better understanding of state-of-the-art image denoising techniques. Within each category, several representative algorithms are selected for evaluation and comparison. The experimental results are discussed and analyzed to determine the overall advantages and disadvantages of each category. In general, the nonlocal methods within each category produce better denoising results than local ones. In addition, methods based on overcomplete representations using learned dictionaries perform better than others. The comprehensive study in this paper would serve as a good reference and stimulate new research ideas in image denoising.

The comparative effectiveness of conventional and digital image libraries.

PubMed

McColl, R I; Johnson, A

2001-03-01

Before introducing a hospital-wide image database to improve access, navigation and retrieval speed, a comparative study between a conventional slide library and a matching image database was undertaken to assess its relative benefits. Paired time trials and personal questionnaires revealed faster retrieval rates, higher image quality, and easier viewing for the pilot digital image database. Analysis of confidentiality, copyright and data protection exposed similar issues for both systems, thus concluding that the digital image database is a more effective library system. The authors suggest that in the future, medical images will be stored on large, professionally administered, centrally located file servers, allowing specialist image libraries to be tailored locally for individual users. The further integration of the database with web technology will enable cheap and efficient remote access for a wide range of users.

An improved finger-vein recognition algorithm based on template matching

NASA Astrophysics Data System (ADS)

Liu, Yueyue; Di, Si; Jin, Jian; Huang, Daoping

2016-10-01

Finger-vein recognition has became the most popular biometric identify methods. The investigation on the recognition algorithms always is the key point in this field. So far, there are many applicable algorithms have been developed. However, there are still some problems in practice, such as the variance of the finger position which may lead to the image distortion and shifting; during the identification process, some matching parameters determined according to experience may also reduce the adaptability of algorithm. Focus on above mentioned problems, this paper proposes an improved finger-vein recognition algorithm based on template matching. In order to enhance the robustness of the algorithm for the image distortion, the least squares error method is adopted to correct the oblique finger. During the feature extraction, local adaptive threshold method is adopted. As regard as the matching scores, we optimized the translation preferences as well as matching distance between the input images and register images on the basis of Naoto Miura algorithm. Experimental results indicate that the proposed method can improve the robustness effectively under the finger shifting and rotation conditions.

Locality-constrained anomaly detection for hyperspectral imagery

NASA Astrophysics Data System (ADS)

Liu, Jiabin; Li, Wei; Du, Qian; Liu, Kui

2015-12-01

Detecting a target with low-occurrence-probability from unknown background in a hyperspectral image, namely anomaly detection, is of practical significance. Reed-Xiaoli (RX) algorithm is considered as a classic anomaly detector, which calculates the Mahalanobis distance between local background and the pixel under test. Local RX, as an adaptive RX detector, employs a dual-window strategy to consider pixels within the frame between inner and outer windows as local background. However, the detector is sensitive if such a local region contains anomalous pixels (i.e., outliers). In this paper, a locality-constrained anomaly detector is proposed to remove outliers in the local background region before employing the RX algorithm. Specifically, a local linear representation is designed to exploit the internal relationship between linearly correlated pixels in the local background region and the pixel under test and its neighbors. Experimental results demonstrate that the proposed detector improves the original local RX algorithm.

Localized high-resolution DTI of the human midbrain using single-shot EPI, parallel imaging, and outer-volume suppression at 7 T

PubMed Central

Wargo, Christopher J.; Gore, John C.

2013-01-01

Localized high-resolution diffusion tensor images (DTI) from the midbrain were obtained using reduced field-of-view (rFOV) methods combined with SENSE parallel imaging and single-shot echo planar (EPI) acquisitions at 7 T. This combination aimed to diminish sensitivities of DTI to motion, susceptibility variations, and EPI artifacts at ultra-high field. Outer-volume suppression (OVS) was applied in DTI acquisitions at 2- and 1-mm2 resolutions, b=1000 s/mm2, and six diffusion directions, resulting in scans of 7- and 14-min durations. Mean apparent diffusion coefficient (ADC) and fractional anisotropy (FA) values were measured in various fiber tract locations at the two resolutions and compared. Geometric distortion and signal-to-noise ratio (SNR) were additionally measured and compared for reduced-FOV and full-FOV DTI scans. Up to an eight-fold data reduction was achieved using DTI-OVS with SENSE at 1 mm2, and geometric distortion was halved. The localization of fiber tracts was improved, enabling targeted FA and ADC measurements. Significant differences in diffusion properties were observed between resolutions for a number of regions suggesting that FA values are impacted by partial volume effects even at a 2-mm2 resolution. The combined SENSE DTI-OVS approach allows large reductions in DTI data acquisition and provides improved quality for high-resolution diffusion studies of the human brain. PMID:23541390

Directional sinogram interpolation for motion weighted 4D cone-beam CT reconstruction

NASA Astrophysics Data System (ADS)

Zhang, Hua; Kruis, Matthijs; Sonke, Jan-Jakob

2017-03-01

The image quality of respiratory sorted four-dimensional (4D) cone-beam (CB) computed tomography (CT) is often limited by streak artifacts due to insufficient projections. A motion weighted reconstruction (MWR) method is proposed to decrease streak artifacts and improve image quality. Firstly, respiratory correlated CBCT projections were interpolated by directional sinogram interpolation (DSI) to generate additional CB projections for each phase and subsequently reconstructed. Secondly, local motion was estimated by deformable image registration of the interpolated 4D CBCT. Thirdly, a regular 3D FDK CBCT was reconstructed from the non-interpolated projections. Finally, weights were assigned to each voxel, based on the local motion, and then were used to combine the 3D FDK CBCT and interpolated 4D CBCT to generate the final 4D image. MWR method was compared with regular 4D CBCT scans as well as McKinnon and Bates (MKB) based reconstructions. Comparisons were made in terms of (1) comparing the steepness of an extracted profile from the boundary of the region-of-interest (ROI), (2) contrast-to-noise ratio (CNR) inside certain ROIs, and (3) the root-mean-square-error (RMSE) between the planning CT and CBCT inside a homogeneous moving region. Comparisons were made for both a phantom and four patient scans. In a 4D phantom, RMSE were reduced by 24.7% and 38.7% for MKB and MWR respectively, compared to conventional 4D CBCT. Meanwhile, interpolation induced blur was minimal in static regions for MWR based reconstructions. In regions with considerable respiratory motion, image blur using MWR is less than the MKB and 3D Feldkamp (FDK) methods. In the lung cancer patients, average CNRs of MKB, DSI and MWR improved by a factor 1.7, 2.8 and 3.5 respectively relative to 4D FDK. MWR effectively reduces RMSE in 4D cone-beam CT and improves the image quality in both the static and respiratory moving regions compared to 4D FDK and MKB methods.

Directional sinogram interpolation for motion weighted 4D cone-beam CT reconstruction.

PubMed

Zhang, Hua; Kruis, Matthijs; Sonke, Jan-Jakob

2017-03-21

The image quality of respiratory sorted four-dimensional (4D) cone-beam (CB) computed tomography (CT) is often limited by streak artifacts due to insufficient projections. A motion weighted reconstruction (MWR) method is proposed to decrease streak artifacts and improve image quality. Firstly, respiratory correlated CBCT projections were interpolated by directional sinogram interpolation (DSI) to generate additional CB projections for each phase and subsequently reconstructed. Secondly, local motion was estimated by deformable image registration of the interpolated 4D CBCT. Thirdly, a regular 3D FDK CBCT was reconstructed from the non-interpolated projections. Finally, weights were assigned to each voxel, based on the local motion, and then were used to combine the 3D FDK CBCT and interpolated 4D CBCT to generate the final 4D image. MWR method was compared with regular 4D CBCT scans as well as McKinnon and Bates (MKB) based reconstructions. Comparisons were made in terms of (1) comparing the steepness of an extracted profile from the boundary of the region-of-interest (ROI), (2) contrast-to-noise ratio (CNR) inside certain ROIs, and (3) the root-mean-square-error (RMSE) between the planning CT and CBCT inside a homogeneous moving region. Comparisons were made for both a phantom and four patient scans. In a 4D phantom, RMSE were reduced by 24.7% and 38.7% for MKB and MWR respectively, compared to conventional 4D CBCT. Meanwhile, interpolation induced blur was minimal in static regions for MWR based reconstructions. In regions with considerable respiratory motion, image blur using MWR is less than the MKB and 3D Feldkamp (FDK) methods. In the lung cancer patients, average CNRs of MKB, DSI and MWR improved by a factor 1.7, 2.8 and 3.5 respectively relative to 4D FDK. MWR effectively reduces RMSE in 4D cone-beam CT and improves the image quality in both the static and respiratory moving regions compared to 4D FDK and MKB methods.

Intensity correction for multichannel hyperpolarized 13C imaging of the heart.

PubMed

Dominguez-Viqueira, William; Geraghty, Benjamin J; Lau, Justin Y C; Robb, Fraser J; Chen, Albert P; Cunningham, Charles H

2016-02-01

Develop and test an analytic correction method to correct the signal intensity variation caused by the inhomogeneous reception profile of an eight-channel phased array for hyperpolarized (13) C imaging. Fiducial markers visible in anatomical images were attached to the individual coils to provide three dimensional localization of the receive hardware with respect to the image frame of reference. The coil locations and dimensions were used to numerically model the reception profile using the Biot-Savart Law. The accuracy of the coil sensitivity estimation was validated with images derived from a homogenous (13) C phantom. Numerical coil sensitivity estimates were used to perform intensity correction of in vivo hyperpolarized (13) C cardiac images in pigs. In comparison to the conventional sum-of-squares reconstruction, improved signal uniformity was observed in the corrected images. The analytical intensity correction scheme was shown to improve the uniformity of multichannel image reconstruction in hyperpolarized [1-(13) C]pyruvate and (13) C-bicarbonate cardiac MRI. The method is independent of the pulse sequence used for (13) C data acquisition, simple to implement and does not require additional scan time, making it an attractive technique for multichannel hyperpolarized (13) C MRI. © 2015 Wiley Periodicals, Inc.

