Head CT: Image quality improvement of posterior fossa and radiation dose reduction with ASiR - comparative studies of CT head examinations.

PubMed

Guziński, Maciej; Waszczuk, Łukasz; Sąsiadek, Marek J

2016-10-01

To evaluate head CT protocol developed to improve visibility of the brainstem and cerebellum, lower bone-related artefacts in the posterior fossa and maintain patient radioprotection. A paired comparison of head CT performed without Adaptive Statistical Iterative Reconstruction (ASiR) and a clinically indicated follow-up with 40 % ASiR was acquired in one group of 55 patients. Patients were scanned in the axial mode with different scanner settings for the brain and the posterior fossa. Objective image quality analysis was performed with signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Subjective image quality analysis was based on brain structure visibility and evaluation of the artefacts. We achieved 19 % reduction of total DLP and significantly better image quality of posterior fossa structures. SNR for white and grey matter in the cerebellum were 34 % to 36 % higher, respectively, CNR was improved by 142 % and subjective analyses were better for images with ASiR. When imaging parameters are set independently for the brain and the posterior fossa imaging, ASiR has a great potential to improve CT performance: image quality of the brainstem and cerebellum is improved, and radiation dose for the brain as well as total radiation dose are reduced. •With ASiR it is possible to lower radiation dose or improve image quality •Sequentional imaging allows setting scan parameters for brain and posterior-fossa independently •We improved visibility of brainstem structures and decreased radiation dose •Total radiation dose (DLP) was decreased by 19.

Quality Improvement of Liver Ultrasound Images Using Fuzzy Techniques.

PubMed

Bayani, Azadeh; Langarizadeh, Mostafa; Radmard, Amir Reza; Nejad, Ahmadreza Farzaneh

2016-12-01

Liver ultrasound images are so common and are applied so often to diagnose diffuse liver diseases like fatty liver. However, the low quality of such images makes it difficult to analyze them and diagnose diseases. The purpose of this study, therefore, is to improve the contrast and quality of liver ultrasound images. In this study, a number of image contrast enhancement algorithms which are based on fuzzy logic were applied to liver ultrasound images - in which the view of kidney is observable - using Matlab2013b to improve the image contrast and quality which has a fuzzy definition; just like image contrast improvement algorithms using a fuzzy intensification operator, contrast improvement algorithms applying fuzzy image histogram hyperbolization, and contrast improvement algorithms by fuzzy IF-THEN rules. With the measurement of Mean Squared Error and Peak Signal to Noise Ratio obtained from different images, fuzzy methods provided better results, and their implementation - compared with histogram equalization method - led both to the improvement of contrast and visual quality of images and to the improvement of liver segmentation algorithms results in images. Comparison of the four algorithms revealed the power of fuzzy logic in improving image contrast compared with traditional image processing algorithms. Moreover, contrast improvement algorithm based on a fuzzy intensification operator was selected as the strongest algorithm considering the measured indicators. This method can also be used in future studies on other ultrasound images for quality improvement and other image processing and analysis applications.

Quality Improvement of Liver Ultrasound Images Using Fuzzy Techniques

PubMed Central

Bayani, Azadeh; Langarizadeh, Mostafa; Radmard, Amir Reza; Nejad, Ahmadreza Farzaneh

2016-01-01

Background: Liver ultrasound images are so common and are applied so often to diagnose diffuse liver diseases like fatty liver. However, the low quality of such images makes it difficult to analyze them and diagnose diseases. The purpose of this study, therefore, is to improve the contrast and quality of liver ultrasound images. Methods: In this study, a number of image contrast enhancement algorithms which are based on fuzzy logic were applied to liver ultrasound images - in which the view of kidney is observable - using Matlab2013b to improve the image contrast and quality which has a fuzzy definition; just like image contrast improvement algorithms using a fuzzy intensification operator, contrast improvement algorithms applying fuzzy image histogram hyperbolization, and contrast improvement algorithms by fuzzy IF-THEN rules. Results: With the measurement of Mean Squared Error and Peak Signal to Noise Ratio obtained from different images, fuzzy methods provided better results, and their implementation - compared with histogram equalization method - led both to the improvement of contrast and visual quality of images and to the improvement of liver segmentation algorithms results in images. Conclusion: Comparison of the four algorithms revealed the power of fuzzy logic in improving image contrast compared with traditional image processing algorithms. Moreover, contrast improvement algorithm based on a fuzzy intensification operator was selected as the strongest algorithm considering the measured indicators. This method can also be used in future studies on other ultrasound images for quality improvement and other image processing and analysis applications. PMID:28077898

Hierarchical classification strategy for Phenotype extraction from epidermal growth factor receptor endocytosis screening.

PubMed

Cao, Lu; Graauw, Marjo de; Yan, Kuan; Winkel, Leah; Verbeek, Fons J

2016-05-03

Endocytosis is regarded as a mechanism of attenuating the epidermal growth factor receptor (EGFR) signaling and of receptor degradation. There is increasing evidence becoming available showing that breast cancer progression is associated with a defect in EGFR endocytosis. In order to find related Ribonucleic acid (RNA) regulators in this process, high-throughput imaging with fluorescent markers is used to visualize the complex EGFR endocytosis process. Subsequently a dedicated automatic image and data analysis system is developed and applied to extract the phenotype measurement and distinguish different developmental episodes from a huge amount of images acquired through high-throughput imaging. For the image analysis, a phenotype measurement quantifies the important image information into distinct features or measurements. Therefore, the manner in which prominent measurements are chosen to represent the dynamics of the EGFR process becomes a crucial step for the identification of the phenotype. In the subsequent data analysis, classification is used to categorize each observation by making use of all prominent measurements obtained from image analysis. Therefore, a better construction for a classification strategy will support to raise the performance level in our image and data analysis system. In this paper, we illustrate an integrated analysis method for EGFR signalling through image analysis of microscopy images. Sophisticated wavelet-based texture measurements are used to obtain a good description of the characteristic stages in the EGFR signalling. A hierarchical classification strategy is designed to improve the recognition of phenotypic episodes of EGFR during endocytosis. Different strategies for normalization, feature selection and classification are evaluated. The results of performance assessment clearly demonstrate that our hierarchical classification scheme combined with a selected set of features provides a notable improvement in the temporal analysis of EGFR endocytosis. Moreover, it is shown that the addition of the wavelet-based texture features contributes to this improvement. Our workflow can be applied to drug discovery to analyze defected EGFR endocytosis processes.

Research on assessment and improvement method of remote sensing image reconstruction

NASA Astrophysics Data System (ADS)

Sun, Li; Hua, Nian; Yu, Yanbo; Zhao, Zhanping

2018-01-01

Remote sensing image quality assessment and improvement is an important part of image processing. Generally, the use of compressive sampling theory in remote sensing imaging system can compress images while sampling which can improve efficiency. A method of two-dimensional principal component analysis (2DPCA) is proposed to reconstruct the remote sensing image to improve the quality of the compressed image in this paper, which contain the useful information of image and can restrain the noise. Then, remote sensing image quality influence factors are analyzed, and the evaluation parameters for quantitative evaluation are introduced. On this basis, the quality of the reconstructed images is evaluated and the different factors influence on the reconstruction is analyzed, providing meaningful referential data for enhancing the quality of remote sensing images. The experiment results show that evaluation results fit human visual feature, and the method proposed have good application value in the field of remote sensing image processing.

On-line 3-dimensional confocal imaging in vivo.

PubMed

Li, J; Jester, J V; Cavanagh, H D; Black, T D; Petroll, W M

2000-09-01

In vivo confocal microscopy through focusing (CMTF) can provide a 3-D stack of high-resolution corneal images and allows objective measurements of corneal sublayer thickness and backscattering. However, current systems require time-consuming off-line image processing and analysis on multiple software platforms. Furthermore, there is a trade off between the CMTF speed and measurement precision. The purpose of this study was to develop a novel on-line system for in vivo corneal imaging and analysis that overcomes these limitations. A tandem scanning confocal microscope (TSCM) was used for corneal imaging. The TSCM video camera was interfaced directly to a PC image acquisition board to implement real-time digitization. Software was developed to allow in vivo 2-D imaging, CMTF image acquisition, interactive 3-D reconstruction, and analysis of CMTF data to be performed on line in a single user-friendly environment. A procedure was also incorporated to separate the odd/even video fields, thereby doubling the CMTF sampling rate and theoretically improving the precision of CMTF thickness measurements by a factor of two. In vivo corneal examinations of a normal human and a photorefractive keratectomy patient are presented to demonstrate the capabilities of the new system. Improvements in the convenience, speed, and functionality of in vivo CMTF image acquisition, display, and analysis are demonstrated. This is the first full-featured software package designed for in vivo TSCM imaging of the cornea, which performs both 2-D and 3-D image acquisition, display, and processing as well as CMTF analysis. The use of a PC platform and incorporation of easy to use, on line, and interactive features should help to improve the clinical utility of this technology.

Measurements and analysis in imaging for biomedical applications

NASA Astrophysics Data System (ADS)

Hoeller, Timothy L.

2009-02-01

A Total Quality Management (TQM) approach can be used to analyze data from biomedical optical and imaging platforms of tissues. A shift from individuals to teams, partnerships, and total participation are necessary from health care groups for improved prognostics using measurement analysis. Proprietary measurement analysis software is available for calibrated, pixel-to-pixel measurements of angles and distances in digital images. Feature size, count, and color are determinable on an absolute and comparative basis. Although changes in images of histomics are based on complex and numerous factors, the variation of changes in imaging analysis to correlations of time, extent, and progression of illness can be derived. Statistical methods are preferred. Applications of the proprietary measurement software are available for any imaging platform. Quantification of results provides improved categorization of illness towards better health. As health care practitioners try to use quantified measurement data for patient diagnosis, the techniques reported can be used to track and isolate causes better. Comparisons, norms, and trends are available from processing of measurement data which is obtained easily and quickly from Scientific Software and methods. Example results for the class actions of Preventative and Corrective Care in Ophthalmology and Dermatology, respectively, are provided. Improved and quantified diagnosis can lead to better health and lower costs associated with health care. Systems support improvements towards Lean and Six Sigma affecting all branches of biology and medicine. As an example for use of statistics, the major types of variation involving a study of Bone Mineral Density (BMD) are examined. Typically, special causes in medicine relate to illness and activities; whereas, common causes are known to be associated with gender, race, size, and genetic make-up. Such a strategy of Continuous Process Improvement (CPI) involves comparison of patient results to baseline data using F-statistics. Self-parings over time are also useful. Special and common causes are identified apart from aging in applying the statistical methods. In the future, implementation of imaging measurement methods by research staff, doctors, and concerned patient partners result in improved health diagnosis, reporting, and cause determination. The long-term prospects for quantified measurements are better quality in imaging analysis with applications of higher utility for heath care providers.

Improved biliary detection and diagnosis through intelligent machine analysis.

PubMed

Logeswaran, Rajasvaran

2012-09-01

This paper reports on work undertaken to improve automated detection of bile ducts in magnetic resonance cholangiopancreatography (MRCP) images, with the objective of conducting preliminary classification of the images for diagnosis. The proposed I-BDeDIMA (Improved Biliary Detection and Diagnosis through Intelligent Machine Analysis) scheme is a multi-stage framework consisting of successive phases of image normalization, denoising, structure identification, object labeling, feature selection and disease classification. A combination of multiresolution wavelet, dynamic intensity thresholding, segment-based region growing, region elimination, statistical analysis and neural networks, is used in this framework to achieve good structure detection and preliminary diagnosis. Tests conducted on over 200 clinical images with known diagnosis have shown promising results of over 90% accuracy. The scheme outperforms related work in the literature, making it a viable framework for computer-aided diagnosis of biliary diseases. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Multiscale Image Processing of Solar Image Data

NASA Astrophysics Data System (ADS)

Young, C.; Myers, D. C.

2001-12-01

It is often said that the blessing and curse of solar physics is too much data. Solar missions such as Yohkoh, SOHO and TRACE have shown us the Sun with amazing clarity but have also increased the amount of highly complex data. We have improved our view of the Sun yet we have not improved our analysis techniques. The standard techniques used for analysis of solar images generally consist of observing the evolution of features in a sequence of byte scaled images or a sequence of byte scaled difference images. The determination of features and structures in the images are done qualitatively by the observer. There is little quantitative and objective analysis done with these images. Many advances in image processing techniques have occured in the past decade. Many of these methods are possibly suited for solar image analysis. Multiscale/Multiresolution methods are perhaps the most promising. These methods have been used to formulate the human ability to view and comprehend phenomena on different scales. So these techniques could be used to quantitify the imaging processing done by the observers eyes and brains. In this work we present several applications of multiscale techniques applied to solar image data. Specifically, we discuss uses of the wavelet, curvelet, and related transforms to define a multiresolution support for EIT, LASCO and TRACE images.

An interactive method based on the live wire for segmentation of the breast in mammography images.

PubMed

Zewei, Zhang; Tianyue, Wang; Li, Guo; Tingting, Wang; Lu, Xu

2014-01-01

In order to improve accuracy of computer-aided diagnosis of breast lumps, the authors introduce an improved interactive segmentation method based on Live Wire. This paper presents the Gabor filters and FCM clustering algorithm is introduced to the Live Wire cost function definition. According to the image FCM analysis for image edge enhancement, we eliminate the interference of weak edge and access external features clear segmentation results of breast lumps through improving Live Wire on two cases of breast segmentation data. Compared with the traditional method of image segmentation, experimental results show that the method achieves more accurate segmentation of breast lumps and provides more accurate objective basis on quantitative and qualitative analysis of breast lumps.

Improved Reconstruction of Radio Holographic Signal for Forward Scatter Radar Imaging

PubMed Central

Hu, Cheng; Liu, Changjiang; Wang, Rui; Zeng, Tao

2016-01-01

Forward scatter radar (FSR), as a specially configured bistatic radar, is provided with the capabilities of target recognition and classification by the Shadow Inverse Synthetic Aperture Radar (SISAR) imaging technology. This paper mainly discusses the reconstruction of radio holographic signal (RHS), which is an important procedure in the signal processing of FSR SISAR imaging. Based on the analysis of signal characteristics, the method for RHS reconstruction is improved in two parts: the segmental Hilbert transformation and the reconstruction of mainlobe RHS. In addition, a quantitative analysis of the method’s applicability is presented by distinguishing between the near field and far field in forward scattering. Simulation results validated the method’s advantages in improving the accuracy of RHS reconstruction and imaging. PMID:27164114

Analysis of Fringe Field Formed Inside LDA Measurement Volume Using Compact Two Hololens Imaging Systems

NASA Astrophysics Data System (ADS)

Ghosh, Abhijit; Nirala, A. K.; Yadav, H. L.

2018-03-01

We have designed and fabricated four LDA optical setups consisting of aberration compensated four different compact two hololens imaging systems. We have experimentally investigated and realized a hololens recording geometry which is interferogram of converging spherical wavefront with mutually coherent planar wavefront. Proposed real time monitoring and actual fringe field analysis techniques allow complete characterizations of fringes formed at measurement volume and permit to evaluate beam quality, alignment and fringe uniformity with greater precision. After experimentally analyzing the fringes formed at measurement volume by all four imaging systems, it is found that fringes obtained using compact two hololens imaging systems get improved both qualitatively and quantitatively compared to that obtained using conventional imaging system. Results indicate qualitative improvement of non-uniformity in fringe thickness and micro intensity variations perpendicular to the fringes, and quantitative improvement of 39.25% in overall average normalized standard deviations of fringe width formed by compact two hololens imaging systems compare to that of conventional imaging system.

SU-E-J-275: Review - Computerized PET/CT Image Analysis in the Evaluation of Tumor Response to Therapy

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

Lu, W; Wang, J; Zhang, H

Purpose: To review the literature in using computerized PET/CT image analysis for the evaluation of tumor response to therapy. Methods: We reviewed and summarized more than 100 papers that used computerized image analysis techniques for the evaluation of tumor response with PET/CT. This review mainly covered four aspects: image registration, tumor segmentation, image feature extraction, and response evaluation. Results: Although rigid image registration is straightforward, it has been shown to achieve good alignment between baseline and evaluation scans. Deformable image registration has been shown to improve the alignment when complex deformable distortions occur due to tumor shrinkage, weight loss ormore » gain, and motion. Many semi-automatic tumor segmentation methods have been developed on PET. A comparative study revealed benefits of high levels of user interaction with simultaneous visualization of CT images and PET gradients. On CT, semi-automatic methods have been developed for only tumors that show marked difference in CT attenuation between the tumor and the surrounding normal tissues. Quite a few multi-modality segmentation methods have been shown to improve accuracy compared to single-modality algorithms. Advanced PET image features considering spatial information, such as tumor volume, tumor shape, total glycolytic volume, histogram distance, and texture features have been found more informative than the traditional SUVmax for the prediction of tumor response. Advanced CT features, including volumetric, attenuation, morphologic, structure, and texture descriptors, have also been found advantage over the traditional RECIST and WHO criteria in certain tumor types. Predictive models based on machine learning technique have been constructed for correlating selected image features to response. These models showed improved performance compared to current methods using cutoff value of a single measurement for tumor response. Conclusion: This review showed that computerized PET/CT image analysis holds great potential to improve the accuracy in evaluation of tumor response. This work was supported in part by the National Cancer Institute Grant R01CA172638.« less

An approach to integrate the human vision psychology and perception knowledge into image enhancement

NASA Astrophysics Data System (ADS)

Wang, Hui; Huang, Xifeng; Ping, Jiang

2009-07-01

Image enhancement is very important image preprocessing technology especially when the image is captured in the poor imaging condition or dealing with the high bits image. The benefactor of image enhancement either may be a human observer or a computer vision process performing some kind of higher-level image analysis, such as target detection or scene understanding. One of the main objects of the image enhancement is getting a high dynamic range image and a high contrast degree image for human perception or interpretation. So, it is very necessary to integrate either empirical or statistical human vision psychology and perception knowledge into image enhancement. The human vision psychology and perception claims that humans' perception and response to the intensity fluctuation δu of visual signals are weighted by the background stimulus u, instead of being plainly uniform. There are three main laws: Weber's law, Weber- Fechner's law and Stevens's Law that describe this phenomenon in the psychology and psychophysics. This paper will integrate these three laws of the human vision psychology and perception into a very popular image enhancement algorithm named Adaptive Plateau Equalization (APE). The experiments were done on the high bits star image captured in night scene and the infrared-red image both the static image and the video stream. For the jitter problem in the video stream, this algorithm reduces this problem using the difference between the current frame's plateau value and the previous frame's plateau value to correct the current frame's plateau value. Considering the random noise impacts, the pixel value mapping process is not only depending on the current pixel but the pixels in the window surround the current pixel. The window size is usually 3×3. The process results of this improved algorithms is evaluated by the entropy analysis and visual perception analysis. The experiments' result showed the improved APE algorithms improved the quality of the image, the target and the surrounding assistant targets could be identified easily, and the noise was not amplified much. For the low quality image, these improved algorithms augment the information entropy and improve the image and the video stream aesthetic quality, while for the high quality image they will not debase the quality of the image.