Modulation of Memory T Cells to Control Acquired Bone Marrow Failure

DTIC Science & Technology

2016-01-01

Representative images show the tissues from one of 6 recipients in each group at day 7 after transplantation. Images were obtained with an OlympusBX41...alloreactive effector T cells capable of mediating host tissue injury and could be beneficial targets for improving the efficacy of allogeneic HSCT...leukemia (GVL) effect, but showed impaired expansion in local tissues .69-72 This nTEM pool might have less diverse T cell receptor (TCR) repertoire

F18 EF5 PET/CT Imaging in Patients with Brain Metastases from Breast Cancer

DTIC Science & Technology

2012-07-01

been demonstrated to improve local control and survival in select patients after WBRT . At present we do not have any method of determining a priori...relapse after WBRT would represent a significant step forward in the management of patients with brain metastases from breast cancer. We propose to...use a noninvasive imaging method to detect residual tumor hypoxia in patients receiving WBRT . Body: Task 1. To estimate the degree of hypoxia

Optical eye tracking system for real-time noninvasive tumor localization in external beam radiotherapy

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

Via, Riccardo, E-mail: riccardo.via@polimi.it; Fassi, Aurora; Fattori, Giovanni

Purpose: External beam radiotherapy currently represents an important therapeutic strategy for the treatment of intraocular tumors. Accurate target localization and efficient compensation of involuntary eye movements are crucial to avoid deviations in dose distribution with respect to the treatment plan. This paper describes an eye tracking system (ETS) based on noninvasive infrared video imaging. The system was designed for capturing the tridimensional (3D) ocular motion and provides an on-line estimation of intraocular lesions position based on a priori knowledge coming from volumetric imaging. Methods: Eye tracking is performed by localizing cornea and pupil centers on stereo images captured by twomore » calibrated video cameras, exploiting eye reflections produced by infrared illumination. Additionally, torsional eye movements are detected by template matching in the iris region of eye images. This information allows estimating the 3D position and orientation of the eye by means of an eye local reference system. By combining ETS measurements with volumetric imaging for treatment planning [computed tomography (CT) and magnetic resonance (MR)], one is able to map the position of the lesion to be treated in local eye coordinates, thus enabling real-time tumor referencing during treatment setup and irradiation. Experimental tests on an eye phantom and seven healthy subjects were performed to assess ETS tracking accuracy. Results: Measurements on phantom showed an overall median accuracy within 0.16 mm and 0.40° for translations and rotations, respectively. Torsional movements were affected by 0.28° median uncertainty. On healthy subjects, the gaze direction error ranged between 0.19° and 0.82° at a median working distance of 29 cm. The median processing time of the eye tracking algorithm was 18.60 ms, thus allowing eye monitoring up to 50 Hz. Conclusions: A noninvasive ETS prototype was designed to perform real-time target localization and eye movement monitoring during ocular radiotherapy treatments. The device aims at improving state-of-the-art invasive procedures based on surgical implantation of radiopaque clips and repeated acquisition of X-ray images, with expected positive effects on treatment quality and patient outcome.« less

Optical eye tracking system for real-time noninvasive tumor localization in external beam radiotherapy.

PubMed

Via, Riccardo; Fassi, Aurora; Fattori, Giovanni; Fontana, Giulia; Pella, Andrea; Tagaste, Barbara; Riboldi, Marco; Ciocca, Mario; Orecchia, Roberto; Baroni, Guido

2015-05-01

External beam radiotherapy currently represents an important therapeutic strategy for the treatment of intraocular tumors. Accurate target localization and efficient compensation of involuntary eye movements are crucial to avoid deviations in dose distribution with respect to the treatment plan. This paper describes an eye tracking system (ETS) based on noninvasive infrared video imaging. The system was designed for capturing the tridimensional (3D) ocular motion and provides an on-line estimation of intraocular lesions position based on a priori knowledge coming from volumetric imaging. Eye tracking is performed by localizing cornea and pupil centers on stereo images captured by two calibrated video cameras, exploiting eye reflections produced by infrared illumination. Additionally, torsional eye movements are detected by template matching in the iris region of eye images. This information allows estimating the 3D position and orientation of the eye by means of an eye local reference system. By combining ETS measurements with volumetric imaging for treatment planning [computed tomography (CT) and magnetic resonance (MR)], one is able to map the position of the lesion to be treated in local eye coordinates, thus enabling real-time tumor referencing during treatment setup and irradiation. Experimental tests on an eye phantom and seven healthy subjects were performed to assess ETS tracking accuracy. Measurements on phantom showed an overall median accuracy within 0.16 mm and 0.40° for translations and rotations, respectively. Torsional movements were affected by 0.28° median uncertainty. On healthy subjects, the gaze direction error ranged between 0.19° and 0.82° at a median working distance of 29 cm. The median processing time of the eye tracking algorithm was 18.60 ms, thus allowing eye monitoring up to 50 Hz. A noninvasive ETS prototype was designed to perform real-time target localization and eye movement monitoring during ocular radiotherapy treatments. The device aims at improving state-of-the-art invasive procedures based on surgical implantation of radiopaque clips and repeated acquisition of X-ray images, with expected positive effects on treatment quality and patient outcome.

Image simulation for electron energy loss spectroscopy

DOE PAGES

Oxley, Mark P.; Pennycook, Stephen J.

2007-10-22

In this paper, aberration correction of the probe forming optics of the scanning transmission electron microscope has allowed the probe-forming aperture to be increased in size, resulting in probes of the order of 1 Å in diameter. The next generation of correctors promise even smaller probes. Improved spectrometer optics also offers the possibility of larger electron energy loss spectrometry detectors. The localization of images based on core-loss electron energy loss spectroscopy is examined as function of both probe-forming aperture and detector size. The effective ionization is nonlocal in nature, and two common local approximations are compared to full nonlocal calculations.more » Finally, the affect of the channelling of the electron probe within the sample is also discussed.« less

About Non-Line-Of-Sight Satellite Detection and Exclusion in a 3D Map-Aided Localization Algorithm

PubMed Central

Peyraud, Sébastien; Bétaille, David; Renault, Stéphane; Ortiz, Miguel; Mougel, Florian; Meizel, Dominique; Peyret, François

2013-01-01

Reliable GPS positioning in city environment is a key issue actually, signals are prone to multipath, with poor satellite geometry in many streets. Using a 3D urban model to forecast satellite visibility in urban contexts in order to improve GPS localization is the main topic of the present article. A virtual image processing that detects and eliminates possible faulty measurements is the core of this method. This image is generated using the position estimated a priori by the navigation process itself, under road constraints. This position is then updated by measurements to line-of-sight satellites only. This closed-loop real-time processing has shown very first promising full-scale test results. PMID:23344379

A phantom study of the immobilization and the indications for using virtual isocenter in stereoscopic X‐ray image guidance system referring to position localizer in frameless radiosurgery

PubMed Central

Chang, Hsiao‐Han; Lee, Hsiao‐Fei; Sung, Chien‐Cheng; Liao, Tsung‐I

2013-01-01

A frameless radiosurgery system is using a set of thermoplastic mask for fixation and stereoscopic X‐ray imaging for alignment. The accuracy depends on mask fixation and imaging. Under certain circumstances, the guidance images may contain insufficient bony structures, resulting in lesser accuracy. A virtual isocenter function is designed for such scenarios. In this study, we investigated the immobilization and the indications for using virtual isocenter. Twenty‐four arbitrary imaginary treatment targets (ITTs) in phantom were evaluated. The external Localizer with positioner films was used as reference. The alignments by using actual and virtual isocenter in image guidance were compared. The deviation of the alignment after mask removing and then resetting was also checked. The results illustrated that the mean deviation between the alignment by image guidance using actual isocenter (Isoimg) and the localizer(Isoloc) was 2.26mm±1.16mm (standard deviation, SD), 1.66mm±0.83mm for using virtual isocenter. The deviation of the alignment by the image guidance using actual isocenter to the localizer before and after mask resetting was 7.02mm±5.8mm. The deviations before and after mask resetting were insignificant for the target center from skull edge larger than 80 mm on craniocaudal direction. The deviations between the alignment using actual and virtual isocenter in image guidance were not significant if the minimum distance from target center to skull edge was larger or equal to 30 mm. Due to an unacceptable deviation after mask resetting, the image guidance is necessary to improve the accuracy of frameless immobilization. A treatment isocenter less than 30 mm from the skull bone should be an indication for using virtual isocenter to align in image guidance. The virtual isocenter should be set as caudally as possible, and the sella of skull should be the ideal point. PACS numbers: 87.55.kh, 87.55.ne, 87.55.tm PMID:23835379

People detection in crowded scenes using active contour models

NASA Astrophysics Data System (ADS)

Sidla, Oliver

2009-01-01

The detection of pedestrians in real-world scenes is a daunting task, especially in crowded situations. Our experience over the last years has shown that active shape models (ASM) can contribute significantly to a robust pedestrian detection system. The paper starts with an overview of shape model approaches, it then explains our approach which builds on top of Eigenshape models which have been trained using real-world data. These models are placed over candidate regions and matched to image gradients using a scoring function which integrates i) point distribution, ii) local gradient orientations iii) local image gradient strengths. A matching and shape model update process is iteratively applied in order to fit the flexible models to the local image content. The weights of the scoring function have a significant impact on the ASM performance. We analyze different settings of scoring weights for gradient magnitude, relative orientation differences, distance between model and gradient in an experiment which uses real-world data. Although for only one pedestrian model in an image computation time is low, the number of necessary processing cycles which is needed to track many people in crowded scenes can become the bottleneck in a real-time application. We describe the measures which have been taken in order to improve the speed of the ASM implementation and make it real-time capable.

Estimation of Local Orientations in Fibrous Structures With Applications to the Purkinje System

PubMed Central

Plank, Gernot; Trayanova, Natalia A.; Vidal, René

2011-01-01

The extraction of the cardiac Purkinje system (PS) from intensity images is a critical step toward the development of realistic structural models of the heart. Such models are important for uncovering the mechanisms of cardiac disease and improving its treatment and prevention. Unfortunately, the manual extraction of the PS is a challenging and error-prone task due to the presence of image noise and numerous fiber junctions. To deal with these challenges, we propose a framework that estimates local fiber orientations with high accuracy and reconstructs the fibers via tracking. Our key contribution is the development of a descriptor for estimating the orientation distribution function (ODF), a spherical function encoding the local geometry of the fibers at a point of interest. The fiber/branch orientations are identified as the modes of the ODFs via spherical clustering and guide the extraction of the fiber centerlines. Experiments on synthetic data evaluate the sensitivity of our approach to image noise, width of the fiber, and choice of the mode detection strategy, and show its superior performance compared to those of the existing descriptors. Experiments on the free-running PS in an MR image also demonstrate the accuracy of our method in reconstructing such sparse fibrous structures. PMID:21335301

Using Digital Image Correlation to Characterize Local Strains on Vascular Tissue Specimens.

PubMed

Zhou, Boran; Ravindran, Suraj; Ferdous, Jahid; Kidane, Addis; Sutton, Michael A; Shazly, Tarek

2016-01-24

Characterization of the mechanical behavior of biological and engineered soft tissues is a central component of fundamental biomedical research and product development. Stress-strain relationships are typically obtained from mechanical testing data to enable comparative assessment among samples and in some cases identification of constitutive mechanical properties. However, errors may be introduced through the use of average strain measures, as significant heterogeneity in the strain field may result from geometrical non-uniformity of the sample and stress concentrations induced by mounting/gripping of soft tissues within the test system. When strain field heterogeneity is significant, accurate assessment of the sample mechanical response requires measurement of local strains. This study demonstrates a novel biomechanical testing protocol for calculating local surface strains using a mechanical testing device coupled with a high resolution camera and a digital image correlation technique. A series of sample surface images are acquired and then analyzed to quantify the local surface strain of a vascular tissue specimen subjected to ramped uniaxial loading. This approach can improve accuracy in experimental vascular biomechanics and has potential for broader use among other native soft tissues, engineered soft tissues, and soft hydrogel/polymeric materials. In the video, we demonstrate how to set up the system components and perform a complete experiment on native vascular tissue.

Crowded field photometry with deconvolved images.

NASA Astrophysics Data System (ADS)

Linde, P.; Spännare, S.

A local implementation of the Lucy-Richardson algorithm has been used to deconvolve a set of crowded stellar field images. The effects of deconvolution on detection limits as well as on photometric and astrometric properties have been investigated as a function of the number of deconvolution iterations. Results show that deconvolution improves detection of faint stars, although artifacts are also found. Deconvolution provides more stars measurable without significant degradation of positional accuracy. The photometric precision is affected by deconvolution in several ways. Errors due to unresolved images are notably reduced, while flux redistribution between stars and background increases the errors.

Malignant pleural disease: diagnosis by using diffusion-weighted and dynamic contrast-enhanced MR imaging--initial experience.

PubMed

Coolen, Johan; De Keyzer, Frederik; Nafteux, Philippe; De Wever, Walter; Dooms, Christophe; Vansteenkiste, Johan; Roebben, Ilse; Verbeken, Eric; De Leyn, Paul; Van Raemdonck, Dirk; Nackaerts, Kristiaan; Dymarkowski, Steven; Verschakelen, Johny

2012-06-01

To investigate the use of diffusion-weighted (DW) imaging for differentiating benign lesions from malignant pleural disease (MPD) and to retrospectively assess dynamic contrast material-enhanced (DCE) magnetic resonance (MR) imaging acquisitions to find out whether combining these measurements with DW imaging could improve the diagnostic value of DW imaging. This study was approved by the local ethics committee, and all patients provided written informed consent. Thirty-one consecutive patients with pleural abnormalities suspicious for MPD underwent whole-body positron emission tomography (PET)/computed tomography (CT) and thorax MR examinations. Diagnostic thoracoscopy with histopathologic analysis of pleural biopsies served as the reference standard. First-line evaluation of each suspicious lesion was performed by using the apparent diffusion coefficient (ADC) calculated from the DW image, and the optimal cutoff value was found by using receiver operating characteristic curve analysis. Afterward, DCE MR imaging data were used to improve the diagnosis in the range of ADCs where DW imaging results were equivocal. Sensitivity, specificity, and accuracy of PET/CT for diagnosis of MPD were 100%, 35.3%, and 64.5%. The optimal ADC threshold to differentiate benign lesions from MPD with DW MR imaging was 1.52 × 10(-3) mm(2)/sec, with sensitivity, specificity, and accuracy of 71.4%, 100%, and 87.1%, respectively. This result could be improved to 92.8%, 94.1%, and 93.5%, respectively, when DCE MR imaging data were included in those cases where ADC was between 1.52 and 2.00 × 10(-3) mm(2)/sec. A total of 20 patients had disease diagnosed correctly, nine had disease diagnosed incorrectly, and two cases were undetermined with PET/CT. DW imaging helped stage disease correctly in 27 patients and incorrectly in four. The undetermined cases at PET/CT were correctly diagnosed at MR imaging. DW imaging is a promising tool for differentiating MPD from benign lesions, with high accuracy, and supplementation with DCE MR imaging seems to further improve sensitivity.

Directional bilateral filters for smoothing fluorescence microscopy images

NASA Astrophysics Data System (ADS)

Venkatesh, Manasij; Mohan, Kavya; Seelamantula, Chandra Sekhar

2015-08-01

Images obtained through fluorescence microscopy at low numerical aperture (NA) are noisy and have poor resolution. Images of specimens such as F-actin filaments obtained using confocal or widefield fluorescence microscopes contain directional information and it is important that an image smoothing or filtering technique preserve the directionality. F-actin filaments are widely studied in pathology because the abnormalities in actin dynamics play a key role in diagnosis of cancer, cardiac diseases, vascular diseases, myofibrillar myopathies, neurological disorders, etc. We develop the directional bilateral filter as a means of filtering out the noise in the image without significantly altering the directionality of the F-actin filaments. The bilateral filter is anisotropic to start with, but we add an additional degree of anisotropy by employing an oriented domain kernel for smoothing. The orientation is locally adapted using a structure tensor and the parameters of the bilateral filter are optimized for within the framework of statistical risk minimization. We show that the directional bilateral filter has better denoising performance than the traditional Gaussian bilateral filter and other denoising techniques such as SURE-LET, non-local means, and guided image filtering at various noise levels in terms of peak signal-to-noise ratio (PSNR). We also show quantitative improvements in low NA images of F-actin filaments.

Accurate permittivity measurements for microwave imaging via ultra-wideband removal of spurious reflectors.

PubMed

Pelletier, Mathew G; Viera, Joseph A; Wanjura, John; Holt, Greg

2010-01-01

The use of microwave imaging is becoming more prevalent for detection of interior hidden defects in manufactured and packaged materials. In applications for detection of hidden moisture, microwave tomography can be used to image the material and then perform an inverse calculation to derive an estimate of the variability of the hidden material, such internal moisture, thereby alerting personnel to damaging levels of the hidden moisture before material degradation occurs. One impediment to this type of imaging occurs with nearby objects create strong reflections that create destructive and constructive interference, at the receiver, as the material is conveyed past the imaging antenna array. In an effort to remove the influence of the reflectors, such as metal bale ties, research was conducted to develop an algorithm for removal of the influence of the local proximity reflectors from the microwave images. This research effort produced a technique, based upon the use of ultra-wideband signals, for the removal of spurious reflections created by local proximity reflectors. This improvement enables accurate microwave measurements of moisture in such products as cotton bales, as well as other physical properties such as density or material composition. The proposed algorithm was shown to reduce errors by a 4:1 ratio and is an enabling technology for imaging applications in the presence of metal bale ties.

Fast X-ray imaging of cavitating flows

DOE PAGES

Khlifa, Ilyass; Vabre, Alexandre; Hočevar, Marko; ...

2017-10-20

A new method based on ultra-fast X-ray imaging was developed in this work for simultaneous investigations of the dynamics and the structures of complex two-phase flows. Here in this paper, cavitation was created inside a millimetric 2D Venturi-type test section, while seeding particles were injected into the flow. Thanks to the phase-contrast enhancement technique provided by the APS (Advanced Photon Source) synchrotron beam, high definition X-ray images of the complex cavitating flows were obtained. These images contain valuable information about both the liquid and the gaseous phases. By means of image processing, the two phases were separated, and velocity fieldsmore » of each phase were therefore calculated using image cross-correlations. The local vapour volume fractions were also obtained thanks to the local intensity levels within the recorded images. These simultaneous measurements, provided by this new technique, afford more insight into the structure and the dynamic of two-phase flows as well as the interactions between then, and hence enable to improve our understanding of their behavior. In the case of cavitating flows inside a Venturi-type test section, the X-ray measurements demonstrates, for the first time, the presence of significant slip velocities between the phases within sheet cavities for both steady and unsteady flow configurations.« less

Fast X-ray imaging of cavitating flows

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

Khlifa, Ilyass; Vabre, Alexandre; Hočevar, Marko

A new method based on ultra-fast X-ray imaging was developed in this work for simultaneous investigations of the dynamics and the structures of complex two-phase flows. Here in this paper, cavitation was created inside a millimetric 2D Venturi-type test section, while seeding particles were injected into the flow. Thanks to the phase-contrast enhancement technique provided by the APS (Advanced Photon Source) synchrotron beam, high definition X-ray images of the complex cavitating flows were obtained. These images contain valuable information about both the liquid and the gaseous phases. By means of image processing, the two phases were separated, and velocity fieldsmore » of each phase were therefore calculated using image cross-correlations. The local vapour volume fractions were also obtained thanks to the local intensity levels within the recorded images. These simultaneous measurements, provided by this new technique, afford more insight into the structure and the dynamic of two-phase flows as well as the interactions between then, and hence enable to improve our understanding of their behavior. In the case of cavitating flows inside a Venturi-type test section, the X-ray measurements demonstrates, for the first time, the presence of significant slip velocities between the phases within sheet cavities for both steady and unsteady flow configurations.« less

Exploring combined dark and bright field illumination to improve the detection of defects on specular surfaces

NASA Astrophysics Data System (ADS)

Forte, Paulo M. F.; Felgueiras, P. E. R.; Ferreira, Flávio P.; Sousa, M. A.; Nunes-Pereira, Eduardo J.; Bret, Boris P. J.; Belsley, Michael S.

2017-01-01

An automatic optical inspection system for detecting local defects on specular surfaces is presented. The system uses an image display to produce a sequence of structured diffuse illumination patterns and a digital camera to acquire the corresponding sequence of images. An image enhancement algorithm, which measures the local intensity variations between bright- and dark-field illumination conditions, yields a final image in which the defects are revealed with a high contrast. Subsequently, an image segmentation algorithm, which compares statistically the enhanced image of the inspected surface with the corresponding image for a defect-free template, allows separating defects from non-defects with an adjusting decision threshold. The method can be applied to shiny surfaces of any material including metal, plastic and glass. The described method was tested on the plastic surface of a car dashboard system. We were able to detect not only scratches but also dust and fingerprints. In our experiment we observed a detection contrast increase from about 40%, when using an extended light source, to more than 90% when using a structured light source. The presented method is simple, robust and can be carried out with short cycle times, making it appropriate for applications in industrial environments.

Bag-of-features based medical image retrieval via multiple assignment and visual words weighting.

PubMed

Wang, Jingyan; Li, Yongping; Zhang, Ying; Wang, Chao; Xie, Honglan; Chen, Guoling; Gao, Xin

2011-11-01

Bag-of-features based approaches have become prominent for image retrieval and image classification tasks in the past decade. Such methods represent an image as a collection of local features, such as image patches and key points with scale invariant feature transform (SIFT) descriptors. To improve the bag-of-features methods, we first model the assignments of local descriptors as contribution functions, and then propose a novel multiple assignment strategy. Assuming the local features can be reconstructed by their neighboring visual words in a vocabulary, reconstruction weights can be solved by quadratic programming. The weights are then used to build contribution functions, resulting in a novel assignment method, called quadratic programming (QP) assignment. We further propose a novel visual word weighting method. The discriminative power of each visual word is analyzed by the sub-similarity function in the bin that corresponds to the visual word. Each sub-similarity function is then treated as a weak classifier. A strong classifier is learned by boosting methods that combine those weak classifiers. The weighting factors of the visual words are learned accordingly. We evaluate the proposed methods on medical image retrieval tasks. The methods are tested on three well-known data sets, i.e., the ImageCLEFmed data set, the 304 CT Set, and the basal-cell carcinoma image set. Experimental results demonstrate that the proposed QP assignment outperforms the traditional nearest neighbor assignment, the multiple assignment, and the soft assignment, whereas the proposed boosting based weighting strategy outperforms the state-of-the-art weighting methods, such as the term frequency weights and the term frequency-inverse document frequency weights.

Fusion of multichannel local and global structural cues for photo aesthetics evaluation.

PubMed

Luming Zhang; Yue Gao; Zimmermann, Roger; Qi Tian; Xuelong Li

2014-03-01

Photo aesthetic quality evaluation is a fundamental yet under addressed task in computer vision and image processing fields. Conventional approaches are frustrated by the following two drawbacks. First, both the local and global spatial arrangements of image regions play an important role in photo aesthetics. However, existing rules, e.g., visual balance, heuristically define which spatial distribution among the salient regions of a photo is aesthetically pleasing. Second, it is difficult to adjust visual cues from multiple channels automatically in photo aesthetics assessment. To solve these problems, we propose a new photo aesthetics evaluation framework, focusing on learning the image descriptors that characterize local and global structural aesthetics from multiple visual channels. In particular, to describe the spatial structure of the image local regions, we construct graphlets small-sized connected graphs by connecting spatially adjacent atomic regions. Since spatially adjacent graphlets distribute closely in their feature space, we project them onto a manifold and subsequently propose an embedding algorithm. The embedding algorithm encodes the photo global spatial layout into graphlets. Simultaneously, the importance of graphlets from multiple visual channels are dynamically adjusted. Finally, these post-embedding graphlets are integrated for photo aesthetics evaluation using a probabilistic model. Experimental results show that: 1) the visualized graphlets explicitly capture the aesthetically arranged atomic regions; 2) the proposed approach generalizes and improves four prominent aesthetic rules; and 3) our approach significantly outperforms state-of-the-art algorithms in photo aesthetics prediction.