A New Feature-Enhanced Speckle Reduction Method Based on Multiscale Analysis for Ultrasound B-Mode Imaging.

PubMed

Kang, Jinbum; Lee, Jae Young; Yoo, Yangmo

2016-06-01

Effective speckle reduction in ultrasound B-mode imaging is important for enhancing the image quality and improving the accuracy in image analysis and interpretation. In this paper, a new feature-enhanced speckle reduction (FESR) method based on multiscale analysis and feature enhancement filtering is proposed for ultrasound B-mode imaging. In FESR, clinical features (e.g., boundaries and borders of lesions) are selectively emphasized by edge, coherence, and contrast enhancement filtering from fine to coarse scales while simultaneously suppressing speckle development via robust diffusion filtering. In the simulation study, the proposed FESR method showed statistically significant improvements in edge preservation, mean structure similarity, speckle signal-to-noise ratio, and contrast-to-noise ratio (CNR) compared with other speckle reduction methods, e.g., oriented speckle reducing anisotropic diffusion (OSRAD), nonlinear multiscale wavelet diffusion (NMWD), the Laplacian pyramid-based nonlinear diffusion and shock filter (LPNDSF), and the Bayesian nonlocal means filter (OBNLM). Similarly, the FESR method outperformed the OSRAD, NMWD, LPNDSF, and OBNLM methods in terms of CNR, i.e., 10.70 ± 0.06 versus 9.00 ± 0.06, 9.78 ± 0.06, 8.67 ± 0.04, and 9.22 ± 0.06 in the phantom study, respectively. Reconstructed B-mode images that were developed using the five speckle reduction methods were reviewed by three radiologists for evaluation based on each radiologist's diagnostic preferences. All three radiologists showed a significant preference for the abdominal liver images obtained using the FESR methods in terms of conspicuity, margin sharpness, artificiality, and contrast, p<0.0001. For the kidney and thyroid images, the FESR method showed similar improvement over other methods. However, the FESR method did not show statistically significant improvement compared with the OBNLM method in margin sharpness for the kidney and thyroid images. These results demonstrate that the proposed FESR method can improve the image quality of ultrasound B-mode imaging by enhancing the visualization of lesion features while effectively suppressing speckle noise.

Three Dimensional Optical Coherence Tomography Imaging: Advantages and Advances

PubMed Central

Gabriele, Michelle L; Wollstein, Gadi; Ishikawa, Hiroshi; Xu, Juan; Kim, Jongsick; Kagemann, Larry; Folio, Lindsey S; Schuman, Joel S.

2010-01-01

Three dimensional (3D) ophthalmic imaging using optical coherence tomography (OCT) has revolutionized assessment of the eye, the retina in particular. Recent technological improvements have made the acquisition of 3D-OCT datasets feasible. However, while volumetric data can improve disease diagnosis and follow-up, novel image analysis techniques are now necessary in order to process the dense 3D-OCT dataset. Fundamental software improvements include methods for correcting subject eye motion, segmenting structures or volumes of interest, extracting relevant data post hoc and signal averaging to improve delineation of retinal layers. In addition, innovative methods for image display, such as C-mode sectioning, provide a unique viewing perspective and may improve interpretation of OCT images of pathologic structures. While all of these methods are being developed, most remain in an immature state. This review describes the current status of 3D-OCT scanning and interpretation, and discusses the need for standardization of clinical protocols as well as the potential benefits of 3D-OCT scanning that could come when software methods for fully exploiting these rich data sets are available clinically. The implications of new image analysis approaches include improved reproducibility of measurements garnered from 3D-OCT, which may then help improve disease discrimination and progression detection. In addition, 3D-OCT offers the potential for preoperative surgical planning and intraoperative surgical guidance. PMID:20542136

Performance characterization of image and video analysis systems at Siemens Corporate Research

NASA Astrophysics Data System (ADS)

Ramesh, Visvanathan; Jolly, Marie-Pierre; Greiffenhagen, Michael

2000-06-01

There has been a significant increase in commercial products using imaging analysis techniques to solve real-world problems in diverse fields such as manufacturing, medical imaging, document analysis, transportation and public security, etc. This has been accelerated by various factors: more advanced algorithms, the availability of cheaper sensors, and faster processors. While algorithms continue to improve in performance, a major stumbling block in translating improvements in algorithms to faster deployment of image analysis systems is the lack of characterization of limits of algorithms and how they affect total system performance. The research community has realized the need for performance analysis and there have been significant efforts in the last few years to remedy the situation. Our efforts at SCR have been on statistical modeling and characterization of modules and systems. The emphasis is on both white-box and black box methodologies to evaluate and optimize vision systems. In the first part of this paper we review the literature on performance characterization and then provide an overview of the status of research in performance characterization of image and video understanding systems. The second part of the paper is on performance evaluation of medical image segmentation algorithms. Finally, we highlight some research issues in performance analysis in medical imaging systems.

Digital Dental X-ray Database for Caries Screening

NASA Astrophysics Data System (ADS)

Rad, Abdolvahab Ehsani; Rahim, Mohd Shafry Mohd; Rehman, Amjad; Saba, Tanzila

2016-06-01

Standard database is the essential requirement to compare the performance of image analysis techniques. Hence the main issue in dental image analysis is the lack of available image database which is provided in this paper. Periapical dental X-ray images which are suitable for any analysis and approved by many dental experts are collected. This type of dental radiograph imaging is common and inexpensive, which is normally used for dental disease diagnosis and abnormalities detection. Database contains 120 various Periapical X-ray images from top to bottom jaw. Dental digital database is constructed to provide the source for researchers to use and compare the image analysis techniques and improve or manipulate the performance of each technique.

Analysis of off-axis holographic system based on improved Jamin interferometer

NASA Astrophysics Data System (ADS)

Li, Baosheng; Dong, Hang; Chen, Lijuan; Zhong, Qi

2018-02-01

In this paper, an improved Interferometer was introduced which based on traditional Jamin Interferometer to solve the twin image where appear in on-axis holographic. Adjust the angle of reference light and object light that projected onto the CCD by change the reflector of the system to separate the zero order of diffraction, the virtual image and the real image, so that could eliminate the influence of the twin image. The result of analysis shows that the system could be realized in theory. After actually building the system, the hologram of the calibration plate is reconstructed and the result is shown to be feasible.

Finite Element Analysis of Film Stack Architecture for Complementary Metal-Oxide-Semiconductor Image Sensors.

PubMed

Wu, Kuo-Tsai; Hwang, Sheng-Jye; Lee, Huei-Huang

2017-05-02

Image sensors are the core components of computer, communication, and consumer electronic products. Complementary metal oxide semiconductor (CMOS) image sensors have become the mainstay of image-sensing developments, but are prone to leakage current. In this study, we simulate the CMOS image sensor (CIS) film stacking process by finite element analysis. To elucidate the relationship between the leakage current and stack architecture, we compare the simulated and measured leakage currents in the elements. Based on the analysis results, we further improve the performance by optimizing the architecture of the film stacks or changing the thin-film material. The material parameters are then corrected to improve the accuracy of the simulation results. The simulated and experimental results confirm a positive correlation between measured leakage current and stress. This trend is attributed to the structural defects induced by high stress, which generate leakage. Using this relationship, we can change the structure of the thin-film stack to reduce the leakage current and thereby improve the component life and reliability of the CIS components.

Finite Element Analysis of Film Stack Architecture for Complementary Metal-Oxide–Semiconductor Image Sensors

PubMed Central

Wu, Kuo-Tsai; Hwang, Sheng-Jye; Lee, Huei-Huang

2017-01-01

Image sensors are the core components of computer, communication, and consumer electronic products. Complementary metal oxide semiconductor (CMOS) image sensors have become the mainstay of image-sensing developments, but are prone to leakage current. In this study, we simulate the CMOS image sensor (CIS) film stacking process by finite element analysis. To elucidate the relationship between the leakage current and stack architecture, we compare the simulated and measured leakage currents in the elements. Based on the analysis results, we further improve the performance by optimizing the architecture of the film stacks or changing the thin-film material. The material parameters are then corrected to improve the accuracy of the simulation results. The simulated and experimental results confirm a positive correlation between measured leakage current and stress. This trend is attributed to the structural defects induced by high stress, which generate leakage. Using this relationship, we can change the structure of the thin-film stack to reduce the leakage current and thereby improve the component life and reliability of the CIS components. PMID:28468324

Qualitative and quantitative interpretation of SEM image using digital image processing.

PubMed

Saladra, Dawid; Kopernik, Magdalena

2016-10-01

The aim of the this study is improvement of qualitative and quantitative analysis of scanning electron microscope micrographs by development of computer program, which enables automatic crack analysis of scanning electron microscopy (SEM) micrographs. Micromechanical tests of pneumatic ventricular assist devices result in a large number of micrographs. Therefore, the analysis must be automatic. Tests for athrombogenic titanium nitride/gold coatings deposited on polymeric substrates (Bionate II) are performed. These tests include microshear, microtension and fatigue analysis. Anisotropic surface defects observed in the SEM micrographs require support for qualitative and quantitative interpretation. Improvement of qualitative analysis of scanning electron microscope images was achieved by a set of computational tools that includes binarization, simplified expanding, expanding, simple image statistic thresholding, the filters Laplacian 1, and Laplacian 2, Otsu and reverse binarization. Several modifications of the known image processing techniques and combinations of the selected image processing techniques were applied. The introduced quantitative analysis of digital scanning electron microscope images enables computation of stereological parameters such as area, crack angle, crack length, and total crack length per unit area. This study also compares the functionality of the developed computer program of digital image processing with existing applications. The described pre- and postprocessing may be helpful in scanning electron microscopy and transmission electron microscopy surface investigations. © 2016 The Authors Journal of Microscopy © 2016 Royal Microscopical Society.

Human body region enhancement method based on Kinect infrared imaging

NASA Astrophysics Data System (ADS)

Yang, Lei; Fan, Yubo; Song, Xiaowei; Cai, Wenjing

2016-10-01

To effectively improve the low contrast of human body region in the infrared images, a combing method of several enhancement methods is utilized to enhance the human body region. Firstly, for the infrared images acquired by Kinect, in order to improve the overall contrast of the infrared images, an Optimal Contrast-Tone Mapping (OCTM) method with multi-iterations is applied to balance the contrast of low-luminosity infrared images. Secondly, to enhance the human body region better, a Level Set algorithm is employed to improve the contour edges of human body region. Finally, to further improve the human body region in infrared images, Laplacian Pyramid decomposition is adopted to enhance the contour-improved human body region. Meanwhile, the background area without human body region is processed by bilateral filtering to improve the overall effect. With theoretical analysis and experimental verification, the results show that the proposed method could effectively enhance the human body region of such infrared images.

Applications of independent component analysis in SAR images

NASA Astrophysics Data System (ADS)

Huang, Shiqi; Cai, Xinhua; Hui, Weihua; Xu, Ping

2009-07-01

The detection of faint, small and hidden targets in synthetic aperture radar (SAR) image is still an issue for automatic target recognition (ATR) system. How to effectively separate these targets from the complex background is the aim of this paper. Independent component analysis (ICA) theory can enhance SAR image targets and improve signal clutter ratio (SCR), which benefits to detect and recognize faint targets. Therefore, this paper proposes a new SAR image target detection algorithm based on ICA. In experimental process, the fast ICA (FICA) algorithm is utilized. Finally, some real SAR image data is used to test the method. The experimental results verify that the algorithm is feasible, and it can improve the SCR of SAR image and increase the detection rate for the faint small targets.

The Analysis of Image Segmentation Hierarchies with a Graph-based Knowledge Discovery System

NASA Technical Reports Server (NTRS)

Tilton, James C.; Cooke, diane J.; Ketkar, Nikhil; Aksoy, Selim

2008-01-01

Currently available pixel-based analysis techniques do not effectively extract the information content from the increasingly available high spatial resolution remotely sensed imagery data. A general consensus is that object-based image analysis (OBIA) is required to effectively analyze this type of data. OBIA is usually a two-stage process; image segmentation followed by an analysis of the segmented objects. We are exploring an approach to OBIA in which hierarchical image segmentations provided by the Recursive Hierarchical Segmentation (RHSEG) software developed at NASA GSFC are analyzed by the Subdue graph-based knowledge discovery system developed by a team at Washington State University. In this paper we discuss out initial approach to representing the RHSEG-produced hierarchical image segmentations in a graphical form understandable by Subdue, and provide results on real and simulated data. We also discuss planned improvements designed to more effectively and completely convey the hierarchical segmentation information to Subdue and to improve processing efficiency.

Potential use of combining the diffusion equation with the free Shrödinger equation to improve the Optical Coherence Tomography image analysis

NASA Astrophysics Data System (ADS)

Cabrera Fernandez, Delia; Salinas, Harry M.; Somfai, Gabor; Puliafito, Carmen A.

2006-03-01

Optical coherence tomography (OCT) is a rapidly emerging medical imaging technology. In ophthalmology, OCT is a powerful tool because it enables visualization of the cross sectional structure of the retina and anterior eye with higher resolutions than any other non-invasive imaging modality. Furthermore, OCT image information can be quantitatively analyzed, enabling objective assessment of features such as macular edema and diabetes retinopathy. We present specific improvements in the quantitative analysis of the OCT system, by combining the diffusion equation with the free Shrödinger equation. In such formulation, important features of the image can be extracted by extending the analysis from the real axis to the complex domain. Experimental results indicate that our proposed novel approach has good performance in speckle noise removal, enhancement and segmentation of the various cellular layers of the retina using the OCT system.

Using normalization 3D model for automatic clinical brain quantative analysis and evaluation

NASA Astrophysics Data System (ADS)

Lin, Hong-Dun; Yao, Wei-Jen; Hwang, Wen-Ju; Chung, Being-Tau; Lin, Kang-Ping

2003-05-01

Functional medical imaging, such as PET or SPECT, is capable of revealing physiological functions of the brain, and has been broadly used in diagnosing brain disorders by clinically quantitative analysis for many years. In routine procedures, physicians manually select desired ROIs from structural MR images and then obtain physiological information from correspondent functional PET or SPECT images. The accuracy of quantitative analysis thus relies on that of the subjectively selected ROIs. Therefore, standardizing the analysis procedure is fundamental and important in improving the analysis outcome. In this paper, we propose and evaluate a normalization procedure with a standard 3D-brain model to achieve precise quantitative analysis. In the normalization process, the mutual information registration technique was applied for realigning functional medical images to standard structural medical images. Then, the standard 3D-brain model that shows well-defined brain regions was used, replacing the manual ROIs in the objective clinical analysis. To validate the performance, twenty cases of I-123 IBZM SPECT images were used in practical clinical evaluation. The results show that the quantitative analysis outcomes obtained from this automated method are in agreement with the clinical diagnosis evaluation score with less than 3% error in average. To sum up, the method takes advantage of obtaining precise VOIs, information automatically by well-defined standard 3-D brain model, sparing manually drawn ROIs slice by slice from structural medical images in traditional procedure. That is, the method not only can provide precise analysis results, but also improve the process rate for mass medical images in clinical.

Contrast improvement of terahertz images of thin histopathologic sections

PubMed Central

Formanek, Florian; Brun, Marc-Aurèle; Yasuda, Akio

2011-01-01

We present terahertz images of 10 μm thick histopathologic sections obtained in reflection geometry with a time-domain spectrometer, and demonstrate improved contrast for sections measured in paraffin with water. Automated segmentation is applied to the complex refractive index data to generate clustered terahertz images distinguishing cancer from healthy tissues. The degree of classification of pixels is then evaluated using registered visible microscope images. Principal component analysis and propagation simulations are employed to investigate the origin and the gain of image contrast. PMID:21326635

Contrast improvement of terahertz images of thin histopathologic sections.

PubMed

Formanek, Florian; Brun, Marc-Aurèle; Yasuda, Akio

2010-12-03

We present terahertz images of 10 μm thick histopathologic sections obtained in reflection geometry with a time-domain spectrometer, and demonstrate improved contrast for sections measured in paraffin with water. Automated segmentation is applied to the complex refractive index data to generate clustered terahertz images distinguishing cancer from healthy tissues. The degree of classification of pixels is then evaluated using registered visible microscope images. Principal component analysis and propagation simulations are employed to investigate the origin and the gain of image contrast.

Image encryption based on a delayed fractional-order chaotic logistic system

NASA Astrophysics Data System (ADS)

Wang, Zhen; Huang, Xia; Li, Ning; Song, Xiao-Na

2012-05-01

A new image encryption scheme is proposed based on a delayed fractional-order chaotic logistic system. In the process of generating a key stream, the time-varying delay and fractional derivative are embedded in the proposed scheme to improve the security. Such a scheme is described in detail with security analyses including correlation analysis, information entropy analysis, run statistic analysis, mean-variance gray value analysis, and key sensitivity analysis. Experimental results show that the newly proposed image encryption scheme possesses high security.

Improved classification accuracy of powdery mildew infection levels of wine grapes by spatial-spectral analysis of hyperspectral images.

PubMed

Knauer, Uwe; Matros, Andrea; Petrovic, Tijana; Zanker, Timothy; Scott, Eileen S; Seiffert, Udo

2017-01-01

Hyperspectral imaging is an emerging means of assessing plant vitality, stress parameters, nutrition status, and diseases. Extraction of target values from the high-dimensional datasets either relies on pixel-wise processing of the full spectral information, appropriate selection of individual bands, or calculation of spectral indices. Limitations of such approaches are reduced classification accuracy, reduced robustness due to spatial variation of the spectral information across the surface of the objects measured as well as a loss of information intrinsic to band selection and use of spectral indices. In this paper we present an improved spatial-spectral segmentation approach for the analysis of hyperspectral imaging data and its application for the prediction of powdery mildew infection levels (disease severity) of intact Chardonnay grape bunches shortly before veraison. Instead of calculating texture features (spatial features) for the huge number of spectral bands independently, dimensionality reduction by means of Linear Discriminant Analysis (LDA) was applied first to derive a few descriptive image bands. Subsequent classification was based on modified Random Forest classifiers and selective extraction of texture parameters from the integral image representation of the image bands generated. Dimensionality reduction, integral images, and the selective feature extraction led to improved classification accuracies of up to [Formula: see text] for detached berries used as a reference sample (training dataset). Our approach was validated by predicting infection levels for a sample of 30 intact bunches. Classification accuracy improved with the number of decision trees of the Random Forest classifier. These results corresponded with qPCR results. An accuracy of 0.87 was achieved in classification of healthy, infected, and severely diseased bunches. However, discrimination between visually healthy and infected bunches proved to be challenging for a few samples, perhaps due to colonized berries or sparse mycelia hidden within the bunch or airborne conidia on the berries that were detected by qPCR. An advanced approach to hyperspectral image classification based on combined spatial and spectral image features, potentially applicable to many available hyperspectral sensor technologies, has been developed and validated to improve the detection of powdery mildew infection levels of Chardonnay grape bunches. The spatial-spectral approach improved especially the detection of light infection levels compared with pixel-wise spectral data analysis. This approach is expected to improve the speed and accuracy of disease detection once the thresholds for fungal biomass detected by hyperspectral imaging are established; it can also facilitate monitoring in plant phenotyping of grapevine and additional crops.