Aberration correction in wide-field fluorescence microscopy by segmented-pupil image interferometry.

PubMed

Scrimgeour, Jan; Curtis, Jennifer E

2012-06-18

We present a new technique for the correction of optical aberrations in wide-field fluorescence microscopy. Segmented-Pupil Image Interferometry (SPII) uses a liquid crystal spatial light modulator placed in the microscope's pupil plane to split the wavefront originating from a fluorescent object into an array of individual beams. Distortion of the wavefront arising from either system or sample aberrations results in displacement of the images formed from the individual pupil segments. Analysis of image registration allows for the local tilt in the wavefront at each segment to be corrected with respect to a central reference. A second correction step optimizes the image intensity by adjusting the relative phase of each pupil segment through image interferometry. This ensures that constructive interference between all segments is achieved at the image plane. Improvements in image quality are observed when Segmented-Pupil Image Interferometry is applied to correct aberrations arising from the microscope's optical path.

A Fusion Algorithm for GFP Image and Phase Contrast Image of Arabidopsis Cell Based on SFL-Contourlet Transform

PubMed Central

Feng, Peng; Wang, Jing; Wei, Biao; Mi, Deling

2013-01-01

A hybrid multiscale and multilevel image fusion algorithm for green fluorescent protein (GFP) image and phase contrast image of Arabidopsis cell is proposed in this paper. Combining intensity-hue-saturation (IHS) transform and sharp frequency localization Contourlet transform (SFL-CT), this algorithm uses different fusion strategies for different detailed subbands, which include neighborhood consistency measurement (NCM) that can adaptively find balance between color background and gray structure. Also two kinds of neighborhood classes based on empirical model are taken into consideration. Visual information fidelity (VIF) as an objective criterion is introduced to evaluate the fusion image. The experimental results of 117 groups of Arabidopsis cell image from John Innes Center show that the new algorithm cannot only make the details of original images well preserved but also improve the visibility of the fusion image, which shows the superiority of the novel method to traditional ones. PMID:23476716

Comparison of k-means related clustering methods for nuclear medicine images segmentation

NASA Astrophysics Data System (ADS)

Borys, Damian; Bzowski, Pawel; Danch-Wierzchowska, Marta; Psiuk-Maksymowicz, Krzysztof

2017-03-01

In this paper, we evaluate the performance of SURF descriptor for high resolution satellite imagery (HRSI) retrieval through a BoVW model on a land-use/land-cover (LULC) dataset. Local feature approaches such as SIFT and SURF descriptors can deal with a large variation of scale, rotation and illumination of the images, providing, therefore, a better discriminative power and retrieval efficiency than global features, especially for HRSI which contain a great range of objects and spatial patterns. Moreover, we combine SURF and color features to improve the retrieval accuracy, and we propose to learn a category-specific dictionary for each image category which results in a more discriminative image representation and boosts the image retrieval performance.

High resolution satellite image indexing and retrieval using SURF features and bag of visual words

NASA Astrophysics Data System (ADS)

Bouteldja, Samia; Kourgli, Assia

2017-03-01

In this paper, we evaluate the performance of SURF descriptor for high resolution satellite imagery (HRSI) retrieval through a BoVW model on a land-use/land-cover (LULC) dataset. Local feature approaches such as SIFT and SURF descriptors can deal with a large variation of scale, rotation and illumination of the images, providing, therefore, a better discriminative power and retrieval efficiency than global features, especially for HRSI which contain a great range of objects and spatial patterns. Moreover, we combine SURF and color features to improve the retrieval accuracy, and we propose to learn a category-specific dictionary for each image category which results in a more discriminative image representation and boosts the image retrieval performance.

Skeletonization with hollow detection on gray image by gray weighted distance transform

NASA Astrophysics Data System (ADS)

Bhattacharya, Prabir; Qian, Kai; Cao, Siqi; Qian, Yi

1998-10-01

A skeletonization algorithm that could be used to process non-uniformly distributed gray-scale images with hollows was presented. This algorithm is based on the Gray Weighted Distance Transformation. The process includes a preliminary phase of investigation in the hollows in the gray-scale image, whether these hollows are considered as topological constraints for the skeleton structure depending on their statistically significant depth. We then extract the resulting skeleton that has certain meaningful information for understanding the object in the image. This improved algorithm can overcome the possible misinterpretation of some complicated images in the extracted skeleton, especially in images with asymmetric hollows and asymmetric features. This algorithm can be executed on a parallel machine as all the operations are executed in local. Some examples are discussed to illustrate the algorithm.

Infrared face recognition based on LBP histogram and KW feature selection

NASA Astrophysics Data System (ADS)

Xie, Zhihua

2014-07-01

The conventional LBP-based feature as represented by the local binary pattern (LBP) histogram still has room for performance improvements. This paper focuses on the dimension reduction of LBP micro-patterns and proposes an improved infrared face recognition method based on LBP histogram representation. To extract the local robust features in infrared face images, LBP is chosen to get the composition of micro-patterns of sub-blocks. Based on statistical test theory, Kruskal-Wallis (KW) feature selection method is proposed to get the LBP patterns which are suitable for infrared face recognition. The experimental results show combination of LBP and KW features selection improves the performance of infrared face recognition, the proposed method outperforms the traditional methods based on LBP histogram, discrete cosine transform(DCT) or principal component analysis(PCA).

How I report breast magnetic resonance imaging studies for breast cancer staging and screening.

PubMed

Vinnicombe, Sarah

2016-07-25

Magnetic resonance imaging (MRI) of the breast is the most sensitive imaging technique for the diagnosis and local staging of primary breast cancer and yet, despite the fact that it has been in use for 20 years, there is little evidence that its widespread uncritical adoption has had a positive impact on patient-related outcomes.This has been attributed previously to the low specificity that might be expected with such a sensitive modality, but with modern techniques and protocols, the specificity and positive predictive value for malignancy can exceed that of breast ultrasound and mammography. A more likely explanation is that historically, clinicians have acted on MRI findings and altered surgical plans without prior histological confirmation. Furthermore, modern adjuvant therapy for breast cancer has improved so much that it has become a very tall order to show a an improvement in outcomes such as local recurrence rates.In order to obtain clinically useful information, it is necessary to understand the strengths and weaknesses of the technique and the physiological processes reflected in breast MRI. An appropriate indication for the scan, proper patient preparation and good scan technique, with rigorous quality assurance, are all essential prerequisites for a diagnostically relevant study.The use of recognised descriptors from a standardised lexicon is helpful, since assessment can then dictate subsequent recommendations for management, as in the American College of Radiology BI-RADS (Breast Imaging Reporting and Data System) lexicon (Morris et al., ACR BI-RADS® Atlas, Breast Imaging Reporting and Data System, 2013). It also enables audit of the service. However, perhaps the most critical factor in the generation of a meaningful report is for the reporting radiologist to have a thorough understanding of the clinical question and of the findings that will influence management. This has never been more important than at present, when we are in the throes of a remarkable paradigm shift in the treatment of both early stage and locally advanced breast cancer.

Hybrid Vision-Fusion system for whole-body scintigraphy.

PubMed

Barjaktarović, Marko; Janković, Milica M; Jeremić, Marija; Matović, Milovan

2018-05-01

Radioiodine therapy in the treatment of differentiated thyroid carcinoma (DTC) is used in clinical practice for the ablation of thyroid residues and/or destruction of tumour tissue. Whole-body scintigraphy for visualization of the spatial 131I distribution performed by a gamma camera (GC) is a standard procedure in DTC patients after application of radioiodine therapy. A common problem is the precise topographic localization of regions where radioiodine is accumulated even in SPECT imaging. SPECT/CT can provide precise topographic localization of regions where radioiodine is accumulated, but it is often unavailable, especially in developing countries because of the high price of the equipment. In this paper, we present a Vision-Fusion system as an affordable solution for 1) acquiring an optical whole-body image during routine whole-body scintigraphy and 2) fusing gamma and optical images (also available for the auto-contour mode of GC). The estimated prediction error for image registration is 1.84 mm. The validity of fusing was tested by performing simultaneous optical and scintigraphy image acquisition of the bar phantom. The fusion result shows that the fusing process has a slight influence and is lower than the spatial resolution of GC (mean value ± standard deviation: 1.24 ± 0.22 mm). The Vision-Fusion system was used for radioiodine post-therapeutic treatment, and 17 patients were followed (11 women and 6 men, with an average age of 48.18 ± 13.27 years). Visual inspection showed no misregistration. Based on our first clinical experience, we noticed that the Vision-Fusion system could be very useful for improving the diagnostic possibility of whole-body scintigraphy after radioiodine therapy. Additionally, the proposed Vision-Fusion software can be used as an upgrade for any GC to improve localizations of thyroid/tumour tissue. Copyright © 2018 Elsevier Ltd. All rights reserved.

Multiparametric [18F]Fluorodeoxyglucose/ [18F]Fluoromisonidazole Positron Emission Tomography/ Magnetic Resonance Imaging of Locally Advanced Cervical Cancer for the Non-Invasive Detection of Tumor Heterogeneity: A Pilot Study

PubMed Central

Andrzejewski, Piotr; Baltzer, Pascal; Polanec, Stephan H.; Sturdza, Alina; Georg, Dietmar; Helbich, Thomas H.; Karanikas, Georgios; Grimm, Christoph; Polterauer, Stephan; Poetter, Richard; Wadsak, Wolfgang; Mitterhauser, Markus; Georg, Petra

2016-01-01

Objectives To investigate fused multiparametric positron emission tomography/magnetic resonance imaging (MP PET/MRI) at 3T in patients with locally advanced cervical cancer, using high-resolution T2-weighted, contrast-enhanced MRI (CE-MRI), diffusion-weighted imaging (DWI), and the radiotracers [18F]fluorodeoxyglucose ([18F]FDG) and [18F]fluoromisonidazol ([18F]FMISO) for the non-invasive detection of tumor heterogeneity for an improved planning of chemo-radiation therapy (CRT). Materials and Methods Sixteen patients with locally advanced cervix were enrolled in this IRB approved and were examined with fused MP [18F]FDG/ [18F]FMISO PET/MRI and in eleven patients complete data sets were acquired. MP PET/MRI was assessed for tumor volume, enhancement (EH)-kinetics, diffusivity, and [18F]FDG/ [18F]FMISO-avidity. Descriptive statistics and voxel-by-voxel analysis of MRI and PET parameters were performed. Correlations were assessed using multiple correlation analysis. Results All tumors displayed imaging parameters concordant with cervix cancer, i.e. type II/III EH-kinetics, restricted diffusivity (median ADC 0.80x10-3mm2/sec), [18F]FDG- (median SUVmax16.2) and [18F]FMISO-avidity (median SUVmax3.1). In all patients, [18F]FMISO PET identified the hypoxic tumor subvolume, which was independent of tumor volume. A voxel-by-voxel analysis revealed only weak correlations between the MRI and PET parameters (0.05–0.22), indicating that each individual parameter yields independent information and the presence of tumor heterogeneity. Conclusion MP [18F]FDG/ [18F]FMISO PET/MRI in patients with cervical cancer facilitates the acquisition of independent predictive and prognostic imaging parameters. MP [18F]FDG/ [18F]FMISO PET/MRI enables insights into tumor biology on multiple levels and provides information on tumor heterogeneity, which has the potential to improve the planning of CRT. PMID:27167829

Multiparametric [18F]Fluorodeoxyglucose/ [18F]Fluoromisonidazole Positron Emission Tomography/ Magnetic Resonance Imaging of Locally Advanced Cervical Cancer for the Non-Invasive Detection of Tumor Heterogeneity: A Pilot Study.

PubMed

Pinker, Katja; Andrzejewski, Piotr; Baltzer, Pascal; Polanec, Stephan H; Sturdza, Alina; Georg, Dietmar; Helbich, Thomas H; Karanikas, Georgios; Grimm, Christoph; Polterauer, Stephan; Poetter, Richard; Wadsak, Wolfgang; Mitterhauser, Markus; Georg, Petra

2016-01-01

To investigate fused multiparametric positron emission tomography/magnetic resonance imaging (MP PET/MRI) at 3T in patients with locally advanced cervical cancer, using high-resolution T2-weighted, contrast-enhanced MRI (CE-MRI), diffusion-weighted imaging (DWI), and the radiotracers [18F]fluorodeoxyglucose ([18F]FDG) and [18F]fluoromisonidazol ([18F]FMISO) for the non-invasive detection of tumor heterogeneity for an improved planning of chemo-radiation therapy (CRT). Sixteen patients with locally advanced cervix were enrolled in this IRB approved and were examined with fused MP [18F]FDG/ [18F]FMISO PET/MRI and in eleven patients complete data sets were acquired. MP PET/MRI was assessed for tumor volume, enhancement (EH)-kinetics, diffusivity, and [18F]FDG/ [18F]FMISO-avidity. Descriptive statistics and voxel-by-voxel analysis of MRI and PET parameters were performed. Correlations were assessed using multiple correlation analysis. All tumors displayed imaging parameters concordant with cervix cancer, i.e. type II/III EH-kinetics, restricted diffusivity (median ADC 0.80x10-3mm2/sec), [18F]FDG- (median SUVmax16.2) and [18F]FMISO-avidity (median SUVmax3.1). In all patients, [18F]FMISO PET identified the hypoxic tumor subvolume, which was independent of tumor volume. A voxel-by-voxel analysis revealed only weak correlations between the MRI and PET parameters (0.05-0.22), indicating that each individual parameter yields independent information and the presence of tumor heterogeneity. MP [18F]FDG/ [18F]FMISO PET/MRI in patients with cervical cancer facilitates the acquisition of independent predictive and prognostic imaging parameters. MP [18F]FDG/ [18F]FMISO PET/MRI enables insights into tumor biology on multiple levels and provides information on tumor heterogeneity, which has the potential to improve the planning of CRT.

Atlas-based head modeling and spatial normalization for high-density diffuse optical tomography: in vivo validation against fMRI.

PubMed

Ferradal, Silvina L; Eggebrecht, Adam T; Hassanpour, Mahlega; Snyder, Abraham Z; Culver, Joseph P

2014-01-15

Diffuse optical imaging (DOI) is increasingly becoming a valuable neuroimaging tool when fMRI is precluded. Recent developments in high-density diffuse optical tomography (HD-DOT) overcome previous limitations of sparse DOI systems, providing improved image quality and brain specificity. These improvements in instrumentation prompt the need for advancements in both i) realistic forward light modeling for accurate HD-DOT image reconstruction, and ii) spatial normalization for voxel-wise comparisons across subjects. Individualized forward light models derived from subject-specific anatomical images provide the optimal inverse solutions, but such modeling may not be feasible in all situations. In the absence of subject-specific anatomical images, atlas-based head models registered to the subject's head using cranial fiducials provide an alternative solution. In addition, a standard atlas is attractive because it defines a common coordinate space in which to compare results across subjects. The question therefore arises as to whether atlas-based forward light modeling ensures adequate HD-DOT image quality at the individual and group level. Herein, we demonstrate the feasibility of using atlas-based forward light modeling and spatial normalization methods. Both techniques are validated using subject-matched HD-DOT and fMRI data sets for visual evoked responses measured in five healthy adult subjects. HD-DOT reconstructions obtained with the registered atlas anatomy (i.e. atlas DOT) had an average localization error of 2.7mm relative to reconstructions obtained with the subject-specific anatomical images (i.e. subject-MRI DOT), and 6.6mm relative to fMRI data. At the group level, the localization error of atlas DOT reconstruction was 4.2mm relative to subject-MRI DOT reconstruction, and 6.1mm relative to fMRI. These results show that atlas-based image reconstruction provides a viable approach to individual head modeling for HD-DOT when anatomical imaging is not available. Copyright © 2013. Published by Elsevier Inc.

Emission reduction by multipurpose buffer strips on arable fields.

PubMed

Sloots, K; van der Vlies, A W

2007-01-01

In the area managed by Hollandse Delta, agriculture is under great pressure and the social awareness of the agricultural sector is increasing steadily. In recent years, a stand-still has been observed in water quality, in terms of agrochemicals, and concentrations even exceed the standard. To improve the waterquality a multi-purpose Field Margin Regulation was drafted for the Hoeksche Waard island in 2005. The regulation prescribes a crop-free strip, 3.5 m wide, alongside wet drainage ditches. The strip must be sown with mixtures of grasses, flowers or herbs. No crop protection chemicals or fertilizer may be used on the strips. A total length of approximately 200 km of buffer strip has now been laid. Besides reducing emissions, the buffer strips also stimulate natural pest control methods and encourage local tourism. Finally, the strips should lead to an improvement in the farmers' image. The regulation has proved to be successful. The buffer strips boosted both local tourism and the image of the agricultural sector. Above all, the strips provided a natural shield for emission to surface water, which will lead to an improvement of the water quality and raise the farmers' awareness of water quality and the environment.

Robust segmentation of trabecular bone for in vivo CT imaging using anisotropic diffusion and multi-scale morphological reconstruction

NASA Astrophysics Data System (ADS)

Chen, Cheng; Jin, Dakai; Zhang, Xiaoliu; Levy, Steven M.; Saha, Punam K.

2017-03-01

Osteoporosis is associated with an increased risk of low-trauma fractures. Segmentation of trabecular bone (TB) is essential to assess TB microstructure, which is a key determinant of bone strength and fracture risk. Here, we present a new method for TB segmentation for in vivo CT imaging. The method uses Hessian matrix-guided anisotropic diffusion to improve local separability of trabecular structures, followed by a new multi-scale morphological reconstruction algorithm for TB segmentation. High sensitivity (0.93), specificity (0.93), and accuracy (0.92) were observed for the new method based on regional manual thresholding on in vivo CT images. Mechanical tests have shown that TB segmentation using the new method improved the ability of derived TB spacing measure for predicting actual bone strength (R2=0.83).

Object-oriented recognition of high-resolution remote sensing image

NASA Astrophysics Data System (ADS)

Wang, Yongyan; Li, Haitao; Chen, Hong; Xu, Yuannan

2016-01-01

With the development of remote sensing imaging technology and the improvement of multi-source image's resolution in satellite visible light, multi-spectral and hyper spectral , the high resolution remote sensing image has been widely used in various fields, for example military field, surveying and mapping, geophysical prospecting, environment and so forth. In remote sensing image, the segmentation of ground targets, feature extraction and the technology of automatic recognition are the hotspot and difficulty in the research of modern information technology. This paper also presents an object-oriented remote sensing image scene classification method. The method is consist of vehicles typical objects classification generation, nonparametric density estimation theory, mean shift segmentation theory, multi-scale corner detection algorithm, local shape matching algorithm based on template. Remote sensing vehicles image classification software system is designed and implemented to meet the requirements .

Figure Text Extraction in Biomedical Literature

PubMed Central

Kim, Daehyun; Yu, Hong

2011-01-01

Background Figures are ubiquitous in biomedical full-text articles, and they represent important biomedical knowledge. However, the sheer volume of biomedical publications has made it necessary to develop computational approaches for accessing figures. Therefore, we are developing the Biomedical Figure Search engine (http://figuresearch.askHERMES.org) to allow bioscientists to access figures efficiently. Since text frequently appears in figures, automatically extracting such text may assist the task of mining information from figures. Little research, however, has been conducted exploring text extraction from biomedical figures. Methodology We first evaluated an off-the-shelf Optical Character Recognition (OCR) tool on its ability to extract text from figures appearing in biomedical full-text articles. We then developed a Figure Text Extraction Tool (FigTExT) to improve the performance of the OCR tool for figure text extraction through the use of three innovative components: image preprocessing, character recognition, and text correction. We first developed image preprocessing to enhance image quality and to improve text localization. Then we adapted the off-the-shelf OCR tool on the improved text localization for character recognition. Finally, we developed and evaluated a novel text correction framework by taking advantage of figure-specific lexicons. Results/Conclusions The evaluation on 382 figures (9,643 figure texts in total) randomly selected from PubMed Central full-text articles shows that FigTExT performed with 84% precision, 98% recall, and 90% F1-score for text localization and with 62.5% precision, 51.0% recall and 56.2% F1-score for figure text extraction. When limiting figure texts to those judged by domain experts to be important content, FigTExT performed with 87.3% precision, 68.8% recall, and 77% F1-score. FigTExT significantly improved the performance of the off-the-shelf OCR tool we used, which on its own performed with 36.6% precision, 19.3% recall, and 25.3% F1-score for text extraction. In addition, our results show that FigTExT can extract texts that do not appear in figure captions or other associated text, further suggesting the potential utility of FigTExT for improving figure search. PMID:21249186

Figure text extraction in biomedical literature.

PubMed

Kim, Daehyun; Yu, Hong

2011-01-13

Figures are ubiquitous in biomedical full-text articles, and they represent important biomedical knowledge. However, the sheer volume of biomedical publications has made it necessary to develop computational approaches for accessing figures. Therefore, we are developing the Biomedical Figure Search engine (http://figuresearch.askHERMES.org) to allow bioscientists to access figures efficiently. Since text frequently appears in figures, automatically extracting such text may assist the task of mining information from figures. Little research, however, has been conducted exploring text extraction from biomedical figures. We first evaluated an off-the-shelf Optical Character Recognition (OCR) tool on its ability to extract text from figures appearing in biomedical full-text articles. We then developed a Figure Text Extraction Tool (FigTExT) to improve the performance of the OCR tool for figure text extraction through the use of three innovative components: image preprocessing, character recognition, and text correction. We first developed image preprocessing to enhance image quality and to improve text localization. Then we adapted the off-the-shelf OCR tool on the improved text localization for character recognition. Finally, we developed and evaluated a novel text correction framework by taking advantage of figure-specific lexicons. The evaluation on 382 figures (9,643 figure texts in total) randomly selected from PubMed Central full-text articles shows that FigTExT performed with 84% precision, 98% recall, and 90% F1-score for text localization and with 62.5% precision, 51.0% recall and 56.2% F1-score for figure text extraction. When limiting figure texts to those judged by domain experts to be important content, FigTExT performed with 87.3% precision, 68.8% recall, and 77% F1-score. FigTExT significantly improved the performance of the off-the-shelf OCR tool we used, which on its own performed with 36.6% precision, 19.3% recall, and 25.3% F1-score for text extraction. In addition, our results show that FigTExT can extract texts that do not appear in figure captions or other associated text, further suggesting the potential utility of FigTExT for improving figure search.