Improved volumetric measurement of brain structure with a distortion correction procedure using an ADNI phantom.

PubMed

Maikusa, Norihide; Yamashita, Fumio; Tanaka, Kenichiro; Abe, Osamu; Kawaguchi, Atsushi; Kabasawa, Hiroyuki; Chiba, Shoma; Kasahara, Akihiro; Kobayashi, Nobuhisa; Yuasa, Tetsuya; Sato, Noriko; Matsuda, Hiroshi; Iwatsubo, Takeshi

2013-06-01

Serial magnetic resonance imaging (MRI) images acquired from multisite and multivendor MRI scanners are widely used in measuring longitudinal structural changes in the brain. Precise and accurate measurements are important in understanding the natural progression of neurodegenerative disorders such as Alzheimer's disease. However, geometric distortions in MRI images decrease the accuracy and precision of volumetric or morphometric measurements. To solve this problem, the authors suggest a commercially available phantom-based distortion correction method that accommodates the variation in geometric distortion within MRI images obtained with multivendor MRI scanners. The authors' method is based on image warping using a polynomial function. The method detects fiducial points within a phantom image using phantom analysis software developed by the Mayo Clinic and calculates warping functions for distortion correction. To quantify the effectiveness of the authors' method, the authors corrected phantom images obtained from multivendor MRI scanners and calculated the root-mean-square (RMS) of fiducial errors and the circularity ratio as evaluation values. The authors also compared the performance of the authors' method with that of a distortion correction method based on a spherical harmonics description of the generic gradient design parameters. Moreover, the authors evaluated whether this correction improves the test-retest reproducibility of voxel-based morphometry in human studies. A Wilcoxon signed-rank test with uncorrected and corrected images was performed. The root-mean-square errors and circularity ratios for all slices significantly improved (p < 0.0001) after the authors' distortion correction. Additionally, the authors' method was significantly better than a distortion correction method based on a description of spherical harmonics in improving the distortion of root-mean-square errors (p < 0.001 and 0.0337, respectively). Moreover, the authors' method reduced the RMS error arising from gradient nonlinearity more than gradwarp methods. In human studies, the coefficient of variation of voxel-based morphometry analysis of the whole brain improved significantly from 3.46% to 2.70% after distortion correction of the whole gray matter using the authors' method (Wilcoxon signed-rank test, p < 0.05). The authors proposed a phantom-based distortion correction method to improve reproducibility in longitudinal structural brain analysis using multivendor MRI. The authors evaluated the authors' method for phantom images in terms of two geometrical values and for human images in terms of test-retest reproducibility. The results showed that distortion was corrected significantly using the authors' method. In human studies, the reproducibility of voxel-based morphometry analysis for the whole gray matter significantly improved after distortion correction using the authors' method.

Rock classification based on resistivity patterns in electrical borehole wall images

NASA Astrophysics Data System (ADS)

Linek, Margarete; Jungmann, Matthias; Berlage, Thomas; Pechnig, Renate; Clauser, Christoph

2007-06-01

Electrical borehole wall images represent grey-level-coded micro-resistivity measurements at the borehole wall. Different scientific methods have been implemented to transform image data into quantitative log curves. We introduce a pattern recognition technique applying texture analysis, which uses second-order statistics based on studying the occurrence of pixel pairs. We calculate so-called Haralick texture features such as contrast, energy, entropy and homogeneity. The supervised classification method is used for assigning characteristic texture features to different rock classes and assessing the discriminative power of these image features. We use classifiers obtained from training intervals to characterize the entire image data set recovered in ODP hole 1203A. This yields a synthetic lithology profile based on computed texture data. We show that Haralick features accurately classify 89.9% of the training intervals. We obtained misclassification for vesicular basaltic rocks. Hence, further image analysis tools are used to improve the classification reliability. We decompose the 2D image signal by the application of wavelet transformation in order to enhance image objects horizontally, diagonally and vertically. The resulting filtered images are used for further texture analysis. This combined classification based on Haralick features and wavelet transformation improved our classification up to a level of 98%. The application of wavelet transformation increases the consistency between standard logging profiles and texture-derived lithology. Texture analysis of borehole wall images offers the potential to facilitate objective analysis of multiple boreholes with the same lithology.

Face sketch recognition based on edge enhancement via deep learning

NASA Astrophysics Data System (ADS)

Xie, Zhenzhu; Yang, Fumeng; Zhang, Yuming; Wu, Congzhong

2017-11-01

In this paper,we address the face sketch recognition problem. Firstly, we utilize the eigenface algorithm to convert a sketch image into a synthesized sketch face image. Subsequently, considering the low-level vision problem in synthesized face sketch image .Super resolution reconstruction algorithm based on CNN(convolutional neural network) is employed to improve the visual effect. To be specific, we uses a lightweight super-resolution structure to learn a residual mapping instead of directly mapping the feature maps from the low-level space to high-level patch representations, which making the networks are easier to optimize and have lower computational complexity. Finally, we adopt LDA(Linear Discriminant Analysis) algorithm to realize face sketch recognition on synthesized face image before super resolution and after respectively. Extensive experiments on the face sketch database(CUFS) from CUHK demonstrate that the recognition rate of SVM(Support Vector Machine) algorithm improves from 65% to 69% and the recognition rate of LDA(Linear Discriminant Analysis) algorithm improves from 69% to 75%.What'more,the synthesized face image after super resolution can not only better describer image details such as hair ,nose and mouth etc, but also improve the recognition accuracy effectively.

[Utility of axial images in an early Alzheimer disease diagnosis support system (VSRAD)].

PubMed

Goto, Masami; Aoki, Shigeki; Abe, Osamu; Masumoto, Tomohiko; Watanabe, Yasushi; Satake, Yoshiroh; Nishida, Katsuji; Ino, Kenji; Yano, Keiichi; Iida, Kyohhito; Mima, Kazuo; Ohtomo, Kuni

2006-09-20

In recent years, voxel-based morphometry (VBM) has become a popular tool for the early diagnosis of Alzheimer disease. The Voxel-Based Specific Regional Analysis System for Alzheimer's Disease (VSRAD), a VBM system that uses MRI, has been reported to be clinically useful. The able-bodied person database (DB) of VSRAD, which employs sagittal plane imaging, is not suitable for analysis by axial plane imaging. However, axial plane imaging is useful for avoiding motion artifacts from the eyeball. Therefore, we created an able-bodied person DB by axial plane imaging and examined its utility. We also analyzed groups of able-bodied persons and persons with dementia by axial plane imaging and reviewed the validity. After using the DB of axial plane imaging, the Z-score of the intrahippocampal region improved by 8 in 13 instances. In all brains, the Z-score improved by 13 in all instances.

Intrasubject multimodal groupwise registration with the conditional template entropy.

PubMed

Polfliet, Mathias; Klein, Stefan; Huizinga, Wyke; Paulides, Margarethus M; Niessen, Wiro J; Vandemeulebroucke, Jef

2018-05-01

Image registration is an important task in medical image analysis. Whereas most methods are designed for the registration of two images (pairwise registration), there is an increasing interest in simultaneously aligning more than two images using groupwise registration. Multimodal registration in a groupwise setting remains difficult, due to the lack of generally applicable similarity metrics. In this work, a novel similarity metric for such groupwise registration problems is proposed. The metric calculates the sum of the conditional entropy between each image in the group and a representative template image constructed iteratively using principal component analysis. The proposed metric is validated in extensive experiments on synthetic and intrasubject clinical image data. These experiments showed equivalent or improved registration accuracy compared to other state-of-the-art (dis)similarity metrics and improved transformation consistency compared to pairwise mutual information. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Analysis of objects in binary images. M.S. Thesis - Old Dominion Univ.

NASA Technical Reports Server (NTRS)

Leonard, Desiree M.

1991-01-01

Digital image processing techniques are typically used to produce improved digital images through the application of successive enhancement techniques to a given image or to generate quantitative data about the objects within that image. In support of and to assist researchers in a wide range of disciplines, e.g., interferometry, heavy rain effects on aerodynamics, and structure recognition research, it is often desirable to count objects in an image and compute their geometric properties. Therefore, an image analysis application package, focusing on a subset of image analysis techniques used for object recognition in binary images, was developed. This report describes the techniques and algorithms utilized in three main phases of the application and are categorized as: image segmentation, object recognition, and quantitative analysis. Appendices provide supplemental formulas for the algorithms employed as well as examples and results from the various image segmentation techniques and the object recognition algorithm implemented.

An Analysis of Web Image Queries for Search.

ERIC Educational Resources Information Center

Pu, Hsiao-Tieh

2003-01-01

Examines the differences between Web image and textual queries, and attempts to develop an analytic model to investigate their implications for Web image retrieval systems. Provides results that give insight into Web image searching behavior and suggests implications for improvement of current Web image search engines. (AEF)

Improving high resolution retinal image quality using speckle illumination HiLo imaging

PubMed Central

Zhou, Xiaolin; Bedggood, Phillip; Metha, Andrew

2014-01-01

Retinal image quality from flood illumination adaptive optics (AO) ophthalmoscopes is adversely affected by out-of-focus light scatter due to the lack of confocality. This effect is more pronounced in small eyes, such as that of rodents, because the requisite high optical power confers a large dioptric thickness to the retina. A recently-developed structured illumination microscopy (SIM) technique called HiLo imaging has been shown to reduce the effect of out-of-focus light scatter in flood illumination microscopes and produce pseudo-confocal images with significantly improved image quality. In this work, we adopted the HiLo technique to a flood AO ophthalmoscope and performed AO imaging in both (physical) model and live rat eyes. The improvement in image quality from HiLo imaging is shown both qualitatively and quantitatively by using spatial spectral analysis. PMID:25136486

Improving high resolution retinal image quality using speckle illumination HiLo imaging.

PubMed

Zhou, Xiaolin; Bedggood, Phillip; Metha, Andrew

2014-08-01

Retinal image quality from flood illumination adaptive optics (AO) ophthalmoscopes is adversely affected by out-of-focus light scatter due to the lack of confocality. This effect is more pronounced in small eyes, such as that of rodents, because the requisite high optical power confers a large dioptric thickness to the retina. A recently-developed structured illumination microscopy (SIM) technique called HiLo imaging has been shown to reduce the effect of out-of-focus light scatter in flood illumination microscopes and produce pseudo-confocal images with significantly improved image quality. In this work, we adopted the HiLo technique to a flood AO ophthalmoscope and performed AO imaging in both (physical) model and live rat eyes. The improvement in image quality from HiLo imaging is shown both qualitatively and quantitatively by using spatial spectral analysis.

Automated removal of spurious intermediate cerebral blood flow volumes improves image quality among older patients: A clinical arterial spin labeling investigation.

PubMed

Shirzadi, Zahra; Crane, David E; Robertson, Andrew D; Maralani, Pejman J; Aviv, Richard I; Chappell, Michael A; Goldstein, Benjamin I; Black, Sandra E; MacIntosh, Bradley J

2015-11-01

To evaluate the impact of rejecting intermediate cerebral blood flow (CBF) images that are adversely affected by head motion during an arterial spin labeling (ASL) acquisition. Eighty participants were recruited, representing a wide age range (14-90 years) and heterogeneous cerebrovascular health conditions including bipolar disorder, chronic stroke, and moderate to severe white matter hyperintensities of presumed vascular origin. Pseudocontinuous ASL and T1 -weigthed anatomical images were acquired on a 3T scanner. ASL intermediate CBF images were included based on their contribution to the mean estimate, with the goal to maximize CBF detectability in gray matter (GM). Simulations were conducted to evaluate the performance of the proposed optimization procedure relative to other ASL postprocessing approaches. Clinical CBF images were also assessed visually by two experienced neuroradiologists. Optimized CBF images (CBFopt ) had significantly greater agreement with a synthetic ground truth CBF image and greater CBF detectability relative to the other ASL analysis methods (P < 0.05). Moreover, empirical CBFopt images showed a significantly improved signal-to-noise ratio relative to CBF images obtained from other postprocessing approaches (mean: 12.6%; range 1% to 56%; P < 0.001), and this improvement was age-dependent (P = 0.03). Differences between CBF images from different analysis procedures were not perceptible by visual inspection, while there was a moderate agreement between the ratings (κ = 0.44, P < 0.001). This study developed an automated head motion threshold-free procedure to improve the detection of CBF in GM. The improvement in CBF image quality was larger when considering older participants. © 2015 Wiley Periodicals, Inc.

Improvement of automatic hemorrhage detection methods using brightness correction on fundus images

NASA Astrophysics Data System (ADS)

Hatanaka, Yuji; Nakagawa, Toshiaki; Hayashi, Yoshinori; Kakogawa, Masakatsu; Sawada, Akira; Kawase, Kazuhide; Hara, Takeshi; Fujita, Hiroshi

2008-03-01

We have been developing several automated methods for detecting abnormalities in fundus images. The purpose of this study is to improve our automated hemorrhage detection method to help diagnose diabetic retinopathy. We propose a new method for preprocessing and false positive elimination in the present study. The brightness of the fundus image was changed by the nonlinear curve with brightness values of the hue saturation value (HSV) space. In order to emphasize brown regions, gamma correction was performed on each red, green, and blue-bit image. Subsequently, the histograms of each red, blue, and blue-bit image were extended. After that, the hemorrhage candidates were detected. The brown regions indicated hemorrhages and blood vessels and their candidates were detected using density analysis. We removed the large candidates such as blood vessels. Finally, false positives were removed by using a 45-feature analysis. To evaluate the new method for the detection of hemorrhages, we examined 125 fundus images, including 35 images with hemorrhages and 90 normal images. The sensitivity and specificity for the detection of abnormal cases was were 80% and 88%, respectively. These results indicate that the new method may effectively improve the performance of our computer-aided diagnosis system for hemorrhages.

Study on polarization image methods in turbid medium

NASA Astrophysics Data System (ADS)

Fu, Qiang; Mo, Chunhe; Liu, Boyu; Duan, Jin; Zhang, Su; Zhu, Yong

2014-11-01

Polarization imaging detection technology in addition to the traditional imaging information, also can get polarization multi-dimensional information, thus improve the probability of target detection and recognition.Image fusion in turbid medium target polarization image research, is helpful to obtain high quality images. Based on visible light wavelength of light wavelength of laser polarization imaging, through the rotation Angle of polaroid get corresponding linear polarized light intensity, respectively to obtain the concentration range from 5% to 10% of turbid medium target stocks of polarization parameters, introduces the processing of image fusion technology, main research on access to the polarization of the image by using different polarization image fusion methods for image processing, discusses several kinds of turbid medium has superior performance of polarization image fusion method, and gives the treatment effect and analysis of data tables. Then use pixel level, feature level and decision level fusion algorithm on three levels of information fusion, DOLP polarization image fusion, the results show that: with the increase of the polarization Angle, polarization image will be more and more fuzzy, quality worse and worse. Than a single fused image contrast of the image be improved obviously, the finally analysis on reasons of the increase the image contrast and polarized light.

Key Issues in the Analysis of Remote Sensing Data: A report on the workshop

NASA Technical Reports Server (NTRS)

Swain, P. H. (Principal Investigator)

1981-01-01

The procedures of a workshop assessing the state of the art of machine analysis of remotely sensed data are summarized. Areas discussed were: data bases, image registration, image preprocessing operations, map oriented considerations, advanced digital systems, artificial intelligence methods, image classification, and improved classifier training. Recommendations of areas for further research are presented.

Tolerance of image enhancement brightness and contrast in lateral cephalometric digital radiography for Steiner analysis

NASA Astrophysics Data System (ADS)

Rianti, R. A.; Priaminiarti, M.; Syahraini, S. I.

2017-08-01

Image enhancement brightness and contrast can be adjusted on lateral cephalometric digital radiographs to improve image quality and anatomic landmarks for measurement by Steiner analysis. To determine the limit value for adjustments of image enhancement brightness and contrast in lateral cephalometric digital radiography for Steiner analysis. Image enhancement brightness and contrast were adjusted on 100 lateral cephalometric radiography in 10-point increments (-30, -20, -10, 0, +10, +20, +30). Steiner analysis measurements were then performed by two observers. Reliabilities were tested by the Interclass Correlation Coefficient (ICC) and significance tested by ANOVA or the Kruskal Wallis test. No significant differences were detected in lateral cephalometric analysis measurements following adjustment of the image enhancement brightness and contrast. The limit value of adjustments of the image enhancement brightness and contrast associated with incremental 10-point changes (-30, -20, -10, 0, +10, +20, +30) does not affect the results of Steiner analysis.

Cryo-imaging of fluorescently labeled single cells in a mouse

NASA Astrophysics Data System (ADS)

Steyer, Grant J.; Roy, Debashish; Salvado, Olivier; Stone, Meredith E.; Wilson, David L.

2009-02-01

We developed a cryo-imaging system to provide single-cell detection of fluorescently labeled cells in mouse, with particular applicability to stem cells and metastatic cancer. The Case cryoimaging system consists of a fluorescence microscope, robotic imaging positioner, customized cryostat, PC-based control system, and visualization/analysis software. The system alternates between sectioning (10-40 μm) and imaging, collecting color brightfield and fluorescent blockface image volumes >60GB. In mouse experiments, we imaged quantum-dot labeled stem cells, GFP-labeled cancer and stem cells, and cell-size fluorescent microspheres. To remove subsurface fluorescence, we used a simplified model of light-tissue interaction whereby the next image was scaled, blurred, and subtracted from the current image. We estimated scaling and blurring parameters by minimizing entropy of subtracted images. Tissue specific attenuation parameters were found [uT : heart (267 +/- 47.6 μm), liver (218 +/- 27.1 μm), brain (161 +/- 27.4 μm)] to be within the range of estimates in the literature. "Next image" processing removed subsurface fluorescence equally well across multiple tissues (brain, kidney, liver, adipose tissue, etc.), and analysis of 200 microsphere images in the brain gave 97+/-2% reduction of subsurface fluorescence. Fluorescent signals were determined to arise from single cells based upon geometric and integrated intensity measurements. Next image processing greatly improved axial resolution, enabled high quality 3D volume renderings, and improved enumeration of single cells with connected component analysis by up to 24%. Analysis of image volumes identified metastatic cancer sites, found homing of stem cells to injury sites, and showed microsphere distribution correlated with blood flow patterns. We developed and evaluated cryo-imaging to provide single-cell detection of fluorescently labeled cells in mouse. Our cryo-imaging system provides extreme (>60GB), micron-scale, fluorescence, and bright field image data. Here we describe our image preprocessing, analysis, and visualization techniques. Processing improves axial resolution, reduces subsurface fluorescence by 97%, and enables single cell detection and counting. High quality 3D volume renderings enable us to evaluate cell distribution patterns. Applications include the myriad of biomedical experiments using fluorescent reporter gene and exogenous fluorophore labeling of cells in applications such as stem cell regenerative medicine, cancer, tissue engineering, etc.