Multiscale wavelet representations for mammographic feature analysis

NASA Astrophysics Data System (ADS)

Laine, Andrew F.; Song, Shuwu

1992-12-01

This paper introduces a novel approach for accomplishing mammographic feature analysis through multiresolution representations. We show that efficient (nonredundant) representations may be identified from digital mammography and used to enhance specific mammographic features within a continuum of scale space. The multiresolution decomposition of wavelet transforms provides a natural hierarchy in which to embed an interactive paradigm for accomplishing scale space feature analysis. Choosing wavelets (or analyzing functions) that are simultaneously localized in both space and frequency, results in a powerful methodology for image analysis. Multiresolution and orientation selectivity, known biological mechanisms in primate vision, are ingrained in wavelet representations and inspire the techniques presented in this paper. Our approach includes local analysis of complete multiscale representations. Mammograms are reconstructed from wavelet coefficients, enhanced by linear, exponential and constant weight functions localized in scale space. By improving the visualization of breast pathology we can improve the changes of early detection of breast cancers (improve quality) while requiring less time to evaluate mammograms for most patients (lower costs).

Local/non-local regularized image segmentation using graph-cuts: application to dynamic and multispectral MRI.

PubMed

Hanson, Erik A; Lundervold, Arvid

2013-11-01

Multispectral, multichannel, or time series image segmentation is important for image analysis in a wide range of applications. Regularization of the segmentation is commonly performed using local image information causing the segmented image to be locally smooth or piecewise constant. A new spatial regularization method, incorporating non-local information, was developed and tested. Our spatial regularization method applies to feature space classification in multichannel images such as color images and MR image sequences. The spatial regularization involves local edge properties, region boundary minimization, as well as non-local similarities. The method is implemented in a discrete graph-cut setting allowing fast computations. The method was tested on multidimensional MRI recordings from human kidney and brain in addition to simulated MRI volumes. The proposed method successfully segment regions with both smooth and complex non-smooth shapes with a minimum of user interaction.

CT reconstruction from portal images acquired during volumetric-modulated arc therapy

NASA Astrophysics Data System (ADS)

Poludniowski, G.; Thomas, M. D. R.; Evans, P. M.; Webb, S.

2010-10-01

Volumetric-modulated arc therapy (VMAT), a form of intensity-modulated arc therapy (IMAT), has become a topic of research and clinical activity in recent years. As a form of arc therapy, portal images acquired during the treatment fraction form a (partial) Radon transform of the patient. We show that these portal images, when used in a modified global cone-beam filtered backprojection (FBP) algorithm, allow a surprisingly recognizable CT-volume to be reconstructed. The possibility of distinguishing anatomy in such VMAT-CT reconstructions suggests that this could prove to be a valuable treatment position-verification tool. Further, some potential for local-tomography techniques to improve image quality is shown.

Recent advances in imaging cancer of the kidney and urinary tract.

PubMed

Hilton, Susan; Jones, Lisa P

2014-10-01

Modern radiologic imaging is an aid to treatment planning for localized renal cancer, enabling characterization of mass lesions. For patients who present with advanced renal cancer, new imaging techniques enable a functional assessment of treatment response not possible using anatomic measurements alone. Multidetector CT urography permits simultaneous assessment of the kidneys and urinary tract for patients with unexplained hematuria. Both CT and MRI play a significant role in staging and follow up of patients treated for urothelial cancer. Newer imaging methods such as diffusion-weighted MRI have shown promising results for improving accuracy of staging and follow up of urothelial cancer. Copyright © 2014 Elsevier Inc. All rights reserved.

Performance test and image correction of CMOS image sensor in radiation environment

NASA Astrophysics Data System (ADS)

Wang, Congzheng; Hu, Song; Gao, Chunming; Feng, Chang

2016-09-01

CMOS image sensors rival CCDs in domains that include strong radiation resistance as well as simple drive signals, so it is widely applied in the high-energy radiation environment, such as space optical imaging application and video monitoring of nuclear power equipment. However, the silicon material of CMOS image sensors has the ionizing dose effect in the high-energy rays, and then the indicators of image sensors, such as signal noise ratio (SNR), non-uniformity (NU) and bad point (BP) are degraded because of the radiation. The radiation environment of test experiments was generated by the 60Co γ-rays source. The camera module based on image sensor CMV2000 from CMOSIS Inc. was chosen as the research object. The ray dose used for the experiments was with a dose rate of 20krad/h. In the test experiences, the output signals of the pixels of image sensor were measured on the different total dose. The results of data analysis showed that with the accumulation of irradiation dose, SNR of image sensors decreased, NU of sensors was enhanced, and the number of BP increased. The indicators correction of image sensors was necessary, as it was the main factors to image quality. The image processing arithmetic was adopt to the data from the experiences in the work, which combined local threshold method with NU correction based on non-local means (NLM) method. The results from image processing showed that image correction can effectively inhibit the BP, improve the SNR, and reduce the NU.

Binarization algorithm for document image with complex background

NASA Astrophysics Data System (ADS)

Miao, Shaojun; Lu, Tongwei; Min, Feng

2015-12-01

The most important step in image preprocessing for Optical Character Recognition (OCR) is binarization. Due to the complex background or varying light in the text image, binarization is a very difficult problem. This paper presents the improved binarization algorithm. The algorithm can be divided into several steps. First, the background approximation can be obtained by the polynomial fitting, and the text is sharpened by using bilateral filter. Second, the image contrast compensation is done to reduce the impact of light and improve contrast of the original image. Third, the first derivative of the pixels in the compensated image are calculated to get the average value of the threshold, then the edge detection is obtained. Fourth, the stroke width of the text is estimated through a measuring of distance between edge pixels. The final stroke width is determined by choosing the most frequent distance in the histogram. Fifth, according to the value of the final stroke width, the window size is calculated, then a local threshold estimation approach can begin to binaries the image. Finally, the small noise is removed based on the morphological operators. The experimental result shows that the proposed method can effectively remove the noise caused by complex background and varying light.

New public dataset for spotting patterns in medieval document images

NASA Astrophysics Data System (ADS)

En, Sovann; Nicolas, Stéphane; Petitjean, Caroline; Jurie, Frédéric; Heutte, Laurent

2017-01-01

With advances in technology, a large part of our cultural heritage is becoming digitally available. In particular, in the field of historical document image analysis, there is now a growing need for indexing and data mining tools, thus allowing us to spot and retrieve the occurrences of an object of interest, called a pattern, in a large database of document images. Patterns may present some variability in terms of color, shape, or context, making the spotting of patterns a challenging task. Pattern spotting is a relatively new field of research, still hampered by the lack of available annotated resources. We present a new publicly available dataset named DocExplore dedicated to spotting patterns in historical document images. The dataset contains 1500 images and 1464 queries, and allows the evaluation of two tasks: image retrieval and pattern localization. A standardized benchmark protocol along with ad hoc metrics is provided for a fair comparison of the submitted approaches. We also provide some first results obtained with our baseline system on this new dataset, which show that there is room for improvement and that should encourage researchers of the document image analysis community to design new systems and submit improved results.

Educational Technology in Voc Ed. Information Series No. 268.

ERIC Educational Resources Information Center

Lipson, Joseph I.

This monograph provides a vision of the future for vocational educators in a position to improve programs, such as teachers and administrators of local educational agencies and state leaders who set priorities in educational agencies. The monograph addresses nationwide technological concerns of the computer, image storage and creation, and…

Feature Modeling in Underwater Environments Using Sparse Linear Combinations

DTIC Science & Technology

2010-01-01

nose of the tor- pedo obviously has a different optical depth than the tail and points in between. Our chosen PSF does not consider this, but it...IEEE Transactions on Information Theory, 52(4), 2006. 4 [6] R. Hess and A. Fern. Improved video registration using non-distinctive local image

Dorsolateral Frontal Lobe Epilepsy

PubMed Central

Lee, Ricky W.; Worrell, Greg A.

2012-01-01

Dorsolateral frontal lobe seizures often present as a diagnostic challenge. The diverse semiologies may not produce lateralizing or localizing signs, and can appear bizarre and suggest psychogenic events. Unfortunately, scalp EEG and MRI are often unsatisfactory. It is not uncommon that these traditional diagnostic studies are either unhelpful or even misleading. In some cases SPECT and PET imaging can be an effective tool to identify the origin of seizures. However, these techniques and other emerging techniques all have limitations, and new approaches are needed to improve source localization. PMID:23027094

Recent trends in soft-tissue infection imaging.

PubMed

Petruzzi, Nicholas; Shanthly, Nylla; Thakur, Mathew

2009-03-01

This article discusses the current techniques and future directions of infection imaging with particular attention to respiratory, central nervous system, abdominal, and postoperative infections. The agents currently in use localize to areas of infection and inflammation. An infection-specific imaging agent would greatly improve the utility of scintigraphy in imaging occult infections. The superior spatial resolution of (18)F-fluorodeoxyglucose positron emission tomography ((18)F-FDG-PET) and its lack of reliance on a functional immune system, gives this agent certain advantages over the other radiopharmaceuticals. In respiratory tract infection imaging, an important advancement would be the ability to quantitatively delineate lung inflammation, allowing one to monitor the therapeutic response in a variety of conditions. Current studies suggest PET should be considered the most accurate quantitative method. Scintigraphy has much to offer in localizing abdominal infection as well as inflammation. We may begin to see a gradual increase in the usage of (18)F-FDG-PET in detecting occult abdominal infections. Commonly used modalities for imaging inflammatory bowel disease are scintigraphy with (111)In-oxine/(99m)Tc-HMPAO labeled autologous white blood cells. The literature on central nervous system infection imaging is relatively scarce. Few clinical studies have been performed and numerous new agents have been developed for this use with varying results. Further studies are needed to more clearly delineate the future direction of this field. In evaluating the postoperative spine, (99m)Tc-ciprofloxacin single-photon emission computed tomography (SPECT) was reported to be >80% sensitive in patients more than 6 months after surgery. FDG-PET has also been suggested for this purpose and may play a larger role than originally thought. It appears PET/computed tomography (CT) is gaining support, especially in imaging those with fever of unknown origin or nonfunctional immune systems. Although an infection-specific agent is lacking, the development of one would greatly advance our ability to detect, localize, and quantify infections. Overall, imaging such an agent via SPECT/CT or PET/CT will pave the way for greater clinical reliability in the localization of infection.

Multispectral image sharpening using a shift-invariant wavelet transform and adaptive processing of multiresolution edges

USGS Publications Warehouse

Lemeshewsky, G.P.; Rahman, Z.-U.; Schowengerdt, R.A.; Reichenbach, S.E.

2002-01-01

Enhanced false color images from mid-IR, near-IR (NIR), and visible bands of the Landsat thematic mapper (TM) are commonly used for visually interpreting land cover type. Described here is a technique for sharpening or fusion of NIR with higher resolution panchromatic (Pan) that uses a shift-invariant implementation of the discrete wavelet transform (SIDWT) and a reported pixel-based selection rule to combine coefficients. There can be contrast reversals (e.g., at soil-vegetation boundaries between NIR and visible band images) and consequently degraded sharpening and edge artifacts. To improve performance for these conditions, I used a local area-based correlation technique originally reported for comparing image-pyramid-derived edges for the adaptive processing of wavelet-derived edge data. Also, using the redundant data of the SIDWT improves edge data generation. There is additional improvement because sharpened subband imagery is used with the edge-correlation process. A reported technique for sharpening three-band spectral imagery used forward and inverse intensity, hue, and saturation transforms and wavelet-based sharpening of intensity. This technique had limitations with opposite contrast data, and in this study sharpening was applied to single-band multispectral-Pan image pairs. Sharpening used simulated 30-m NIR imagery produced by degrading the spatial resolution of a higher resolution reference. Performance, evaluated by comparison between sharpened and reference image, was improved when sharpened subband data were used with the edge correlation.