Remote sensing image ship target detection method based on visual attention model

NASA Astrophysics Data System (ADS)

Sun, Yuejiao; Lei, Wuhu; Ren, Xiaodong

2017-11-01

The traditional methods of detecting ship targets in remote sensing images mostly use sliding window to search the whole image comprehensively. However, the target usually occupies only a small fraction of the image. This method has high computational complexity for large format visible image data. The bottom-up selective attention mechanism can selectively allocate computing resources according to visual stimuli, thus improving the computational efficiency and reducing the difficulty of analysis. Considering of that, a method of ship target detection in remote sensing images based on visual attention model was proposed in this paper. The experimental results show that the proposed method can reduce the computational complexity while improving the detection accuracy, and improve the detection efficiency of ship targets in remote sensing images.

Computerized PET/CT image analysis in the evaluation of tumour response to therapy

PubMed Central

Wang, J; Zhang, H H

2015-01-01

Current cancer therapy strategy is mostly population based, however, there are large differences in tumour response among patients. It is therefore important for treating physicians to know individual tumour response. In recent years, many studies proposed the use of computerized positron emission tomography/CT image analysis in the evaluation of tumour response. Results showed that computerized analysis overcame some major limitations of current qualitative and semiquantitative analysis and led to improved accuracy. In this review, we summarize these studies in four steps of the analysis: image registration, tumour segmentation, image feature extraction and response evaluation. Future works are proposed and challenges described. PMID:25723599

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.

Improved Cloud Detection Utilizing Defense Meteorological Satellite Program near Infrared Measurements

DTIC Science & Technology

1982-01-27

Visible 3. 3 Ea r th Location, Colocation, and Normalization 4. IMAGE ANALYSIS 4. 1 Interactive Capabilities 4.2 Examples 5. AUTOMATED CLOUD...computer Interactive Data Access System (McIDAS) before image analysis and algorithm development were done. Earth-location is an automated procedure to...the factor l / s in (SSE) toward the gain settings given in Table 5. 4. IMAGE ANALYSIS 4.1 Interactive Capabilities The development of automated

Ultrasonic image analysis and image-guided interventions.

PubMed

Noble, J Alison; Navab, Nassir; Becher, H

2011-08-06

The fields of medical image analysis and computer-aided interventions deal with reducing the large volume of digital images (X-ray, computed tomography, magnetic resonance imaging (MRI), positron emission tomography and ultrasound (US)) to more meaningful clinical information using software algorithms. US is a core imaging modality employed in these areas, both in its own right and used in conjunction with the other imaging modalities. It is receiving increased interest owing to the recent introduction of three-dimensional US, significant improvements in US image quality, and better understanding of how to design algorithms which exploit the unique strengths and properties of this real-time imaging modality. This article reviews the current state of art in US image analysis and its application in image-guided interventions. The article concludes by giving a perspective from clinical cardiology which is one of the most advanced areas of clinical application of US image analysis and describing some probable future trends in this important area of ultrasonic imaging research.

Image Analysis in Plant Sciences: Publish Then Perish.

PubMed

Lobet, Guillaume

2017-07-01

Image analysis has become a powerful technique for most plant scientists. In recent years dozens of image analysis tools have been published in plant science journals. These tools cover the full spectrum of plant scales, from single cells to organs and canopies. However, the field of plant image analysis remains in its infancy. It still has to overcome important challenges, such as the lack of robust validation practices or the absence of long-term support. In this Opinion article, I: (i) present the current state of the field, based on data from the plant-image-analysis.org database; (ii) identify the challenges faced by its community; and (iii) propose workable ways of improvement. Copyright © 2017 Elsevier Ltd. All rights reserved.

Applications of High-speed motion analysis system on Solid Rocket Motor (SRM)

NASA Astrophysics Data System (ADS)

Liu, Yang; He, Guo-qiang; Li, Jiang; Liu, Pei-jin; Chen, Jian

2007-01-01

High-speed motion analysis system could record images up to 12,000fps and analyzed with the image processing system. The system stored data and images directly in electronic memory convenient for managing and analyzing. The high-speed motion analysis system and the X-ray radiography system were established the high-speed real-time X-ray radiography system, which could diagnose and measure the dynamic and high-speed process in opaque. The image processing software was developed for improve quality of the original image for acquiring more precise information. The typical applications of high-speed motion analysis system on solid rocket motor (SRM) were introduced in the paper. The research of anomalous combustion of solid propellant grain with defects, real-time measurement experiment of insulator eroding, explosion incision process of motor, structure and wave character of plume during the process of ignition and flameout, measurement of end burning of solid propellant, measurement of flame front and compatibility between airplane and missile during the missile launching were carried out using high-speed motion analysis system. The significative results were achieved through the research. Aim at application of high-speed motion analysis system on solid rocket motor, the key problem, such as motor vibrancy, electrical source instability, geometry aberrance, and yawp disturbance, which damaged the image quality, was solved. The image processing software was developed which improved the capability of measuring the characteristic of image. The experimental results showed that the system was a powerful facility to study instantaneous and high-speed process in solid rocket motor. With the development of the image processing technique, the capability of high-speed motion analysis system was enhanced.

Review of automatic detection of pig behaviours by using image analysis

NASA Astrophysics Data System (ADS)

Han, Shuqing; Zhang, Jianhua; Zhu, Mengshuai; Wu, Jianzhai; Kong, Fantao

2017-06-01

Automatic detection of lying, moving, feeding, drinking, and aggressive behaviours of pigs by means of image analysis can save observation input by staff. It would help staff make early detection of diseases or injuries of pigs during breeding and improve management efficiency of swine industry. This study describes the progress of pig behaviour detection based on image analysis and advancement in image segmentation of pig body, segmentation of pig adhesion and extraction of pig behaviour characteristic parameters. Challenges for achieving automatic detection of pig behaviours were summarized.

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

NASA Astrophysics Data System (ADS)

Venkataraman, Sankar; Li, Wenjing

2008-03-01

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

An Improved Method for Measuring Quantitative Resistance to the Wheat Pathogen Zymoseptoria tritici Using High-Throughput Automated Image Analysis.

PubMed

Stewart, Ethan L; Hagerty, Christina H; Mikaberidze, Alexey; Mundt, Christopher C; Zhong, Ziming; McDonald, Bruce A

2016-07-01

Zymoseptoria tritici causes Septoria tritici blotch (STB) on wheat. An improved method of quantifying STB symptoms was developed based on automated analysis of diseased leaf images made using a flatbed scanner. Naturally infected leaves (n = 949) sampled from fungicide-treated field plots comprising 39 wheat cultivars grown in Switzerland and 9 recombinant inbred lines (RIL) grown in Oregon were included in these analyses. Measures of quantitative resistance were percent leaf area covered by lesions, pycnidia size and gray value, and pycnidia density per leaf and lesion. These measures were obtained automatically with a batch-processing macro utilizing the image-processing software ImageJ. All phenotypes in both locations showed a continuous distribution, as expected for a quantitative trait. The trait distributions at both sites were largely overlapping even though the field and host environments were quite different. Cultivars and RILs could be assigned to two or more statistically different groups for each measured phenotype. Traditional visual assessments of field resistance were highly correlated with quantitative resistance measures based on image analysis for the Oregon RILs. These results show that automated image analysis provides a promising tool for assessing quantitative resistance to Z. tritici under field conditions.

OIPAV: an integrated software system for ophthalmic image processing, analysis and visualization

NASA Astrophysics Data System (ADS)

Zhang, Lichun; Xiang, Dehui; Jin, Chao; Shi, Fei; Yu, Kai; Chen, Xinjian

2018-03-01

OIPAV (Ophthalmic Images Processing, Analysis and Visualization) is a cross-platform software which is specially oriented to ophthalmic images. It provides a wide range of functionalities including data I/O, image processing, interaction, ophthalmic diseases detection, data analysis and visualization to help researchers and clinicians deal with various ophthalmic images such as optical coherence tomography (OCT) images and color photo of fundus, etc. It enables users to easily access to different ophthalmic image data manufactured from different imaging devices, facilitate workflows of processing ophthalmic images and improve quantitative evaluations. In this paper, we will present the system design and functional modules of the platform and demonstrate various applications. With a satisfying function scalability and expandability, we believe that the software can be widely applied in ophthalmology field.

Interpolation of longitudinal shape and image data via optimal mass transport

NASA Astrophysics Data System (ADS)

Gao, Yi; Zhu, Liang-Jia; Bouix, Sylvain; Tannenbaum, Allen

2014-03-01

Longitudinal analysis of medical imaging data has become central to the study of many disorders. Unfortunately, various constraints (study design, patient availability, technological limitations) restrict the acquisition of data to only a few time points, limiting the study of continuous disease/treatment progression. Having the ability to produce a sensible time interpolation of the data can lead to improved analysis, such as intuitive visualizations of anatomical changes, or the creation of more samples to improve statistical analysis. In this work, we model interpolation of medical image data, in particular shape data, using the theory of optimal mass transport (OMT), which can construct a continuous transition from two time points while preserving "mass" (e.g., image intensity, shape volume) during the transition. The theory even allows a short extrapolation in time and may help predict short-term treatment impact or disease progression on anatomical structure. We apply the proposed method to the hippocampus-amygdala complex in schizophrenia, the heart in atrial fibrillation, and full head MR images in traumatic brain injury.

An image analysis system for near-infrared (NIR) fluorescence lymph imaging

NASA Astrophysics Data System (ADS)

Zhang, Jingdan; Zhou, Shaohua Kevin; Xiang, Xiaoyan; Rasmussen, John C.; Sevick-Muraca, Eva M.

2011-03-01

Quantitative analysis of lymphatic function is crucial for understanding the lymphatic system and diagnosing the associated diseases. Recently, a near-infrared (NIR) fluorescence imaging system is developed for real-time imaging lymphatic propulsion by intradermal injection of microdose of a NIR fluorophore distal to the lymphatics of interest. However, the previous analysis software3, 4 is underdeveloped, requiring extensive time and effort to analyze a NIR image sequence. In this paper, we develop a number of image processing techniques to automate the data analysis workflow, including an object tracking algorithm to stabilize the subject and remove the motion artifacts, an image representation named flow map to characterize lymphatic flow more reliably, and an automatic algorithm to compute lymph velocity and frequency of propulsion. By integrating all these techniques to a system, the analysis workflow significantly reduces the amount of required user interaction and improves the reliability of the measurement.

Noise Gating Solar Images

NASA Astrophysics Data System (ADS)

DeForest, Craig; Seaton, Daniel B.; Darnell, John A.

2017-08-01

I present and demonstrate a new, general purpose post-processing technique, "3D noise gating", that can reduce image noise by an order of magnitude or more without effective loss of spatial or temporal resolution in typical solar applications.Nearly all scientific images are, ultimately, limited by noise. Noise can be direct Poisson "shot noise" from photon counting effects, or introduced by other means such as detector read noise. Noise is typically represented as a random variable (perhaps with location- or image-dependent characteristics) that is sampled once per pixel or once per resolution element of an image sequence. Noise limits many aspects of image analysis, including photometry, spatiotemporal resolution, feature identification, morphology extraction, and background modeling and separation.Identifying and separating noise from image signal is difficult. The common practice of blurring in space and/or time works because most image "signal" is concentrated in the low Fourier components of an image, while noise is evenly distributed. Blurring in space and/or time attenuates the high spatial and temporal frequencies, reducing noise at the expense of also attenuating image detail. Noise-gating exploits the same property -- "coherence" -- that we use to identify features in images, to separate image features from noise.Processing image sequences through 3-D noise gating results in spectacular (more than 10x) improvements in signal-to-noise ratio, while not blurring bright, resolved features in either space or time. This improves most types of image analysis, including feature identification, time sequence extraction, absolute and relative photometry (including differential emission measure analysis), feature tracking, computer vision, correlation tracking, background modeling, cross-scale analysis, visual display/presentation, and image compression.I will introduce noise gating, describe the method, and show examples from several instruments (including SDO/AIA , SDO/HMI, STEREO/SECCHI, and GOES-R/SUVI) that explore the benefits and limits of the technique.

Roles of universal three-dimensional image analysis devices that assist surgical operations.

PubMed

Sakamoto, Tsuyoshi

2014-04-01

The circumstances surrounding medical image analysis have undergone rapid evolution. In such a situation, it can be said that "imaging" obtained through medical imaging modality and the "analysis" that we employ have become amalgamated. Recently, we feel the distance between "imaging" and "analysis" has become closer regarding the imaging analysis of any organ system, as if both terms mentioned above have become integrated. The history of medical image analysis started with the appearance of the computer. The invention of multi-planar reconstruction (MPR) used in the helical scan had a significant impact and became the basis for recent image analysis. Subsequently, curbed MPR (CPR) and other methods were developed, and the 3D diagnostic imaging and image analysis of the human body have started on a full scale. Volume rendering: the development of a new rendering algorithm and the significant improvement of memory and CPUs contributed to the development of "volume rendering," which allows 3D views with retained internal information. A new value was created by this development; computed tomography (CT) images that used to be for "diagnosis" before that time have become "applicable to treatment." In the past, before the development of volume rendering, a clinician had to mentally reconstruct an image reconfigured for diagnosis into a 3D image, but these developments have allowed the depiction of a 3D image on a monitor. Current technology: Currently, in Japan, the estimation of the liver volume and the perfusion area of the portal vein and hepatic vein are vigorously being adopted during preoperative planning for hepatectomy. Such a circumstance seems to be brought by the substantial improvement of said basic techniques and by upgrading the user interface, allowing doctors easy manipulation by themselves. The following describes the specific techniques. Future of post-processing technology: It is expected, in terms of the role of image analysis, for better or worse, that computer-aided diagnosis (CAD) will develop to a highly advanced level in every diagnostic field. Further, it is also expected in the treatment field that a technique coordinating various devices will be strongly required as a surgery navigator. Actually, surgery using an image navigator is being widely studied, and coordination with hardware, including robots, will also be developed. © 2014 Japanese Society of Hepato-Biliary-Pancreatic Surgery.

Advanced Cell Classifier: User-Friendly Machine-Learning-Based Software for Discovering Phenotypes in High-Content Imaging Data.

PubMed

Piccinini, Filippo; Balassa, Tamas; Szkalisity, Abel; Molnar, Csaba; Paavolainen, Lassi; Kujala, Kaisa; Buzas, Krisztina; Sarazova, Marie; Pietiainen, Vilja; Kutay, Ulrike; Smith, Kevin; Horvath, Peter

2017-06-28

High-content, imaging-based screens now routinely generate data on a scale that precludes manual verification and interrogation. Software applying machine learning has become an essential tool to automate analysis, but these methods require annotated examples to learn from. Efficiently exploring large datasets to find relevant examples remains a challenging bottleneck. Here, we present Advanced Cell Classifier (ACC), a graphical software package for phenotypic analysis that addresses these difficulties. ACC applies machine-learning and image-analysis methods to high-content data generated by large-scale, cell-based experiments. It features methods to mine microscopic image data, discover new phenotypes, and improve recognition performance. We demonstrate that these features substantially expedite the training process, successfully uncover rare phenotypes, and improve the accuracy of the analysis. ACC is extensively documented, designed to be user-friendly for researchers without machine-learning expertise, and distributed as a free open-source tool at www.cellclassifier.org. Copyright © 2017 Elsevier Inc. All rights reserved.

Qualitative and quantitative effects of harmonic echocardiographic imaging on endocardial edge definition and side-lobe artifacts

NASA Technical Reports Server (NTRS)

Rubin, D. N.; Yazbek, N.; Garcia, M. J.; Stewart, W. J.; Thomas, J. D.

2000-01-01

Harmonic imaging is a new ultrasonographic technique that is designed to improve image quality by exploiting the spontaneous generation of higher frequencies as ultrasound propagates through tissue. We studied 51 difficult-to-image patients with blinded side-by-side cineloop evaluation of endocardial border definition by harmonic versus fundamental imaging. In addition, quantitative intensities from cavity versus wall were compared for harmonic versus fundamental imaging. Harmonic imaging improved left ventricular endocardial border delineation over fundamental imaging (superior: harmonic = 71.1%, fundamental = 18.7%; similar: 10.2%; P <.001). Quantitative analysis of 100 wall/cavity combinations demonstrated brighter wall segments and more strikingly darker cavities during harmonic imaging (cavity intensity on a 0 to 255 scale: fundamental = 15.6 +/- 8.6; harmonic = 6.0 +/- 5.3; P <.0001), which led to enhanced contrast between the wall and cavity (1.89 versus 1.19, P <.0001). Harmonic imaging reduces side-lobe artifacts, resulting in a darker cavity and brighter walls, thereby improving image contrast and endocardial delineation.

A pathologist-designed imaging system for anatomic pathology signout, teaching, and research.

PubMed

Schubert, E; Gross, W; Siderits, R H; Deckenbaugh, L; He, F; Becich, M J

1994-11-01

Pathology images are derived from gross surgical specimens, light microscopy, immunofluorescence, electron microscopy, molecular diagnostic gels, flow cytometry, image analysis data, and clinical laboratory data in graphic form. We have implemented a network of desktop personal computers (PCs) that allow us to easily capture, store, and retrieve gross and microscopic, anatomic, and research pathology images. System architecture involves multiple image acquisition and retrieval sites and a central file server for storage. The digitized images are conveyed via a local area network to and from image capture or display stations. Acquisition sites consist of a high-resolution camera connected to a frame grabber card in a 486-type personal computer, equipped with 16 MB (Table 1) RAM, a 1.05-gigabyte hard drive, and a 32-bit ethernet card for access to our anatomic pathology reporting system. We have designed a push-button workstation for acquiring and indexing images that does not significantly interfere with surgical pathology sign-out. Advantages of the system include the following: (1) Improving patient care: the availability of gross images at time of microscopic sign-out, verification of recurrence of malignancy from archived images, monitoring of bone marrow engraftment and immunosuppressive intervention after bone marrow/solid organ transplantation on repeat biopsies, and ability to seek instantaneous consultation with any pathologist on the network; (2) enhancing the teaching environment: building a digital surgical pathology atlas, improving the availability of images for conference support, and sharing cases across the network; (3) enhancing research: case study compilation, metastudy analysis, and availability of digitized images for quantitative analysis and permanent/reusable image records for archival study; and (4) other practical and economic considerations: storing case requisition images and hand-drawn diagrams deters the spread of gross room contaminants and results in considerable cost savings in photographic media for conferences, improved quality assurance by porting control stains across the network, and a multiplicity of other advantages that enhance image and information management in pathology.

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.

Simulation of realistic abnormal SPECT brain perfusion images: application in semi-quantitative analysis

NASA Astrophysics Data System (ADS)

Ward, T.; Fleming, J. S.; Hoffmann, S. M. A.; Kemp, P. M.

2005-11-01

Simulation is useful in the validation of functional image analysis methods, particularly when considering the number of analysis techniques currently available lacking thorough validation. Problems exist with current simulation methods due to long run times or unrealistic results making it problematic to generate complete datasets. A method is presented for simulating known abnormalities within normal brain SPECT images using a measured point spread function (PSF), and incorporating a stereotactic atlas of the brain for anatomical positioning. This allows for the simulation of realistic images through the use of prior information regarding disease progression. SPECT images of cerebral perfusion have been generated consisting of a control database and a group of simulated abnormal subjects that are to be used in a UK audit of analysis methods. The abnormality is defined in the stereotactic space, then transformed to the individual subject space, convolved with a measured PSF and removed from the normal subject image. The dataset was analysed using SPM99 (Wellcome Department of Imaging Neuroscience, University College, London) and the MarsBaR volume of interest (VOI) analysis toolbox. The results were evaluated by comparison with the known ground truth. The analysis showed improvement when using a smoothing kernel equal to system resolution over the slightly larger kernel used routinely. Significant correlation was found between effective volume of a simulated abnormality and the detected size using SPM99. Improvements in VOI analysis sensitivity were found when using the region median over the region mean. The method and dataset provide an efficient methodology for use in the comparison and cross validation of semi-quantitative analysis methods in brain SPECT, and allow the optimization of analysis parameters.

Analysis and improvement of the quantum image matching

NASA Astrophysics Data System (ADS)

Dang, Yijie; Jiang, Nan; Hu, Hao; Zhang, Wenyin

2017-11-01

We investigate the quantum image matching algorithm proposed by Jiang et al. (Quantum Inf Process 15(9):3543-3572, 2016). Although the complexity of this algorithm is much better than the classical exhaustive algorithm, there may be an error in it: After matching the area between two images, only the pixel at the upper left corner of the matched area played part in following steps. That is to say, the paper only matched one pixel, instead of an area. If more than one pixels in the big image are the same as the one at the upper left corner of the small image, the algorithm will randomly measure one of them, which causes the error. In this paper, an improved version is presented which takes full advantage of the whole matched area to locate a small image in a big image. The theoretical analysis indicates that the network complexity is higher than the previous algorithm, but it is still far lower than the classical algorithm. Hence, this algorithm is still efficient.

Optimization of oncological {sup 18}F-FDG PET/CT imaging based on a multiparameter analysis

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

Menezes, Vinicius O., E-mail: vinicius@radtec.com.br; Machado, Marcos A. D.; Queiroz, Cleiton C.

2016-02-15

Purpose: This paper describes a method to achieve consistent clinical image quality in {sup 18}F-FDG scans accounting for patient habitus, dose regimen, image acquisition, and processing techniques. Methods: Oncological PET/CT scan data for 58 subjects were evaluated retrospectively to derive analytical curves that predict image quality. Patient noise equivalent count rate and coefficient of variation (CV) were used as metrics in their analysis. Optimized acquisition protocols were identified and prospectively applied to 179 subjects. Results: The adoption of different schemes for three body mass ranges (<60 kg, 60–90 kg, >90 kg) allows improved image quality with both point spread functionmore » and ordered-subsets expectation maximization-3D reconstruction methods. The application of this methodology showed that CV improved significantly (p < 0.0001) in clinical practice. Conclusions: Consistent oncological PET/CT image quality on a high-performance scanner was achieved from an analysis of the relations existing between dose regimen, patient habitus, acquisition, and processing techniques. The proposed methodology may be used by PET/CT centers to develop protocols to standardize PET/CT imaging procedures and achieve better patient management and cost-effective operations.« less

Automated MicroSPECT/MicroCT Image Analysis of the Mouse Thyroid Gland.

PubMed

Cheng, Peng; Hollingsworth, Brynn; Scarberry, Daniel; Shen, Daniel H; Powell, Kimerly; Smart, Sean C; Beech, John; Sheng, Xiaochao; Kirschner, Lawrence S; Menq, Chia-Hsiang; Jhiang, Sissy M

2017-11-01

The ability of thyroid follicular cells to take up iodine enables the use of radioactive iodine (RAI) for imaging and targeted killing of RAI-avid thyroid cancer following thyroidectomy. To facilitate identifying novel strategies to improve 131 I therapeutic efficacy for patients with RAI refractory disease, it is desired to optimize image acquisition and analysis for preclinical mouse models of thyroid cancer. A customized mouse cradle was designed and used for microSPECT/CT image acquisition at 1 hour (t1) and 24 hours (t24) post injection of 123 I, which mainly reflect RAI influx/efflux equilibrium and RAI retention in the thyroid, respectively. FVB/N mice with normal thyroid glands and TgBRAF V600E mice with thyroid tumors were imaged. In-house CTViewer software was developed to streamline image analysis with new capabilities, along with display of 3D voxel-based 123 I gamma photon intensity in MATLAB. The customized mouse cradle facilitates consistent tissue configuration among image acquisitions such that rigid body registration can be applied to align serial images of the same mouse via the in-house CTViewer software. CTViewer is designed specifically to streamline SPECT/CT image analysis with functions tailored to quantify thyroid radioiodine uptake. Automatic segmentation of thyroid volumes of interest (VOI) from adjacent salivary glands in t1 images is enabled by superimposing the thyroid VOI from the t24 image onto the corresponding aligned t1 image. The extent of heterogeneity in 123 I accumulation within thyroid VOIs can be visualized by 3D display of voxel-based 123 I gamma photon intensity. MicroSPECT/CT image acquisition and analysis for thyroidal RAI uptake is greatly improved by the cradle and the CTViewer software, respectively. Furthermore, the approach of superimposing thyroid VOIs from t24 images to select thyroid VOIs on corresponding aligned t1 images can be applied to studies in which the target tissue has differential radiotracer retention from surrounding tissues.

Improvement of single wavelength-based Thai jasmine rice identification with elliptic Fourier descriptor and neural network analysis

NASA Astrophysics Data System (ADS)

Suwansukho, Kajpanya; Sumriddetchkajorn, Sarun; Buranasiri, Prathan

2012-11-01

Instead of considering only the amount of fluorescent signal spatially distributed on the image of milled rice grains this paper shows how our single-wavelength spectral-imaging-based Thai jasmine (KDML105) rice identification system can be improved by analyzing the shape and size of the image of each milled rice variety especially during the image threshold operation. The image of each milled rice variety is expressed as chain codes and elliptic Fourier coefficients. After that, a feed-forward back-propagation neural network model is applied, resulting in an improved average FAR of 11.0% and FRR of 19.0% in identifying KDML105 milled rice from the unwanted four milled rice varieties.

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.

Image Processing for Binarization Enhancement via Fuzzy Reasoning

NASA Technical Reports Server (NTRS)

Dominguez, Jesus A. (Inventor)

2009-01-01

A technique for enhancing a gray-scale image to improve conversions of the image to binary employs fuzzy reasoning. In the technique, pixels in the image are analyzed by comparing the pixel's gray scale value, which is indicative of its relative brightness, to the values of pixels immediately surrounding the selected pixel. The degree to which each pixel in the image differs in value from the values of surrounding pixels is employed as the variable in a fuzzy reasoning-based analysis that determines an appropriate amount by which the selected pixel's value should be adjusted to reduce vagueness and ambiguity in the image and improve retention of information during binarization of the enhanced gray-scale image.

Improving Image Drizzling in the HST Archive: Advanced Camera for Surveys

NASA Astrophysics Data System (ADS)

Hoffmann, Samantha L.; Avila, Roberto J.

2017-06-01

The Mikulski Archive for Space Telescopes (MAST) pipeline performs geometric distortion corrections, associated image combinations, and cosmic ray rejections with AstroDrizzle on Hubble Space Telescope (HST) data. The MDRIZTAB reference table contains a list of relevant parameters that controls this program. This document details our photometric analysis of Advanced Camera for Surveys Wide Field Channel (ACS/WFC) data processed by AstroDrizzle. Based on this analysis, we update the MDRIZTAB table to improve the quality of the drizzled products delivered by MAST.

Supervised graph hashing for histopathology image retrieval and classification.

PubMed

Shi, Xiaoshuang; Xing, Fuyong; Xu, KaiDi; Xie, Yuanpu; Su, Hai; Yang, Lin

2017-12-01

In pathology image analysis, morphological characteristics of cells are critical to grade many diseases. With the development of cell detection and segmentation techniques, it is possible to extract cell-level information for further analysis in pathology images. However, it is challenging to conduct efficient analysis of cell-level information on a large-scale image dataset because each image usually contains hundreds or thousands of cells. In this paper, we propose a novel image retrieval based framework for large-scale pathology image analysis. For each image, we encode each cell into binary codes to generate image representation using a novel graph based hashing model and then conduct image retrieval by applying a group-to-group matching method to similarity measurement. In order to improve both computational efficiency and memory requirement, we further introduce matrix factorization into the hashing model for scalable image retrieval. The proposed framework is extensively validated with thousands of lung cancer images, and it achieves 97.98% classification accuracy and 97.50% retrieval precision with all cells of each query image used. Copyright © 2017 Elsevier B.V. All rights reserved.

Fast Depiction Invariant Visual Similarity for Content Based Image Retrieval Based on Data-driven Visual Similarity using Linear Discriminant Analysis

NASA Astrophysics Data System (ADS)

Wihardi, Y.; Setiawan, W.; Nugraha, E.

2018-01-01

On this research we try to build CBIRS based on Learning Distance/Similarity Function using Linear Discriminant Analysis (LDA) and Histogram of Oriented Gradient (HoG) feature. Our method is invariant to depiction of image, such as similarity of image to image, sketch to image, and painting to image. LDA can decrease execution time compared to state of the art method, but it still needs an improvement in term of accuracy. Inaccuracy in our experiment happen because we did not perform sliding windows search and because of low number of negative samples as natural-world images.

Super-resolution mapping using multi-viewing CHRIS/PROBA data

NASA Astrophysics Data System (ADS)

Dwivedi, Manish; Kumar, Vinay

2016-04-01

High-spatial resolution Remote Sensing (RS) data provides detailed information which ensures high-definition visual image analysis of earth surface features. These data sets also support improved information extraction capabilities at a fine scale. In order to improve the spatial resolution of coarser resolution RS data, the Super Resolution Reconstruction (SRR) technique has become widely acknowledged which focused on multi-angular image sequences. In this study multi-angle CHRIS/PROBA data of Kutch area is used for SR image reconstruction to enhance the spatial resolution from 18 m to 6m in the hope to obtain a better land cover classification. Various SR approaches like Projection onto Convex Sets (POCS), Robust, Iterative Back Projection (IBP), Non-Uniform Interpolation and Structure-Adaptive Normalized Convolution (SANC) chosen for this study. Subjective assessment through visual interpretation shows substantial improvement in land cover details. Quantitative measures including peak signal to noise ratio and structural similarity are used for the evaluation of the image quality. It was observed that SANC SR technique using Vandewalle algorithm for the low resolution image registration outperformed the other techniques. After that SVM based classifier is used for the classification of SRR and data resampled to 6m spatial resolution using bi-cubic interpolation. A comparative analysis is carried out between classified data of bicubic interpolated and SR derived images of CHRIS/PROBA and SR derived classified data have shown a significant improvement of 10-12% in the overall accuracy. The results demonstrated that SR methods is able to improve spatial detail of multi-angle images as well as the classification accuracy.

Using Image Analysis to Build Reading Comprehension

ERIC Educational Resources Information Center

Brown, Sarah Drake; Swope, John

2010-01-01

Content area reading remains a primary concern of history educators. In order to better prepare students for encounters with text, the authors propose the use of two image analysis strategies tied with a historical theme to heighten student interest in historical content and provide a basis for improved reading comprehension.

STEM_CELL: a software tool for electron microscopy: part 2--analysis of crystalline materials.

PubMed

Grillo, Vincenzo; Rossi, Francesca

2013-02-01

A new graphical software (STEM_CELL) for analysis of HRTEM and STEM-HAADF images is here introduced in detail. The advantage of the software, beyond its graphic interface, is to put together different analysis algorithms and simulation (described in an associated article) to produce novel analysis methodologies. Different implementations and improvements to state of the art approach are reported in the image analysis, filtering, normalization, background subtraction. In particular two important methodological results are here highlighted: (i) the definition of a procedure for atomic scale quantitative analysis of HAADF images, (ii) the extension of geometric phase analysis to large regions up to potentially 1μm through the use of under sampled images with aliasing effects. Copyright © 2012 Elsevier B.V. All rights reserved.

Monte Carlo simulation of PET/MR scanner and assessment of motion correction strategies

NASA Astrophysics Data System (ADS)

Işın, A.; Uzun Ozsahin, D.; Dutta, J.; Haddani, S.; El-Fakhri, G.

2017-03-01

Positron Emission Tomography is widely used in three dimensional imaging of metabolic body function and in tumor detection. Important research efforts are made to improve this imaging modality and powerful simulators such as GATE are used to test and develop methods for this purpose. PET requires acquisition time in the order of few minutes. Therefore, because of the natural patient movements such as respiration, the image quality can be adversely affected which drives scientists to develop motion compensation methods to improve the image quality. The goal of this study is to evaluate various image reconstructions methods with GATE simulation of a PET acquisition of the torso area. Obtained results show the need to compensate natural respiratory movements in order to obtain an image with similar quality as the reference image. Improvements are still possible in the applied motion field's extraction algorithms. Finally a statistical analysis should confirm the obtained results.

Guided SAR image despeckling with probabilistic non local weights

NASA Astrophysics Data System (ADS)

Gokul, Jithin; Nair, Madhu S.; Rajan, Jeny

2017-12-01

SAR images are generally corrupted by granular disturbances called speckle, which makes visual analysis and detail extraction a difficult task. Non Local despeckling techniques with probabilistic similarity has been a recent trend in SAR despeckling. To achieve effective speckle suppression without compromising detail preservation, we propose an improvement for the existing Generalized Guided Filter with Bayesian Non-Local Means (GGF-BNLM) method. The proposed method (Guided SAR Image Despeckling with Probabilistic Non Local Weights) replaces parametric constants based on heuristics in GGF-BNLM method with dynamically derived values based on the image statistics for weight computation. Proposed changes make GGF-BNLM method adaptive and as a result, significant improvement is achieved in terms of performance. Experimental analysis on SAR images shows excellent speckle reduction without compromising feature preservation when compared to GGF-BNLM method. Results are also compared with other state-of-the-art and classic SAR depseckling techniques to demonstrate the effectiveness of the proposed method.

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

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.

[An improved medical image fusion algorithm and quality evaluation].

PubMed

Chen, Meiling; Tao, Ling; Qian, Zhiyu

2009-08-01

Medical image fusion is of very important value for application in medical image analysis and diagnosis. In this paper, the conventional method of wavelet fusion is improved,so a new algorithm of medical image fusion is presented and the high frequency and low frequency coefficients are studied respectively. When high frequency coefficients are chosen, the regional edge intensities of each sub-image are calculated to realize adaptive fusion. The choice of low frequency coefficient is based on the edges of images, so that the fused image preserves all useful information and appears more distinctly. We apply the conventional and the improved fusion algorithms based on wavelet transform to fuse two images of human body and also evaluate the fusion results through a quality evaluation method. Experimental results show that this algorithm can effectively retain the details of information on original images and enhance their edge and texture features. This new algorithm is better than the conventional fusion algorithm based on wavelet transform.

Variable Threshold Method for Determining the Boundaries of Imaged Subvisible Particles.

PubMed

Cavicchi, Richard E; Collett, Cayla; Telikepalli, Srivalli; Hu, Zhishang; Carrier, Michael; Ripple, Dean C

2017-06-01

An accurate assessment of particle characteristics and concentrations in pharmaceutical products by flow imaging requires accurate particle sizing and morphological analysis. Analysis of images begins with the definition of particle boundaries. Commonly a single threshold defines the level for a pixel in the image to be included in the detection of particles, but depending on the threshold level, this results in either missing translucent particles or oversizing of less transparent particles due to the halos and gradients in intensity near the particle boundaries. We have developed an imaging analysis algorithm that sets the threshold for a particle based on the maximum gray value of the particle. We show that this results in tighter boundaries for particles with high contrast, while conserving the number of highly translucent particles detected. The method is implemented as a plugin for FIJI, an open-source image analysis software. The method is tested for calibration beads in water and glycerol/water solutions, a suspension of microfabricated rods, and stir-stressed aggregates made from IgG. The result is that appropriate thresholds are automatically set for solutions with a range of particle properties, and that improved boundaries will allow for more accurate sizing results and potentially improved particle classification studies. Published by Elsevier Inc.

Development of alternative data analysis techniques for improving the accuracy and specificity of natural resource inventories made with digital remote sensing data

NASA Technical Reports Server (NTRS)

Lillesand, T. M.; Meisner, D. E. (Principal Investigator)

1980-01-01

An investigation was conducted into ways to improve the involvement of state and local user personnel in the digital image analysis process by isolating those elements of the analysis process which require extensive involvement by field personnel and providing means for performing those activities apart from a computer facility. In this way, the analysis procedure can be converted from a centralized activity focused on a computer facility to a distributed activity in which users can interact with the data at the field office level or in the field itself. A general image processing software was developed on the University of Minnesota computer system (Control Data Cyber models 172 and 74). The use of color hardcopy image data as a primary medium in supervised training procedures was investigated and digital display equipment and a coordinate digitizer were procured.

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.

Automated daily quality control analysis for mammography in a multi-unit imaging center.

PubMed

Sundell, Veli-Matti; Mäkelä, Teemu; Meaney, Alexander; Kaasalainen, Touko; Savolainen, Sauli

2018-01-01

Background The high requirements for mammography image quality necessitate a systematic quality assurance process. Digital imaging allows automation of the image quality analysis, which can potentially improve repeatability and objectivity compared to a visual evaluation made by the users. Purpose To develop an automatic image quality analysis software for daily mammography quality control in a multi-unit imaging center. Material and Methods An automated image quality analysis software using the discrete wavelet transform and multiresolution analysis was developed for the American College of Radiology accreditation phantom. The software was validated by analyzing 60 randomly selected phantom images from six mammography systems and 20 phantom images with different dose levels from one mammography system. The results were compared to a visual analysis made by four reviewers. Additionally, long-term image quality trends of a full-field digital mammography system and a computed radiography mammography system were investigated. Results The automated software produced feature detection levels comparable to visual analysis. The agreement was good in the case of fibers, while the software detected somewhat more microcalcifications and characteristic masses. Long-term follow-up via a quality assurance web portal demonstrated the feasibility of using the software for monitoring the performance of mammography systems in a multi-unit imaging center. Conclusion Automated image quality analysis enables monitoring the performance of digital mammography systems in an efficient, centralized manner.

Watershed identification of polygonal patterns in noisy SAR images.

PubMed

Moreels, Pierre; Smrekar, Suzanne E

2003-01-01

This paper describes a new approach to pattern recognition in synthetic aperture radar (SAR) images. A visual analysis of the images provided by NASA's Magellan mission to Venus has revealed a number of zones showing polygonal-shaped faults on the surface of the planet. The goal of the paper is to provide a method to automate the identification of such zones. The high level of noise in SAR images and its multiplicative nature make automated image analysis difficult and conventional edge detectors, like those based on gradient images, inefficient. We present a scheme based on an improved watershed algorithm and a two-scale analysis. The method extracts potential edges in the SAR image, analyzes the patterns obtained, and decides whether or not the image contains a "polygon area". This scheme can also be applied to other SAR or visual images, for instance in observation of Mars and Jupiter's satellite Europa.

Improved disparity map analysis through the fusion of monocular image segmentations

NASA Technical Reports Server (NTRS)

Perlant, Frederic P.; Mckeown, David M.