Assistive technology for ultrasound-guided central venous catheter placement.

PubMed

Ikhsan, Mohammad; Tan, Kok Kiong; Putra, Andi Sudjana

2018-01-01

This study evaluated the existing technology used to improve the safety and ease of ultrasound-guided central venous catheterization. Electronic database searches were conducted in Scopus, IEEE, Google Patents, and relevant conference databases (SPIE, MICCAI, and IEEE conferences) for related articles on assistive technology for ultrasound-guided central venous catheterization. A total of 89 articles were examined and pointed to several fields that are currently the focus of improvements to ultrasound-guided procedures. These include improving needle visualization, needle guides and localization technology, image processing algorithms to enhance and segment important features within the ultrasound image, robotic assistance using probe-mounted manipulators, and improving procedure ergonomics through in situ projections of important information. Probe-mounted robotic manipulators provide a promising avenue for assistive technology developed for freehand ultrasound-guided percutaneous procedures. However, there is currently a lack of clinical trials to validate the effectiveness of these devices.

A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina

PubMed Central

Kafieh, Raheleh; Rabbani, Hossein; Kermani, Saeed

2013-01-01

Optical coherence tomography (OCT) is a recently established imaging technique to describe different information about the internal structures of an object and to image various aspects of biological tissues. OCT image segmentation is mostly introduced on retinal OCT to localize the intra-retinal boundaries. Here, we review some of the important image segmentation methods for processing retinal OCT images. We may classify the OCT segmentation approaches into five distinct groups according to the image domain subjected to the segmentation algorithm. Current researches in OCT segmentation are mostly based on improving the accuracy and precision, and on reducing the required processing time. There is no doubt that current 3-D imaging modalities are now moving the research projects toward volume segmentation along with 3-D rendering and visualization. It is also important to develop robust methods capable of dealing with pathologic cases in OCT imaging. PMID:24083137

Quantitative Analysis of {sup 18}F-Fluorodeoxyglucose Positron Emission Tomography Identifies Novel Prognostic Imaging Biomarkers in Locally Advanced Pancreatic Cancer Patients Treated With Stereotactic Body Radiation Therapy

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

Cui, Yi; Global Institution for Collaborative Research and Education, Hokkaido University, Sapporo; Song, Jie

Purpose: To identify prognostic biomarkers in pancreatic cancer using high-throughput quantitative image analysis. Methods and Materials: In this institutional review board–approved study, we retrospectively analyzed images and outcomes for 139 locally advanced pancreatic cancer patients treated with stereotactic body radiation therapy (SBRT). The overall population was split into a training cohort (n=90) and a validation cohort (n=49) according to the time of treatment. We extracted quantitative imaging characteristics from pre-SBRT {sup 18}F-fluorodeoxyglucose positron emission tomography, including statistical, morphologic, and texture features. A Cox proportional hazard regression model was built to predict overall survival (OS) in the training cohort using 162more » robust image features. To avoid over-fitting, we applied the elastic net to obtain a sparse set of image features, whose linear combination constitutes a prognostic imaging signature. Univariate and multivariate Cox regression analyses were used to evaluate the association with OS, and concordance index (CI) was used to evaluate the survival prediction accuracy. Results: The prognostic imaging signature included 7 features characterizing different tumor phenotypes, including shape, intensity, and texture. On the validation cohort, univariate analysis showed that this prognostic signature was significantly associated with OS (P=.002, hazard ratio 2.74), which improved upon conventional imaging predictors including tumor volume, maximum standardized uptake value, and total legion glycolysis (P=.018-.028, hazard ratio 1.51-1.57). On multivariate analysis, the proposed signature was the only significant prognostic index (P=.037, hazard ratio 3.72) when adjusted for conventional imaging and clinical factors (P=.123-.870, hazard ratio 0.53-1.30). In terms of CI, the proposed signature scored 0.66 and was significantly better than competing prognostic indices (CI 0.48-0.64, Wilcoxon rank sum test P<1e-6). Conclusion: Quantitative analysis identified novel {sup 18}F-fluorodeoxyglucose positron emission tomography image features that showed improved prognostic value over conventional imaging metrics. If validated in large, prospective cohorts, the new prognostic signature might be used to identify patients for individualized risk-adaptive therapy.« less

Confocal Adaptive Optics Imaging of Peripapillary Nerve Fiber Bundles: Implications for Glaucomatous Damage Seen on Circumpapillary OCT Scans.

PubMed

Hood, Donald C; Chen, Monica F; Lee, Dongwon; Epstein, Benjamin; Alhadeff, Paula; Rosen, Richard B; Ritch, Robert; Dubra, Alfredo; Chui, Toco Y P

2015-04-01

To improve our understanding of glaucomatous damage as seen on circumpapillary disc scans obtained with frequency-domain optical coherence tomography (fdOCT), fdOCT scans were compared to images of the peripapillary retinal nerve fiber (RNF) bundles obtained with an adaptive optics-scanning light ophthalmoscope (AO-SLO). The AO-SLO images and fdOCT scans were obtained on 6 eyes of 6 patients with deep arcuate defects (5 points ≤-15 db) on 10-2 visual fields. The AO-SLO images were montaged and aligned with the fdOCT images to compare the RNF bundles seen with AO-SLO to the RNF layer thickness measured with fdOCT. All 6 eyes had an abnormally thin (1% confidence limit) RNF layer (RNFL) on fdOCT and abnormal (hyporeflective) regions of RNF bundles on AO-SLO in corresponding regions. However, regions of abnormal, but equal, RNFL thickness on fdOCT scans varied in appearance on AO-SLO images. These regions could be largely devoid of RNF bundles (5 eyes), have abnormal-appearing bundles of lower contrast (6 eyes), or have isolated areas with a few relatively normal-appearing bundles (2 eyes). There also were local variations in reflectivity of the fdOCT RNFL that corresponded to the variations in AO-SLO RNF bundle appearance. Relatively similar 10-2 defects with similar fdOCT RNFL thickness profiles can have very different degrees of RNF bundle damage as seen on fdOCT and AO-SLO. While the results point to limitations of fdOCT RNFL thickness as typically analyzed, they also illustrate the potential for improving fdOCT by attending to variations in local intensity.

Percutaneous Thermal Ablation with Ultrasound Guidance. Fusion Imaging Guidance to Improve Conspicuity of Liver Metastasis

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

Hakime, Antoine, E-mail: thakime@yahoo.com; Yevich, Steven; Tselikas, Lambros

PurposeTo assess whether fusion imaging-guided percutaneous microwave ablation (MWA) can improve visibility and targeting of liver metastasis that were deemed inconspicuous on ultrasound (US).Materials and MethodsMWA of liver metastasis not judged conspicuous enough on US was performed under CT/US fusion imaging guidance. The conspicuity before and after the fusion imaging was graded on a five-point scale, and significance was assessed by Wilcoxon test. Technical success, procedure time, and procedure-related complications were evaluated.ResultsA total of 35 patients with 40 liver metastases (mean size 1.3 ± 0.4 cm) were enrolled. Image fusion improved conspicuity sufficiently to allow fusion-targeted MWA in 33 patients. The time requiredmore » for image fusion processing and tumors’ identification averaged 10 ± 2.1 min (range 5–14). Initial conspicuity on US by inclusion criteria was 1.2 ± 0.4 (range 0–2), while conspicuity after localization on fusion imaging was 3.5 ± 1 (range 1–5, p < 0.001). Technical success rate was 83% (33/40) in intention-to-treat analysis and 100% in analysis of treated tumors. There were no major procedure-related complications.ConclusionsFusion imaging broadens the scope of US-guided MWA to metastasis lacking adequate conspicuity on conventional US. Fusion imaging is an effective tool to increase the conspicuity of liver metastases that were initially deemed non visualizable on conventional US imaging.« less

Multi-atlas learner fusion: An efficient segmentation approach for large-scale data.

PubMed

Asman, Andrew J; Huo, Yuankai; Plassard, Andrew J; Landman, Bennett A

2015-12-01

We propose multi-atlas learner fusion (MLF), a framework for rapidly and accurately replicating the highly accurate, yet computationally expensive, multi-atlas segmentation framework based on fusing local learners. In the largest whole-brain multi-atlas study yet reported, multi-atlas segmentations are estimated for a training set of 3464 MR brain images. Using these multi-atlas estimates we (1) estimate a low-dimensional representation for selecting locally appropriate example images, and (2) build AdaBoost learners that map a weak initial segmentation to the multi-atlas segmentation result. Thus, to segment a new target image we project the image into the low-dimensional space, construct a weak initial segmentation, and fuse the trained, locally selected, learners. The MLF framework cuts the runtime on a modern computer from 36 h down to 3-8 min - a 270× speedup - by completely bypassing the need for deformable atlas-target registrations. Additionally, we (1) describe a technique for optimizing the weak initial segmentation and the AdaBoost learning parameters, (2) quantify the ability to replicate the multi-atlas result with mean accuracies approaching the multi-atlas intra-subject reproducibility on a testing set of 380 images, (3) demonstrate significant increases in the reproducibility of intra-subject segmentations when compared to a state-of-the-art multi-atlas framework on a separate reproducibility dataset, (4) show that under the MLF framework the large-scale data model significantly improve the segmentation over the small-scale model under the MLF framework, and (5) indicate that the MLF framework has comparable performance as state-of-the-art multi-atlas segmentation algorithms without using non-local information. Copyright © 2015 Elsevier B.V. All rights reserved.

Current use of imaging and electromagnetic source localization procedures in epilepsy surgery centers across Europe.