1991-01-01

The focus is to examine how estimates of three dimensional scene structure, as encoded in a scene disparity map, can be improved by the analysis of the original monocular imagery. The utilization of surface illumination information is provided by the segmentation of the monocular image into fine surface patches of nearly homogeneous intensity to remove mismatches generated during stereo matching. These patches are used to guide a statistical analysis of the disparity map based on the assumption that such patches correspond closely with physical surfaces in the scene. Such a technique is quite independent of whether the initial disparity map was generated by automated area-based or feature-based stereo matching. Stereo analysis results are presented on a complex urban scene containing various man-made and natural features. This scene contains a variety of problems including low building height with respect to the stereo baseline, buildings and roads in complex terrain, and highly textured buildings and terrain. The improvements are demonstrated due to monocular fusion with a set of different region-based image segmentations. The generality of this approach to stereo analysis and its utility in the development of general three dimensional scene interpretation systems are also discussed.

Despeckle filtering software toolbox for ultrasound imaging of the common carotid artery.

PubMed

Loizou, Christos P; Theofanous, Charoula; Pantziaris, Marios; Kasparis, Takis

2014-04-01

Ultrasound imaging of the common carotid artery (CCA) is a non-invasive tool used in medicine to assess the severity of atherosclerosis and monitor its progression through time. It is also used in border detection and texture characterization of the atherosclerotic carotid plaque in the CCA, the identification and measurement of the intima-media thickness (IMT) and the lumen diameter that all are very important in the assessment of cardiovascular disease (CVD). Visual perception, however, is hindered by speckle, a multiplicative noise, that degrades the quality of ultrasound B-mode imaging. Noise reduction is therefore essential for improving the visual observation quality or as a pre-processing step for further automated analysis, such as image segmentation of the IMT and the atherosclerotic carotid plaque in ultrasound images. In order to facilitate this preprocessing step, we have developed in MATLAB(®) a unified toolbox that integrates image despeckle filtering (IDF), texture analysis and image quality evaluation techniques to automate the pre-processing and complement the disease evaluation in ultrasound CCA images. The proposed software, is based on a graphical user interface (GUI) and incorporates image normalization, 10 different despeckle filtering techniques (DsFlsmv, DsFwiener, DsFlsminsc, DsFkuwahara, DsFgf, DsFmedian, DsFhmedian, DsFad, DsFnldif, DsFsrad), image intensity normalization, 65 texture features, 15 quantitative image quality metrics and objective image quality evaluation. The software is publicly available in an executable form, which can be downloaded from http://www.cs.ucy.ac.cy/medinfo/. It was validated on 100 ultrasound images of the CCA, by comparing its results with quantitative visual analysis performed by a medical expert. It was observed that the despeckle filters DsFlsmv, and DsFhmedian improved image quality perception (based on the expert's assessment and the image texture and quality metrics). It is anticipated that the system could help the physician in the assessment of cardiovascular image analysis. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Cranial nerve contrast using nerve-specific fluorophores improved by paired-agent imaging with indocyanine green as a control agent

NASA Astrophysics Data System (ADS)

Torres, Veronica C.; Vuong, Victoria D.; Wilson, Todd; Wewel, Joshua; Byrne, Richard W.; Tichauer, Kenneth M.

2017-09-01

Nerve preservation during surgery is critical because damage can result in significant morbidity. This remains a challenge especially for skull base surgeries where cranial nerves (CNs) are involved because visualization and access are particularly poor in that location. We present a paired-agent imaging method to enhance identification of CNs using nerve-specific fluorophores. Two myelin-targeting imaging agents were evaluated, Oxazine 4 and Rhodamine 800, and coadministered with a control agent, indocyanine green, either intravenously or topically in rats. Fluorescence imaging was performed on excised brains ex vivo, and nerve contrast was evaluated via paired-agent ratiometric data analysis. Although contrast was improved among all experimental groups using paired-agent imaging compared to conventional, solely targeted imaging, Oxazine 4 applied directly exhibited the greatest enhancement, with a minimum 3 times improvement in CNs delineation. This work highlights the importance of accounting for nonspecific signal of targeted agents, and demonstrates that paired-agent imaging is one method capable of doing so. Although staining, rinsing, and imaging protocols need to be optimized, these findings serve as a demonstration for the potential use of paired-agent imaging to improve contrast of CNs, and consequently, surgical outcome.

Usefulness of IDEAL T2 imaging for homogeneous fat suppression and reducing susceptibility artefacts in brachial plexus MRI at 3.0 T.

PubMed

Tagliafico, Alberto; Bignotti, Bianca; Tagliafico, Giulio; Martinoli, Carlo

2016-01-01

To quantitatively and qualitatively compare fat-suppressed MR imaging quality using iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL) with that using frequency-selective fat-suppressed (FSFS) T2 images of the brachial plexus at 3.0 T. Prospective MR image analysis was performed in 40 volunteers and 40 patients at a single centre. Oblique-sagittal and coronal IDEAL fat-suppressed T2 images and FSFS T2 images were compared. Visual assessment was performed by two independent musculoskeletal radiologists with respect to: (1) susceptibility artefacts around the neck, (2) homogeneity of fat suppression, (3) image sharpness and (4) tissue resolution contrast of pathologies. The signal-to-noise ratios (SNR) for each image sequence were assessed. Compared to FSFS sequences, IDEAL fat-suppressed T2 images significantly reduced artefacts around the brachial plexus and significantly improved homogeneous fat suppression (p < 0.05). IDEAL significantly improved sharpness and lesion-to-tissue contrast (p < 0.05). The mean SNRs were significantly improved on T2-weighted IDEAL images (p < 0.05). IDEAL technique improved image quality by reducing artefacts around the brachial plexus while maintaining a high SNR and provided superior homogeneous fat suppression than FSFS sequences.

Evaluation of Alzheimer's disease by analysis of MR images using Objective Dialectical Classifiers as an alternative to ADC maps.

PubMed

Dos Santos, Wellington P; de Assis, Francisco M; de Souza, Ricardo E; Dos Santos Filho, Plinio B

2008-01-01

Alzheimer's disease is the most common cause of dementia, yet hard to diagnose precisely without invasive techniques, particularly at the onset of the disease. This work approaches image analysis and classification of synthetic multispectral images composed by diffusion-weighted (DW) magnetic resonance (MR) cerebral images for the evaluation of cerebrospinal fluid area and measuring the advance of Alzheimer's disease. A clinical 1.5 T MR imaging system was used to acquire all images presented. The classification methods are based on Objective Dialectical Classifiers, a new method based on Dialectics as defined in the Philosophy of Praxis. A 2-degree polynomial network with supervised training is used to generate the ground truth image. The classification results are used to improve the usual analysis of the apparent diffusion coefficient map.

Are patient specific meshes required for EIT head imaging?

PubMed

Jehl, Markus; Aristovich, Kirill; Faulkner, Mayo; Holder, David

2016-06-01

Head imaging with electrical impedance tomography (EIT) is usually done with time-differential measurements, to reduce time-invariant modelling errors. Previous research suggested that more accurate head models improved image quality, but no thorough analysis has been done on the required accuracy. We propose a novel pipeline for creation of precise head meshes from magnetic resonance imaging and computed tomography scans, which was applied to four different heads. Voltages were simulated on all four heads for perturbations of different magnitude, haemorrhage and ischaemia, in five different positions and for three levels of instrumentation noise. Statistical analysis showed that reconstructions on the correct mesh were on average 25% better than on the other meshes. However, the stroke detection rates were not improved. We conclude that a generic head mesh is sufficient for monitoring patients for secondary strokes following head trauma.

Methods of Hematoxylin and Erosin Image Information Acquisition and Optimization in Confocal Microscopy

PubMed Central

Yoon, Woong Bae; Kim, Hyunjin; Kim, Kwang Gi; Choi, Yongdoo; Chang, Hee Jin

2016-01-01

Objectives We produced hematoxylin and eosin (H&E) staining-like color images by using confocal laser scanning microscopy (CLSM), which can obtain the same or more information in comparison to conventional tissue staining. Methods We improved images by using several image converting techniques, including morphological methods, color space conversion methods, and segmentation methods. Results An image obtained after image processing showed coloring very similar to that in images produced by H&E staining, and it is advantageous to conduct analysis through fluorescent dye imaging and microscopy rather than analysis based on single microscopic imaging. Conclusions The colors used in CLSM are different from those seen in H&E staining, which is the method most widely used for pathologic diagnosis and is familiar to pathologists. Computer technology can facilitate the conversion of images by CLSM to be very similar to H&E staining images. We believe that the technique used in this study has great potential for application in clinical tissue analysis. PMID:27525165

Methods of Hematoxylin and Erosin Image Information Acquisition and Optimization in Confocal Microscopy.

PubMed

Yoon, Woong Bae; Kim, Hyunjin; Kim, Kwang Gi; Choi, Yongdoo; Chang, Hee Jin; Sohn, Dae Kyung

2016-07-01

We produced hematoxylin and eosin (H&E) staining-like color images by using confocal laser scanning microscopy (CLSM), which can obtain the same or more information in comparison to conventional tissue staining. We improved images by using several image converting techniques, including morphological methods, color space conversion methods, and segmentation methods. An image obtained after image processing showed coloring very similar to that in images produced by H&E staining, and it is advantageous to conduct analysis through fluorescent dye imaging and microscopy rather than analysis based on single microscopic imaging. The colors used in CLSM are different from those seen in H&E staining, which is the method most widely used for pathologic diagnosis and is familiar to pathologists. Computer technology can facilitate the conversion of images by CLSM to be very similar to H&E staining images. We believe that the technique used in this study has great potential for application in clinical tissue analysis.

Improvement of Speckle Contrast Image Processing by an Efficient Algorithm.

PubMed

Steimers, A; Farnung, W; Kohl-Bareis, M

2016-01-01

We demonstrate an efficient algorithm for the temporal and spatial based calculation of speckle contrast for the imaging of blood flow by laser speckle contrast analysis (LASCA). It reduces the numerical complexity of necessary calculations, facilitates a multi-core and many-core implementation of the speckle analysis and enables an independence of temporal or spatial resolution and SNR. The new algorithm was evaluated for both spatial and temporal based analysis of speckle patterns with different image sizes and amounts of recruited pixels as sequential, multi-core and many-core code.

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

Shoaf, S.; APS Engineering Support Division

A real-time image analysis system was developed for beam imaging diagnostics. An Apple Power Mac G5 with an Active Silicon LFG frame grabber was used to capture video images that were processed and analyzed. Software routines were created to utilize vector-processing hardware to reduce the time to process images as compared to conventional methods. These improvements allow for more advanced image processing diagnostics to be performed in real time.

Analysis of Non Local Image Denoising Methods

NASA Astrophysics Data System (ADS)

Pardo, Álvaro

Image denoising is probably one of the most studied problems in the image processing community. Recently a new paradigm on non local denoising was introduced. The Non Local Means method proposed by Buades, Morel and Coll attracted the attention of other researches who proposed improvements and modifications to their proposal. In this work we analyze those methods trying to understand their properties while connecting them to segmentation based on spectral graph properties. We also propose some improvements to automatically estimate the parameters used on these methods.

CMEIAS color segmentation: an improved computing technology to process color images for quantitative microbial ecology studies at single-cell resolution.

PubMed

Gross, Colin A; Reddy, Chandan K; Dazzo, Frank B

2010-02-01

Quantitative microscopy and digital image analysis are underutilized in microbial ecology largely because of the laborious task to segment foreground object pixels from background, especially in complex color micrographs of environmental samples. In this paper, we describe an improved computing technology developed to alleviate this limitation. The system's uniqueness is its ability to edit digital images accurately when presented with the difficult yet commonplace challenge of removing background pixels whose three-dimensional color space overlaps the range that defines foreground objects. Image segmentation is accomplished by utilizing algorithms that address color and spatial relationships of user-selected foreground object pixels. Performance of the color segmentation algorithm evaluated on 26 complex micrographs at single pixel resolution had an overall pixel classification accuracy of 99+%. Several applications illustrate how this improved computing technology can successfully resolve numerous challenges of complex color segmentation in order to produce images from which quantitative information can be accurately extracted, thereby gain new perspectives on the in situ ecology of microorganisms. Examples include improvements in the quantitative analysis of (1) microbial abundance and phylotype diversity of single cells classified by their discriminating color within heterogeneous communities, (2) cell viability, (3) spatial relationships and intensity of bacterial gene expression involved in cellular communication between individual cells within rhizoplane biofilms, and (4) biofilm ecophysiology based on ribotype-differentiated radioactive substrate utilization. The stand-alone executable file plus user manual and tutorial images for this color segmentation computing application are freely available at http://cme.msu.edu/cmeias/ . This improved computing technology opens new opportunities of imaging applications where discriminating colors really matter most, thereby strengthening quantitative microscopy-based approaches to advance microbial ecology in situ at individual single-cell resolution.

Contrast and harmonic imaging improves accuracy and efficiency of novice readers for dobutamine stress echocardiography

NASA Technical Reports Server (NTRS)

Vlassak, Irmien; Rubin, David N.; Odabashian, Jill A.; Garcia, Mario J.; King, Lisa M.; Lin, Steve S.; Drinko, Jeanne K.; Morehead, Annitta J.; Prior, David L.; Asher, Craig R.;

Histopathological Image Analysis: A Review

PubMed Central

Gurcan, Metin N.; Boucheron, Laura; Can, Ali; Madabhushi, Anant; Rajpoot, Nasir; Yener, Bulent

2010-01-01

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement to the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe. PMID:20671804

Fan fault diagnosis based on symmetrized dot pattern analysis and image matching

NASA Astrophysics Data System (ADS)

Xu, Xiaogang; Liu, Haixiao; Zhu, Hao; Wang, Songling

2016-07-01

To detect the mechanical failure of fans, a new diagnostic method based on the symmetrized dot pattern (SDP) analysis and image matching is proposed. Vibration signals of 13 kinds of running states are acquired on a centrifugal fan test bed and reconstructed by the SDP technique. The SDP pattern templates of each running state are established. An image matching method is performed to diagnose the fault. In order to improve the diagnostic accuracy, the single template, multiple templates and clustering fault templates are used to perform the image matching.

An extraction algorithm of pulmonary fissures from multislice CT image

NASA Astrophysics Data System (ADS)

Tachibana, Hiroyuki; Saita, Shinsuke; Yasutomo, Motokatsu; Kubo, Mitsuru; Kawata, Yoshiki; Niki, Noboru; Nakano, Yasutaka; Sasagawa, Michizo; Eguchi, Kenji; Moriyama, Noriyuki

2005-04-01

Aging and smoking history increases number of pulmonary emphysema. Alveoli restoration destroyed by pulmonary emphysema is difficult and early direction is important. Multi-slice CT technology has been improving 3-D image analysis with higher body axis resolution and shorter scan time. And low-dose high accuracy scanning becomes available. Multi-slice CT image helps physicians with accurate measuring but huge volume of the image data takes time and cost. This paper is intended for computer added emphysema region analysis and proves effectiveness of proposed algorithm.

Initial phantom study comparing image quality in computed tomography using adaptive statistical iterative reconstruction and new adaptive statistical iterative reconstruction v.

PubMed

Lim, Kyungjae; Kwon, Heejin; Cho, Jinhan; Oh, Jongyoung; Yoon, Seongkuk; Kang, Myungjin; Ha, Dongho; Lee, Jinhwa; Kang, Eunju

2015-01-01

The purpose of this study was to assess the image quality of a novel advanced iterative reconstruction (IR) method called as "adaptive statistical IR V" (ASIR-V) by comparing the image noise, contrast-to-noise ratio (CNR), and spatial resolution from those of filtered back projection (FBP) and adaptive statistical IR (ASIR) on computed tomography (CT) phantom image. We performed CT scans at 5 different tube currents (50, 70, 100, 150, and 200 mA) using 3 types of CT phantoms. Scanned images were subsequently reconstructed in 7 different scan settings, such as FBP, and 3 levels of ASIR and ASIR-V (30%, 50%, and 70%). The image noise was measured in the first study using body phantom. The CNR was measured in the second study using contrast phantom and the spatial resolutions were measured in the third study using a high-resolution phantom. We compared the image noise, CNR, and spatial resolution among the 7 reconstructed image scan settings to determine whether noise reduction, high CNR, and high spatial resolution could be achieved at ASIR-V. At quantitative analysis of the first and second studies, it showed that the images reconstructed using ASIR-V had reduced image noise and improved CNR compared with those of FBP and ASIR (P < 0.001). At qualitative analysis of the third study, it also showed that the images reconstructed using ASIR-V had significantly improved spatial resolution than those of FBP and ASIR (P < 0.001). Our phantom studies showed that ASIR-V provides a significant reduction in image noise and a significant improvement in CNR as well as spatial resolution. Therefore, this technique has the potential to reduce the radiation dose further without compromising image quality.

MRT letter: Guided filtering of image focus volume for 3D shape recovery of microscopic objects.

PubMed

Mahmood, Muhammad Tariq

2014-12-01

In this letter, a shape from focus (SFF) method is proposed that utilizes the guided image filtering to enhance the image focus volume efficiently. First, image focus volume is computed using a conventional focus measure. Then each layer of image focus volume is filtered using guided filtering. In this work, the all-in-focus image, which can be obtained from the initial focus volume, is used as guidance image. Finally, improved depth map is obtained from the filtered image focus volume by maximizing the focus measure along the optical axis. The proposed SFF method is efficient and provides better depth maps. The improved performance is highlighted by conducting several experiments using image sequences of simulated and real microscopic objects. The comparative analysis demonstrates the effectiveness of the proposed SFF method. © 2014 Wiley Periodicals, Inc.

Temporal binning of time-correlated single photon counting data improves exponential decay fits and imaging speed

PubMed Central

Walsh, Alex J.; Sharick, Joe T.; Skala, Melissa C.; Beier, Hope T.

2016-01-01

Time-correlated single photon counting (TCSPC) enables acquisition of fluorescence lifetime decays with high temporal resolution within the fluorescence decay. However, many thousands of photons per pixel are required for accurate lifetime decay curve representation, instrument response deconvolution, and lifetime estimation, particularly for two-component lifetimes. TCSPC imaging speed is inherently limited due to the single photon per laser pulse nature and low fluorescence event efficiencies (<10%) required to reduce bias towards short lifetimes. Here, simulated fluorescence lifetime decays are analyzed by SPCImage and SLIM Curve software to determine the limiting lifetime parameters and photon requirements of fluorescence lifetime decays that can be accurately fit. Data analysis techniques to improve fitting accuracy for low photon count data were evaluated. Temporal binning of the decays from 256 time bins to 42 time bins significantly (p<0.0001) improved fit accuracy in SPCImage and enabled accurate fits with low photon counts (as low as 700 photons/decay), a 6-fold reduction in required photons and therefore improvement in imaging speed. Additionally, reducing the number of free parameters in the fitting algorithm by fixing the lifetimes to known values significantly reduced the lifetime component error from 27.3% to 3.2% in SPCImage (p<0.0001) and from 50.6% to 4.2% in SLIM Curve (p<0.0001). Analysis of nicotinamide adenine dinucleotide–lactate dehydrogenase (NADH-LDH) solutions confirmed temporal binning of TCSPC data and a reduced number of free parameters improves exponential decay fit accuracy in SPCImage. Altogether, temporal binning (in SPCImage) and reduced free parameters are data analysis techniques that enable accurate lifetime estimation from low photon count data and enable TCSPC imaging speeds up to 6x and 300x faster, respectively, than traditional TCSPC analysis. PMID:27446663

High precision gas hydrate imaging of small-scale and high-resolution marine sparker multichannel seismic data

NASA Astrophysics Data System (ADS)

Luo, D.; Cai, F.

2017-12-01

Small-scale and high-resolution marine sparker multi-channel seismic surveys using large energy sparkers are characterized by a high dominant frequency of the seismic source, wide bandwidth, and a high resolution. The technology with a high-resolution and high-detection precision was designed to improve the imaging quality of shallow sedimentary. In the study, a 20KJ sparker and 24-channel streamer cable with a 6.25m group interval were used as a seismic source and receiver system, respectively. Key factors for seismic imaging of gas hydrate are enhancement of S/N ratio, amplitude compensation and detailed velocity analysis. However, the data in this study has some characteristics below: 1. Small maximum offsets are adverse to velocity analysis and multiple attenuation. 2. Lack of low frequency information, that is, information less than 100Hz are invisible. 3. Low S/N ratio since less coverage times (only 12 times). These characteristics make it difficult to reach the targets of seismic imaging. In the study, the target processing methods are used to improve the seismic imaging quality of gas hydrate. First, some technologies of noise suppression are combined used in pre-stack seismic data to suppression of seismic noise and improve the S/N ratio. These technologies including a spectrum sharing noise elimination method, median filtering and exogenous interference suppression method. Second, the combined method of three technologies including SRME, τ-p deconvolution and high precision Radon transformation is used to remove multiples. Third, accurate velocity field are used in amplitude energy compensation to highlight the Bottom Simulating Reflector (short for BSR, the indicator of gas hydrates) and gas migration pathways (such as gas chimneys, hot spots et al.). Fourth, fine velocity analysis technology are used to improve accuracy of velocity analysis. Fifth, pre-stack deconvolution processing technology is used to compensate for low frequency energy and suppress of ghost, thus formation reflection characteristics are highlighted. The result shows that the small-scale and high resolution marine sparker multi-channel seismic surveys are very effective in improving the resolution and quality of gas hydrate imaging than the conventional seismic acquisition technology.

Image processing for improved eye-tracking accuracy

NASA Technical Reports Server (NTRS)

Mulligan, J. B.; Watson, A. B. (Principal Investigator)

1997-01-01

Video cameras provide a simple, noninvasive method for monitoring a subject's eye movements. An important concept is that of the resolution of the system, which is the smallest eye movement that can be reliably detected. While hardware systems are available that estimate direction of gaze in real-time from a video image of the pupil, such systems must limit image processing to attain real-time performance and are limited to a resolution of about 10 arc minutes. Two ways to improve resolution are discussed. The first is to improve the image processing algorithms that are used to derive an estimate. Off-line analysis of the data can improve resolution by at least one order of magnitude for images of the pupil. A second avenue by which to improve resolution is to increase the optical gain of the imaging setup (i.e., the amount of image motion produced by a given eye rotation). Ophthalmoscopic imaging of retinal blood vessels provides increased optical gain and improved immunity to small head movements but requires a highly sensitive camera. The large number of images involved in a typical experiment imposes great demands on the storage, handling, and processing of data. A major bottleneck had been the real-time digitization and storage of large amounts of video imagery, but recent developments in video compression hardware have made this problem tractable at a reasonable cost. Images of both the retina and the pupil can be analyzed successfully using a basic toolbox of image-processing routines (filtering, correlation, thresholding, etc.), which are, for the most part, well suited to implementation on vectorizing supercomputers.

Setting Standards for Reporting and Quantification in Fluorescence-Guided Surgery.

PubMed

Hoogstins, Charlotte; Burggraaf, Jan Jaap; Koller, Marjory; Handgraaf, Henricus; Boogerd, Leonora; van Dam, Gooitzen; Vahrmeijer, Alexander; Burggraaf, Jacobus

2018-05-29

Intraoperative fluorescence imaging (FI) is a promising technique that could potentially guide oncologic surgeons toward more radical resections and thus improve clinical outcome. Despite the increase in the number of clinical trials, fluorescent agents and imaging systems for intraoperative FI, a standardized approach for imaging system performance assessment and post-acquisition image analysis is currently unavailable. We conducted a systematic, controlled comparison between two commercially available imaging systems using a novel calibration device for FI systems and various fluorescent agents. In addition, we analyzed fluorescence images from previous studies to evaluate signal-to-background ratio (SBR) and determinants of SBR. Using the calibration device, imaging system performance could be quantified and compared, exposing relevant differences in sensitivity. Image analysis demonstrated a profound influence of background noise and the selection of the background on SBR. In this article, we suggest clear approaches for the quantification of imaging system performance assessment and post-acquisition image analysis, attempting to set new standards in the field of FI.

Enhancement of low light level images using color-plus-mono dual camera.

PubMed

Jung, Yong Ju

2017-05-15

In digital photography, the improvement of imaging quality in low light shooting is one of the users' needs. Unfortunately, conventional smartphone cameras that use a single, small image sensor cannot provide satisfactory quality in low light level images. A color-plus-mono dual camera that consists of two horizontally separate image sensors, which simultaneously captures both a color and mono image pair of the same scene, could be useful for improving the quality of low light level images. However, an incorrect image fusion between the color and mono image pair could also have negative effects, such as the introduction of severe visual artifacts in the fused images. This paper proposes a selective image fusion technique that applies an adaptive guided filter-based denoising and selective detail transfer to only those pixels deemed reliable with respect to binocular image fusion. We employ a dissimilarity measure and binocular just-noticeable-difference (BJND) analysis to identify unreliable pixels that are likely to cause visual artifacts during image fusion via joint color image denoising and detail transfer from the mono image. By constructing an experimental system of color-plus-mono camera, we demonstrate that the BJND-aware denoising and selective detail transfer is helpful in improving the image quality during low light shooting.

Performance quantification of a millimeter-wavelength imaging system based on inexpensive glow-discharge-detector focal-plane array.

PubMed

Shilemay, Moshe; Rozban, Daniel; Levanon, Assaf; Yitzhaky, Yitzhak; Kopeika, Natan S; Yadid-Pecht, Orly; Abramovich, Amir

2013-03-01

Inexpensive millimeter-wavelength (MMW) optical digital imaging raises a challenge of evaluating the imaging performance and image quality because of the large electromagnetic wavelengths and pixel sensor sizes, which are 2 to 3 orders of magnitude larger than those of ordinary thermal or visual imaging systems, and also because of the noisiness of the inexpensive glow discharge detectors that compose the focal-plane array. This study quantifies the performances of this MMW imaging system. Its point-spread function and modulation transfer function were investigated. The experimental results and the analysis indicate that the image quality of this MMW imaging system is limited mostly by the noise, and the blur is dominated by the pixel sensor size. Therefore, the MMW image might be improved by oversampling, given that noise reduction is achieved. Demonstration of MMW image improvement through oversampling is presented.

Novel methods of imaging and analysis for the thermoregulatory sweat test.

PubMed

Carroll, Michael Sean; Reed, David W; Kuntz, Nancy L; Weese-Mayer, Debra Ellyn

2018-06-07

The thermoregulatory sweat test (TST) can be central to the identification and management of disorders affecting sudomotor function and small sensory and autonomic nerve fibers, but the cumbersome nature of the standard testing protocol has prevented its widespread adoption. A high resolution, quantitative, clean and simple assay of sweating could significantly improve identification and management of these disorders. Images from 89 clinical TSTs were analyzed retrospectively using two novel techniques. First, using the standard indicator powder, skin surface sweat distributions were determined algorithmically for each patient. Second, a fundamentally novel method using thermal imaging of forced evaporative cooling was evaluated through comparison with the standard technique. Correlation and receiver operating characteristic analyses were used to determine the degree of match between these methods, and the potential limits of thermal imaging were examined through cumulative analysis of all studied patients. Algorithmic encoding of sweating and non-sweating regions produces a more objective analysis for clinical decision making. Additionally, results from the forced cooling method correspond well with those from indicator powder imaging, with a correlation across spatial regions of -0.78 (CI: -0.84 to -0.71). The method works similarly across body regions, and frame-by-frame analysis suggests the ability to identify sweating regions within about 1 second of imaging. While algorithmic encoding can enhance the standard sweat testing protocol, thermal imaging with forced evaporative cooling can dramatically improve the TST by making it less time-consuming and more patient-friendly than the current approach.

Shuttle Imaging Radar - Geologic applications

NASA Technical Reports Server (NTRS)

Macdonald, H.; Bridges, L.; Waite, W.; Kaupp, V.

1982-01-01

The Space Shuttle, on its second flight (November 12, 1981), carried the first science and applications payload which provided an early demonstration of Shuttle's research capabilities. One of the experiments, the Shuttle Imaging Radar-A (SIR-A), had as a prime objective to evaluate the capability of spaceborne imaging radars as a tool for geologic exploration. The results of the experiment will help determine the value of using the combination of space radar and Landsat imagery for improved geologic analysis and mapping. Preliminary analysis of the Shuttle radar imagery with Seasat and Landsat imagery from similar areas provides evidence that spaceborne radars can significantly complement Landsat interpretation, and vastly improve geologic reconnaissance mapping in those areas of the world that are relatively unmapped because of perpetual cloud cover.

Automated image analysis method for detecting and quantifying macrovesicular steatosis in hematoxylin and eosin-stained histology images of human livers.

PubMed

Nativ, Nir I; Chen, Alvin I; Yarmush, Gabriel; Henry, Scot D; Lefkowitch, Jay H; Klein, Kenneth M; Maguire, Timothy J; Schloss, Rene; Guarrera, James V; Berthiaume, Francois; Yarmush, Martin L

2014-02-01

Large-droplet macrovesicular steatosis (ld-MaS) in more than 30% of liver graft hepatocytes is a major risk factor for liver transplantation. An accurate assessment of the ld-MaS percentage is crucial for determining liver graft transplantability, which is currently based on pathologists' evaluations of hematoxylin and eosin (H&E)-stained liver histology specimens, with the predominant criteria being the relative size of the lipid droplets (LDs) and their propensity to displace a hepatocyte's nucleus to the cell periphery. Automated image analysis systems aimed at objectively and reproducibly quantifying ld-MaS do not accurately differentiate large LDs from small-droplet macrovesicular steatosis and do not take into account LD-mediated nuclear displacement; this leads to a poor correlation with pathologists' assessments. Here we present an improved image analysis method that incorporates nuclear displacement as a key image feature for segmenting and classifying ld-MaS from H&E-stained liver histology slides. 52,000 LDs in 54 digital images from 9 patients were analyzed, and the performance of the proposed method was compared against the performance of current image analysis methods and the ld-MaS percentage evaluations of 2 trained pathologists from different centers. We show that combining nuclear displacement and LD size information significantly improves the separation between large and small macrovesicular LDs (specificity = 93.7%, sensitivity = 99.3%) and the correlation with pathologists' ld-MaS percentage assessments (linear regression coefficient of determination = 0.97). This performance vastly exceeds that of other automated image analyzers, which typically underestimate or overestimate pathologists' ld-MaS scores. This work demonstrates the potential of automated ld-MaS analysis in monitoring the steatotic state of livers. The image analysis principles demonstrated here may help to standardize ld-MaS scores among centers and ultimately help in the process of determining liver graft transplantability. © 2013 American Association for the Study of Liver Diseases.

Improved cancer diagnostics by different image processing techniques on OCT images

NASA Astrophysics Data System (ADS)

Kanawade, Rajesh; Lengenfelder, Benjamin; Marini Menezes, Tassiana; Hohmann, Martin; Kopfinger, Stefan; Hohmann, Tim; Grabiec, Urszula; Klämpfl, Florian; Gonzales Menezes, Jean; Waldner, Maximilian; Schmidt, Michael

2015-07-01

Optical-coherence tomography (OCT) is a promising non-invasive, high-resolution imaging modality which can be used for cancer diagnosis and its therapeutic assessment. However, speckle noise makes detection of cancer boundaries and image segmentation problematic and unreliable. Therefore, to improve the image analysis for a precise cancer border detection, the performance of different image processing algorithms such as mean, median, hybrid median filter and rotational kernel transformation (RKT) for this task is investigated. This is done on OCT images acquired from an ex-vivo human cancerous mucosa and in vitro by using cultivated tumour applied on organotypical hippocampal slice cultures. The preliminary results confirm that the border between the healthy and the cancer lesions can be identified precisely. The obtained results are verified with fluorescence microscopy. This research can improve cancer diagnosis and the detection of borders between healthy and cancerous tissue. Thus, it could also reduce the number of biopsies required during screening endoscopy by providing better guidance to the physician.

a New Improved Threshold Segmentation Method for Scanning Images of Reservoir Rocks Considering Pore Fractal Characteristics

NASA Astrophysics Data System (ADS)

Lin, Wei; Li, Xizhe; Yang, Zhengming; Lin, Lijun; Xiong, Shengchun; Wang, Zhiyuan; Wang, Xiangyang; Xiao, Qianhua

Based on the basic principle of the porosity method in image segmentation, considering the relationship between the porosity of the rocks and the fractal characteristics of the pore structures, a new improved image segmentation method was proposed, which uses the calculated porosity of the core images as a constraint to obtain the best threshold. The results of comparative analysis show that the porosity method can best segment images theoretically, but the actual segmentation effect is deviated from the real situation. Due to the existence of heterogeneity and isolated pores of cores, the porosity method that takes the experimental porosity of the whole core as the criterion cannot achieve the desired segmentation effect. On the contrary, the new improved method overcomes the shortcomings of the porosity method, and makes a more reasonable binary segmentation for the core grayscale images, which segments images based on the actual porosity of each image by calculated. Moreover, the image segmentation method based on the calculated porosity rather than the measured porosity also greatly saves manpower and material resources, especially for tight rocks.

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

Impact of imaging guidelines on X-ray use among American provider network chiropractors: interrupted time series analysis.

PubMed

Bussières, André E; Sales, Anne E; Ramsay, Timothy; Hilles, Steven M; Grimshaw, Jeremy M

2014-08-01

Overuse and misuse of spine X-ray imaging for nonspecific back and neck pain persists among chiropractors. Distribution of educational materials among physicians results in small-to-modest improvements in appropriate care, such as ordering spine X-ray studies, but little is known about its impact among North American chiropractors. To evaluate the impact of web-based dissemination of a diagnostic imaging guideline on the use of spine X-ray images among chiropractors. Quasi-experimental design that used interrupted time series to evaluate the effect of guidelines dissemination on spine X-ray imaging claims by chiropractors enlisted in managed care network in the United States. Consecutive adult patients consulting for complaints of spine disorders. A change in level (the mean number of spine X-ray imaging claims per month immediately after the introduction of the guidelines), change in trend (any differences between preintervention and postintervention slopes), estimation of monthly average intervention effect after the intervention. The imaging guideline was disseminated online in April 2008. Administrative claims data were extracted between January 2006 and December 2010. Segmented regression analysis with autoregressive error was used to estimate the impact of guideline recommendations on the rate of spine X-ray studies. Sensitivity analysis considered the effect of two additional quality improvement strategies, a policy change and an education intervention. Time series analysis revealed a significant change in the level of spine X-ray study ordering weeks after introduction of the guidelines (-0.01; 95% confidence interval=-0.01, -0.002; p=.01), but no change in trend of the regression lines. The monthly mean rate of spine X-ray studies within 5 days of initial visit per new patient exams decreased by 10 per 1000, a 5.26% relative decrease after guideline dissemination. Controlling for two quality improvement strategies did not change the results. Web-based guideline dissemination was associated with an immediate reduction in spine X-ray imaging claims. Sensitivity analysis suggests our results are robust. This passive strategy is likely cost-effective in a chiropractic network setting. Copyright © 2014 Elsevier Inc. All rights reserved.

Updating the Synchrotron Radiation Monitor at TLS

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

Kuo, C. H.; Hsu, S. Y.; Wang, C. J.

2007-01-19

The synchrotron radiation monitor provides useful information to support routine operation and physics experiments using the beam. Precisely knowing the profile of the beam helps to improve machine performance. The synchrotron radiation monitor at the Taiwan Light Source (TLS) was recently upgraded. The optics and modeling were improved to increase the accuracy of measurement in the small beam size. A high-performance IEEE-1394 digital CCD camera was used to improve the quality of images and extend the dynamic range of measurement. The image analysis is also improved. This report summarizes status and results.

Optimization of a Biometric System Based on Acoustic Images

PubMed Central

Izquierdo Fuente, Alberto; Del Val Puente, Lara; Villacorta Calvo, Juan J.; Raboso Mateos, Mariano

2014-01-01

On the basis of an acoustic biometric system that captures 16 acoustic images of a person for 4 frequencies and 4 positions, a study was carried out to improve the performance of the system. On a first stage, an analysis to determine which images provide more information to the system was carried out showing that a set of 12 images allows the system to obtain results that are equivalent to using all of the 16 images. Finally, optimization techniques were used to obtain the set of weights associated with each acoustic image that maximizes the performance of the biometric system. These results improve significantly the performance of the preliminary system, while reducing the time of acquisition and computational burden, since the number of acoustic images was reduced. PMID:24616643

Hepatic lesions: improved image quality and detection with the periodically rotated overlapping parallel lines with enhanced reconstruction technique--evaluation of SPIO-enhanced T2-weighted MR images.

PubMed

Hirokawa, Yuusuke; Isoda, Hiroyoshi; Maetani, Yoji S; Arizono, Shigeki; Shimada, Kotaro; Okada, Tomohisa; Shibata, Toshiya; Togashi, Kaori

2009-05-01

To evaluate the effectiveness of the periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) technique for superparamagnetic iron oxide (SPIO)-enhanced T2-weighted magnetic resonance (MR) imaging with respiratory compensation with the prospective acquisition correction (PACE) technique in the detection of hepatic lesions. The institutional human research committee approved this prospective study, and all patients provided written informed consent. Eighty-one patients (mean age, 58 years) underwent hepatic 1.5-T MR imaging. Fat-saturated T2-weighted turbo spin-echo images were acquired with the PACE technique and with and without the PROPELLER method after administration of SPIO. Images were qualitatively evaluated for image artifacts, depiction of liver edge and intrahepatic vessels, overall image quality, and presence of lesions. Three radiologists independently assessed these characteristics with a five-point confidence scale. Diagnostic performance was assessed with receiver operating characteristic (ROC) curve analysis. Quantitative analysis was conducted by measuring the liver signal-to-noise ratio (SNR) and the lesion-to-liver contrast-to-noise ratio (CNR). The Wilcoxon signed rank test and two-tailed Student t test were used, and P < .05 indicated a significant difference. MR imaging with the PROPELLER and PACE techniques resulted in significantly improved image quality, higher sensitivity, and greater area under the ROC curve for hepatic lesion detection than did MR imaging with the PACE technique alone (P < .001). The mean liver SNR and the lesion-to-liver CNR were higher with the PROPELLER technique than without it (P < .001). T2-weighted MR imaging with the PROPELLER and PACE technique and SPIO enhancement is a promising method with which to improve the detection of hepatic lesions. (c) RSNA, 2009.