PubMed

Mouthaan, Brian E; Rados, Matea; Barsi, Péter; Boon, Paul; Carmichael, David W; Carrette, Evelien; Craiu, Dana; Cross, J Helen; Diehl, Beate; Dimova, Petia; Fabo, Daniel; Francione, Stefano; Gaskin, Vladislav; Gil-Nagel, Antonio; Grigoreva, Elena; Guekht, Alla; Hirsch, Edouard; Hecimovic, Hrvoje; Helmstaedter, Christoph; Jung, Julien; Kalviainen, Reetta; Kelemen, Anna; Kimiskidis, Vasilios; Kobulashvili, Teia; Krsek, Pavel; Kuchukhidze, Giorgi; Larsson, Pål G; Leitinger, Markus; Lossius, Morten I; Luzin, Roman; Malmgren, Kristina; Mameniskiene, Ruta; Marusic, Petr; Metin, Baris; Özkara, Cigdem; Pecina, Hrvoje; Quesada, Carlos M; Rugg-Gunn, Fergus; Rydenhag, Bertil; Ryvlin, Philippe; Scholly, Julia; Seeck, Margitta; Staack, Anke M; Steinhoff, Bernhard J; Stepanov, Valentin; Tarta-Arsene, Oana; Trinka, Eugen; Uzan, Mustafa; Vogt, Viola L; Vos, Sjoerd B; Vulliémoz, Serge; Huiskamp, Geertjan; Leijten, Frans S S; Van Eijsden, Pieter; Braun, Kees P J

2016-05-01

In 2014 the European Union-funded E-PILEPSY project was launched to improve awareness of, and accessibility to, epilepsy surgery across Europe. We aimed to investigate the current use of neuroimaging, electromagnetic source localization, and imaging postprocessing procedures in participating centers. A survey on the clinical use of imaging, electromagnetic source localization, and postprocessing methods in epilepsy surgery candidates was distributed among the 25 centers of the consortium. A descriptive analysis was performed, and results were compared to existing guidelines and recommendations. Response rate was 96%. Standard epilepsy magnetic resonance imaging (MRI) protocols are acquired at 3 Tesla by 15 centers and at 1.5 Tesla by 9 centers. Three centers perform 3T MRI only if indicated. Twenty-six different MRI sequences were reported. Six centers follow all guideline-recommended MRI sequences with the proposed slice orientation and slice thickness or voxel size. Additional sequences are used by 22 centers. MRI postprocessing methods are used in 16 centers. Interictal positron emission tomography (PET) is available in 22 centers; all using 18F-fluorodeoxyglucose (FDG). Seventeen centers perform PET postprocessing. Single-photon emission computed tomography (SPECT) is used by 19 centers, of which 15 perform postprocessing. Four centers perform neither PET nor SPECT in children. Seven centers apply magnetoencephalography (MEG) source localization, and nine apply electroencephalography (EEG) source localization. Fourteen combinations of inverse methods and volume conduction models are used. We report a large variation in the presurgical diagnostic workup among epilepsy surgery centers across Europe. This diversity underscores the need for high-quality systematic reviews, evidence-based recommendations, and harmonization of available diagnostic presurgical methods. Wiley Periodicals, Inc. © 2016 International League Against Epilepsy.

Determining the distribution of probes between different subcellular locations through automated unmixing of subcellular patterns.

PubMed

Peng, Tao; Bonamy, Ghislain M C; Glory-Afshar, Estelle; Rines, Daniel R; Chanda, Sumit K; Murphy, Robert F

2010-02-16

Many proteins or other biological macromolecules are localized to more than one subcellular structure. The fraction of a protein in different cellular compartments is often measured by colocalization with organelle-specific fluorescent markers, requiring availability of fluorescent probes for each compartment and acquisition of images for each in conjunction with the macromolecule of interest. Alternatively, tailored algorithms allow finding particular regions in images and quantifying the amount of fluorescence they contain. Unfortunately, this approach requires extensive hand-tuning of algorithms and is often cell type-dependent. Here we describe a machine-learning approach for estimating the amount of fluorescent signal in different subcellular compartments without hand tuning, requiring only the acquisition of separate training images of markers for each compartment. In testing on images of cells stained with mixtures of probes for different organelles, we achieved a 93% correlation between estimated and expected amounts of probes in each compartment. We also demonstrated that the method can be used to quantify drug-dependent protein translocations. The method enables automated and unbiased determination of the distributions of protein across cellular compartments, and will significantly improve imaging-based high-throughput assays and facilitate proteome-scale localization efforts.

Novel Immunohistochemical Techniques Using Discrete Signal Amplification Systems for Human Cutaneous Peripheral Nerve Fiber Imaging

PubMed Central

Wang, Ningshan; Gibbons, Christopher H.; Freeman, Roy

2011-01-01

Confocal imaging uses immunohistochemical binding of specific antibodies to visualize tissues, but technical obstacles limit more widespread use of this technique in the imaging of peripheral nerve tissue. These obstacles include same-species antibody cross-reactivity and weak fluorescent signals of individual and co-localized antigens. The aims of this study were to develop new immunohistochemical techniques for imaging of peripheral nerve fibers. Three-millimeter punch skin biopsies of healthy individuals were fixed, frozen, and cut into 50-µm sections. Tissues were stained with a variety of antibody combinations with two signal amplification systems, streptavidin-biotin-fluorochrome (sABC) and tyramide-horseradish peroxidase-fluorochrome (TSA), used simultaneously to augment immunohistochemical signals. The combination of the TSA and sABC amplification systems provided the first successful co-localization of sympathetic adrenergic and sympathetic cholinergic nerve fibers in cutaneous human sweat glands and vasomotor and pilomotor systems. Primary antibodies from the same species were amplified individually without cross-reactivity or elevated background interference. The confocal fluorescent signal-to-noise ratio increased, and image clarity improved. These modifications to signal amplification systems have the potential for widespread use in the study of human neural tissues. PMID:21411809

An intelligent space for mobile robot localization using a multi-camera system.

PubMed

Rampinelli, Mariana; Covre, Vitor Buback; de Queiroz, Felippe Mendonça; Vassallo, Raquel Frizera; Bastos-Filho, Teodiano Freire; Mazo, Manuel

2014-08-15

This paper describes an intelligent space, whose objective is to localize and control robots or robotic wheelchairs to help people. Such an intelligent space has 11 cameras distributed in two laboratories and a corridor. The cameras are fixed in the environment, and image capturing is done synchronously. The system was programmed as a client/server with TCP/IP connections, and a communication protocol was defined. The client coordinates the activities inside the intelligent space, and the servers provide the information needed for that. Once the cameras are used for localization, they have to be properly calibrated. Therefore, a calibration method for a multi-camera network is also proposed in this paper. A robot is used to move a calibration pattern throughout the field of view of the cameras. Then, the captured images and the robot odometry are used for calibration. As a result, the proposed algorithm provides a solution for multi-camera calibration and robot localization at the same time. The intelligent space and the calibration method were evaluated under different scenarios using computer simulations and real experiments. The results demonstrate the proper functioning of the intelligent space and validate the multi-camera calibration method, which also improves robot localization.

An Intelligent Space for Mobile Robot Localization Using a Multi-Camera System

PubMed Central

Rampinelli, Mariana.; Covre, Vitor Buback.; de Queiroz, Felippe Mendonça.; Vassallo, Raquel Frizera.; Bastos-Filho, Teodiano Freire.; Mazo, Manuel.

2014-01-01

This paper describes an intelligent space, whose objective is to localize and control robots or robotic wheelchairs to help people. Such an intelligent space has 11 cameras distributed in two laboratories and a corridor. The cameras are fixed in the environment, and image capturing is done synchronously. The system was programmed as a client/server with TCP/IP connections, and a communication protocol was defined. The client coordinates the activities inside the intelligent space, and the servers provide the information needed for that. Once the cameras are used for localization, they have to be properly calibrated. Therefore, a calibration method for a multi-camera network is also proposed in this paper. A robot is used to move a calibration pattern throughout the field of view of the cameras. Then, the captured images and the robot odometry are used for calibration. As a result, the proposed algorithm provides a solution for multi-camera calibration and robot localization at the same time. The intelligent space and the calibration method were evaluated under different scenarios using computer simulations and real experiments. The results demonstrate the proper functioning of the intelligent space and validate the multi-camera calibration method, which also improves robot localization. PMID:25196009

GIFTed Demons: deformable image registration with local structure-preserving regularization using supervoxels for liver applications

PubMed Central

Gleeson, Fergus V.; Brady, Michael; Schnabel, Julia A.

2018-01-01

Abstract. Deformable image registration, a key component of motion correction in medical imaging, needs to be efficient and provides plausible spatial transformations that reliably approximate biological aspects of complex human organ motion. Standard approaches, such as Demons registration, mostly use Gaussian regularization for organ motion, which, though computationally efficient, rule out their application to intrinsically more complex organ motions, such as sliding interfaces. We propose regularization of motion based on supervoxels, which provides an integrated discontinuity preserving prior for motions, such as sliding. More precisely, we replace Gaussian smoothing by fast, structure-preserving, guided filtering to provide efficient, locally adaptive regularization of the estimated displacement field. We illustrate the approach by applying it to estimate sliding motions at lung and liver interfaces on challenging four-dimensional computed tomography (CT) and dynamic contrast-enhanced magnetic resonance imaging datasets. The results show that guided filter-based regularization improves the accuracy of lung and liver motion correction as compared to Gaussian smoothing. Furthermore, our framework achieves state-of-the-art results on a publicly available CT liver dataset. PMID:29662918

GIFTed Demons: deformable image registration with local structure-preserving regularization using supervoxels for liver applications.

PubMed

Papież, Bartłomiej W; Franklin, James M; Heinrich, Mattias P; Gleeson, Fergus V; Brady, Michael; Schnabel, Julia A

2018-04-01

Deformable image registration, a key component of motion correction in medical imaging, needs to be efficient and provides plausible spatial transformations that reliably approximate biological aspects of complex human organ motion. Standard approaches, such as Demons registration, mostly use Gaussian regularization for organ motion, which, though computationally efficient, rule out their application to intrinsically more complex organ motions, such as sliding interfaces. We propose regularization of motion based on supervoxels, which provides an integrated discontinuity preserving prior for motions, such as sliding. More precisely, we replace Gaussian smoothing by fast, structure-preserving, guided filtering to provide efficient, locally adaptive regularization of the estimated displacement field. We illustrate the approach by applying it to estimate sliding motions at lung and liver interfaces on challenging four-dimensional computed tomography (CT) and dynamic contrast-enhanced magnetic resonance imaging datasets. The results show that guided filter-based regularization improves the accuracy of lung and liver motion correction as compared to Gaussian smoothing. Furthermore, our framework achieves state-of-the-art results on a publicly available CT liver dataset.

Local search for optimal global map generation using mid-decadal landsat images

USGS Publications Warehouse

Khatib, L.; Gasch, J.; Morris, Robert; Covington, S.

2007-01-01

NASA and the US Geological Survey (USGS) are seeking to generate a map of the entire globe using Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor data from the "mid-decadal" period of 2004 through 2006. The global map is comprised of thousands of scene locations and, for each location, tens of different images of varying quality to chose from. Furthermore, it is desirable for images of adjacent scenes be close together in time of acquisition, to avoid obvious discontinuities due to seasonal changes. These characteristics make it desirable to formulate an automated solution to the problem of generating the complete map. This paper formulates a Global Map Generator problem as a Constraint Optimization Problem (GMG-COP) and describes an approach to solving it using local search. Preliminary results of running the algorithm on image data sets are summarized. The results suggest a significant improvement in map quality using constraint-based solutions. Copyright ?? 2007, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Handheld real-time volumetric 3-D gamma-ray imaging

NASA Astrophysics Data System (ADS)

Haefner, Andrew; Barnowski, Ross; Luke, Paul; Amman, Mark; Vetter, Kai

2017-06-01

This paper presents the concept of real-time fusion of gamma-ray imaging and visual scene data for a hand-held mobile Compton imaging system in 3-D. The ability to obtain and integrate both gamma-ray and scene data from a mobile platform enables improved capabilities in the localization and mapping of radioactive materials. This not only enhances the ability to localize these materials, but it also provides important contextual information of the scene which once acquired can be reviewed and further analyzed subsequently. To demonstrate these concepts, the high-efficiency multimode imager (HEMI) is used in a hand-portable implementation in combination with a Microsoft Kinect sensor. This sensor, in conjunction with open-source software, provides the ability to create a 3-D model of the scene and to track the position and orientation of HEMI in real-time. By combining the gamma-ray data and visual data, accurate 3-D maps of gamma-ray sources are produced in real-time. This approach is extended to map the location of radioactive materials within objects with unknown geometry.