Vessel extraction in retinal images using automatic thresholding and Gabor Wavelet.

PubMed

Ali, Aziah; Hussain, Aini; Wan Zaki, Wan Mimi Diyana

2017-07-01

Retinal image analysis has been widely used for early detection and diagnosis of multiple systemic diseases. Accurate vessel extraction in retinal image is a crucial step towards a fully automated diagnosis system. This work affords an efficient unsupervised method for extracting blood vessels from retinal images by combining existing Gabor Wavelet (GW) method with automatic thresholding. Green channel image is extracted from color retinal image and used to produce Gabor feature image using GW. Both green channel image and Gabor feature image undergo vessel-enhancement step in order to highlight blood vessels. Next, the two vessel-enhanced images are transformed to binary images using automatic thresholding before combined to produce the final vessel output. Combining the images results in significant improvement of blood vessel extraction performance compared to using individual image. Effectiveness of the proposed method was proven via comparative analysis with existing methods validated using publicly available database, DRIVE.

Characterizing of tissue microstructure with single-detector polarization-sensitive optical coherence tomography

NASA Astrophysics Data System (ADS)

Liu, Bin; Harman, Michelle; Giattina, Susanne; Stamper, Debra L.; Demakis, Charles; Chilek, Mark; Raby, Stephanie; Brezinski, Mark E.

2006-06-01

Assessing tissue birefringence with imaging modality polarization-sensitive optical coherence tomography (PS-OCT) could improve the characterization of in vivo tissue pathology. Among the birefringent components, collagen may provide invaluable clinical information because of its alteration in disorders ranging from myocardial infarction to arthritis. But the features required of clinical imaging modality in these areas usually include the ability to assess the parameter of interest rapidly and without extensive data analysis, the characteristics that single-detector PS-OCT demonstrates. But beyond detecting organized collagen, which has been previously demonstrated and confirmed with the appropriate histological techniques, additional information can potentially be gained with PS-OCT, including collagen type, form versus intrinsic birefringence, the collagen angle, and the presence of multiple birefringence materials. In part I, we apply the simple but powerful fast-Fourier transform (FFT) to both PS-OCT mathematical modeling and in vitro bovine meniscus for improved PS-OCT data analysis. The FFT analysis yields, in a rapid, straightforward, and easily interpreted manner, information on the presence of multiple birefringent materials, distinguishing the true anatomical structure from patterns in image resulting from alterations in the polarization state and identifying the tissue/phantom optical axes. Therefore the use of the FFT analysis of PS-OCT data provides information on tissue composition beyond identifying the presence of organized collagen in real time and directly from the image without extensive mathematical manipulation or data analysis. In part II, Helistat phantoms (collagen type I) are analyzed with the ultimate goal of improved tissue characterization. This study, along with the data in part I, advance the insights gained from PS-OCT images beyond simply determining the presence or absence of birefringence.

Grab a coffee: your aerial images are already analyzed

NASA Astrophysics Data System (ADS)

Garetto, Anthony; Rademacher, Thomas; Schulz, Kristian

2015-07-01

For over 2 decades the AIMTM platform has been utilized in mask shops as the standard for actinic review of photomask sites in order to perform defect disposition and repair review. Throughout this time the measurement throughput of the systems has been improved in order to keep pace with the requirements demanded by a manufacturing environment, however the analysis of the sites captured has seen little improvement and remained a manual process. This manual analysis of aerial images is time consuming, subject to error and unreliability and contributes to holding up turn-around time (TAT) and slowing process flow in a manufacturing environment. AutoAnalysis, the first application available for the FAVOR® platform, offers a solution to these problems by providing fully automated data transfer and analysis of AIMTM aerial images. The data is automatically output in a customizable format that can be tailored to your internal needs and the requests of your customers. Savings in terms of operator time arise from the automated analysis which no longer needs to be performed. Reliability is improved as human error is eliminated making sure the most defective region is always and consistently captured. Finally the TAT is shortened and process flow for the back end of the line improved as the analysis is fast and runs in parallel to the measurements. In this paper the concept and approach of AutoAnalysis will be presented as well as an update to the status of the project. A look at the benefits arising from the automation and the customizable approach of the solution will be shown.

Automated X-ray image analysis for cargo security: Critical review and future promise.

PubMed

Rogers, Thomas W; Jaccard, Nicolas; Morton, Edward J; Griffin, Lewis D

2017-01-01

We review the relatively immature field of automated image analysis for X-ray cargo imagery. There is increasing demand for automated analysis methods that can assist in the inspection and selection of containers, due to the ever-growing volumes of traded cargo and the increasing concerns that customs- and security-related threats are being smuggled across borders by organised crime and terrorist networks. We split the field into the classical pipeline of image preprocessing and image understanding. Preprocessing includes: image manipulation; quality improvement; Threat Image Projection (TIP); and material discrimination and segmentation. Image understanding includes: Automated Threat Detection (ATD); and Automated Contents Verification (ACV). We identify several gaps in the literature that need to be addressed and propose ideas for future research. Where the current literature is sparse we borrow from the single-view, multi-view, and CT X-ray baggage domains, which have some characteristics in common with X-ray cargo.

MO-FG-202-06: Improving the Performance of Gamma Analysis QA with Radiomics- Based Image Analysis

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

Wootton, L; Nyflot, M; Ford, E

2016-06-15

Purpose: The use of gamma analysis for IMRT quality assurance has well-known limitations. Traditionally, a simple thresholding technique is used to evaluated passing criteria. However, like any image the gamma distribution is rich in information which thresholding mostly discards. We therefore propose a novel method of analyzing gamma images that uses quantitative image features borrowed from radiomics, with the goal of improving error detection. Methods: 368 gamma images were generated from 184 clinical IMRT beams. For each beam the dose to a phantom was measured with EPID dosimetry and compared to the TPS dose calculated with and without normally distributedmore » (2mm sigma) errors in MLC positions. The magnitude of 17 intensity histogram and size-zone radiomic features were derived from each image. The features that differed most significantly between image sets were determined with ROC analysis. A linear machine-learning model was trained on these features to classify images as with or without errors on 180 gamma images.The model was then applied to an independent validation set of 188 additional gamma distributions, half with and half without errors. Results: The most significant features for detecting errors were histogram kurtosis (p=0.007) and three size-zone metrics (p<1e-6 for each). The sizezone metrics detected clusters of high gamma-value pixels under mispositioned MLCs. The model applied to the validation set had an AUC of 0.8, compared to 0.56 for traditional gamma analysis with the decision threshold restricted to 98% or less. Conclusion: A radiomics-based image analysis method was developed that is more effective in detecting error than traditional gamma analysis. Though the pilot study here considers only MLC position errors, radiomics-based methods for other error types are being developed, which may provide better error detection and useful information on the source of detected errors. This work was partially supported by a grant from the Agency for Healthcare Research and Quality, grant number R18 HS022244-01.« less

A Meta-Analytic Review of Stand-Alone Interventions to Improve Body Image

PubMed Central

Alleva, Jessica M.; Sheeran, Paschal; Webb, Thomas L.; Martijn, Carolien; Miles, Eleanor

2015-01-01

Objective Numerous stand-alone interventions to improve body image have been developed. The present review used meta-analysis to estimate the effectiveness of such interventions, and to identify the specific change techniques that lead to improvement in body image. Methods The inclusion criteria were that (a) the intervention was stand-alone (i.e., solely focused on improving body image), (b) a control group was used, (c) participants were randomly assigned to conditions, and (d) at least one pretest and one posttest measure of body image was taken. Effect sizes were meta-analysed and moderator analyses were conducted. A taxonomy of 48 change techniques used in interventions targeted at body image was developed; all interventions were coded using this taxonomy. Results The literature search identified 62 tests of interventions (N = 3,846). Interventions produced a small-to-medium improvement in body image (d + = 0.38), a small-to-medium reduction in beauty ideal internalisation (d + = -0.37), and a large reduction in social comparison tendencies (d + = -0.72). However, the effect size for body image was inflated by bias both within and across studies, and was reliable but of small magnitude once corrections for bias were applied. Effect sizes for the other outcomes were no longer reliable once corrections for bias were applied. Several features of the sample, intervention, and methodology moderated intervention effects. Twelve change techniques were associated with improvements in body image, and three techniques were contra-indicated. Conclusions The findings show that interventions engender only small improvements in body image, and underline the need for large-scale, high-quality trials in this area. The review identifies effective techniques that could be deployed in future interventions. PMID:26418470

Evaluation of quantitative image analysis criteria for the high-resolution microendoscopic detection of neoplasia in Barrett's esophagus

NASA Astrophysics Data System (ADS)

Muldoon, Timothy J.; Thekkek, Nadhi; Roblyer, Darren; Maru, Dipen; Harpaz, Noam; Potack, Jonathan; Anandasabapathy, Sharmila; Richards-Kortum, Rebecca

2010-03-01

Early detection of neoplasia in patients with Barrett's esophagus is essential to improve outcomes. The aim of this ex vivo study was to evaluate the ability of high-resolution microendoscopic imaging and quantitative image analysis to identify neoplastic lesions in patients with Barrett's esophagus. Nine patients with pathologically confirmed Barrett's esophagus underwent endoscopic examination with biopsies or endoscopic mucosal resection. Resected fresh tissue was imaged with fiber bundle microendoscopy; images were analyzed by visual interpretation or by quantitative image analysis to predict whether the imaged sites were non-neoplastic or neoplastic. The best performing pair of quantitative features were chosen based on their ability to correctly classify the data into the two groups. Predictions were compared to the gold standard of histopathology. Subjective analysis of the images by expert clinicians achieved average sensitivity and specificity of 87% and 61%, respectively. The best performing quantitative classification algorithm relied on two image textural features and achieved a sensitivity and specificity of 87% and 85%, respectively. This ex vivo pilot trial demonstrates that quantitative analysis of images obtained with a simple microendoscope system can distinguish neoplasia in Barrett's esophagus with good sensitivity and specificity when compared to histopathology and to subjective image interpretation.

Adaptive image inversion of contrast 3D echocardiography for enabling automated analysis.

PubMed

Shaheen, Anjuman; Rajpoot, Kashif

2015-08-01

Contrast 3D echocardiography (C3DE) is commonly used to enhance the visual quality of ultrasound images in comparison with non-contrast 3D echocardiography (3DE). Although the image quality in C3DE is perceived to be improved for visual analysis, however it actually deteriorates for the purpose of automatic or semi-automatic analysis due to higher speckle noise and intensity inhomogeneity. Therefore, the LV endocardial feature extraction and segmentation from the C3DE images remains a challenging problem. To address this challenge, this work proposes an adaptive pre-processing method to invert the appearance of C3DE image. The image inversion is based on an image intensity threshold value which is automatically estimated through image histogram analysis. In the inverted appearance, the LV cavity appears dark while the myocardium appears bright thus making it similar in appearance to a 3DE image. Moreover, the resulting inverted image has high contrast and low noise appearance, yielding strong LV endocardium boundary and facilitating feature extraction for segmentation. Our results demonstrate that the inverse appearance of contrast image enables the subsequent LV segmentation. Copyright © 2015 Elsevier Ltd. All rights reserved.

A modeling analysis program for the JPL Table Mountain Io sodium cloud data

NASA Technical Reports Server (NTRS)

Smyth, W. H.; Goldberg, B. A.

1986-01-01

Progress and achievements in the second year are discussed in three main areas: (1) data quality review of the 1981 Region B/C images; (2) data processing activities; and (3) modeling activities. The data quality review revealed that almost all 1981 Region B/C images are of sufficient quality to be valuable in the analyses of the JPL data set. In the second area, the major milestone reached was the successful development and application of complex image-processing software required to render the original image data suitable for modeling analysis studies. In the third area, the lifetime description of sodium atoms in the planet magnetosphere was improved in the model to include the offset dipole nature of the magnetic field as well as an east-west electric field. These improvements are important in properly representing the basic morphology as well as the east-west asymmetries of the sodium cloud.

X-ray dark-field radiography facilitates the diagnosis of pulmonary fibrosis in a mouse model.

PubMed

Hellbach, Katharina; Yaroshenko, Andre; Willer, Konstantin; Conlon, Thomas M; Braunagel, Margarita B; Auweter, Sigrid; Yildirim, Ali Ö; Eickelberg, Oliver; Pfeiffer, Franz; Reiser, Maximilian F; Meinel, Felix G

2017-03-23

The aim of this study was to evaluate whether diagnosing pulmonary fibrosis with projection radiography can be improved by using X-ray dark-field radiograms. Pulmonary X-ray transmission and dark-field images of C57Bl/6N mice, either treated with bleomycin to induce pulmonary fibrosis or PBS to serve as controls, were acquired with a prototype grating-based small-animal scanner. Two blinded readers, both experienced radiologists and familiar with dark-field imaging, had to assess dark-field and transmission images for the absence or presence of fibrosis. Furthermore readers were asked to grade their stage of diagnostic confidence. Histological evaluation of the lungs served as the standard of reference in this study. Both readers showed a notably higher diagnostic confidence when analyzing the dark-field radiographs (p < 0.001). Diagnostic accuracy improved significantly when evaluating the lungs in dark-field images alone (p = 0.02) or in combination with transmission images (p = 0.01) compared to sole analysis of absorption images. Interreader agreement improved from good when assessing only transmission images to excellent when analyzing dark-field images alone or in combination with transmission images. Adding dark-field images to conventional transmission images in a murine model of pulmonary fibrosis leads to an improved diagnosis of this disease on chest radiographs.

Using spectral imaging for the analysis of abnormalities for colorectal cancer: When is it helpful?

PubMed

Awan, Ruqayya; Al-Maadeed, Somaya; Al-Saady, Rafif

2018-01-01

The spectral imaging technique has been shown to provide more discriminative information than the RGB images and has been proposed for a range of problems. There are many studies demonstrating its potential for the analysis of histopathology images for abnormality detection but there have been discrepancies among previous studies as well. Many multispectral based methods have been proposed for histopathology images but the significance of the use of whole multispectral cube versus a subset of bands or a single band is still arguable. We performed comprehensive analysis using individual bands and different subsets of bands to determine the effectiveness of spectral information for determining the anomaly in colorectal images. Our multispectral colorectal dataset consists of four classes, each represented by infra-red spectrum bands in addition to the visual spectrum bands. We performed our analysis of spectral imaging by stratifying the abnormalities using both spatial and spectral information. For our experiments, we used a combination of texture descriptors with an ensemble classification approach that performed best on our dataset. We applied our method to another dataset and got comparable results with those obtained using the state-of-the-art method and convolutional neural network based method. Moreover, we explored the relationship of the number of bands with the problem complexity and found that higher number of bands is required for a complex task to achieve improved performance. Our results demonstrate a synergy between infra-red and visual spectrum by improving the classification accuracy (by 6%) on incorporating the infra-red representation. We also highlight the importance of how the dataset should be divided into training and testing set for evaluating the histopathology image-based approaches, which has not been considered in previous studies on multispectral histopathology images.

Using spectral imaging for the analysis of abnormalities for colorectal cancer: When is it helpful?

PubMed Central

Al-Maadeed, Somaya; Al-Saady, Rafif

2018-01-01

The spectral imaging technique has been shown to provide more discriminative information than the RGB images and has been proposed for a range of problems. There are many studies demonstrating its potential for the analysis of histopathology images for abnormality detection but there have been discrepancies among previous studies as well. Many multispectral based methods have been proposed for histopathology images but the significance of the use of whole multispectral cube versus a subset of bands or a single band is still arguable. We performed comprehensive analysis using individual bands and different subsets of bands to determine the effectiveness of spectral information for determining the anomaly in colorectal images. Our multispectral colorectal dataset consists of four classes, each represented by infra-red spectrum bands in addition to the visual spectrum bands. We performed our analysis of spectral imaging by stratifying the abnormalities using both spatial and spectral information. For our experiments, we used a combination of texture descriptors with an ensemble classification approach that performed best on our dataset. We applied our method to another dataset and got comparable results with those obtained using the state-of-the-art method and convolutional neural network based method. Moreover, we explored the relationship of the number of bands with the problem complexity and found that higher number of bands is required for a complex task to achieve improved performance. Our results demonstrate a synergy between infra-red and visual spectrum by improving the classification accuracy (by 6%) on incorporating the infra-red representation. We also highlight the importance of how the dataset should be divided into training and testing set for evaluating the histopathology image-based approaches, which has not been considered in previous studies on multispectral histopathology images. PMID:29874262

Review of Telemicrobiology

PubMed Central

Rhoads, Daniel D.; Mathison, Blaine A.; Bishop, Henry S.; da Silva, Alexandre J.; Pantanowitz, Liron

2016-01-01

Context Microbiology laboratories are continually pursuing means to improve quality, rapidity, and efficiency of specimen analysis in the face of limited resources. One means by which to achieve these improvements is through the remote analysis of digital images. Telemicrobiology enables the remote interpretation of images of microbiology specimens. To date, the practice of clinical telemicrobiology has not been thoroughly reviewed. Objective Identify the various methods that can be employed for telemicrobiology, including emerging technologies that may provide value to the clinical laboratory. Data Sources Peer-reviewed literature, conference proceedings, meeting presentations, and expert opinions pertaining to telemicrobiology have been evaluated. Results A number of modalities have been employed for telemicroscopy including static capture techniques, whole slide imaging, video telemicroscopy, mobile devices, and hybrid systems. Telemicrobiology has been successfully implemented for applications including routine primary diagnois, expert teleconsultation, and proficiency testing. Emerging areas include digital culture plate reading, mobile health applications and computer-augmented analysis of digital images. Conclusions Static image capture techniques to date have been the most widely used modality for telemicrobiology, despite the fact that other newer technologies are available and may produce better quality interpretations. Increased adoption of telemicrobiology offers added value, quality, and efficiency to the clinical microbiology laboratory. PMID:26317376

Analysis and compensation for the effect of the catheter position on image intensities in intravascular optical coherence tomography

NASA Astrophysics Data System (ADS)

Liu, Shengnan; Eggermont, Jeroen; Wolterbeek, Ron; Broersen, Alexander; Busk, Carol A. G. R.; Precht, Helle; Lelieveldt, Boudewijn P. F.; Dijkstra, Jouke

2016-12-01

Intravascular optical coherence tomography (IVOCT) is an imaging technique that is used to analyze the underlying cause of cardiovascular disease. Because a catheter is used during imaging, the intensities can be affected by the catheter position. This work aims to analyze the effect of the catheter position on IVOCT image intensities and to propose a compensation method to minimize this effect in order to improve the visualization and the automatic analysis of IVOCT images. The effect of catheter position is modeled with respect to the distance between the catheter and the arterial wall (distance-dependent factor) and the incident angle onto the arterial wall (angle-dependent factor). A light transmission model incorporating both factors is introduced. On the basis of this model, the interaction effect of both factors is estimated with a hierarchical multivariant linear regression model. Statistical analysis shows that IVOCT intensities are significantly affected by both factors with p<0.001, as either aspect increases the intensity decreases. This effect differs for different pullbacks. The regression results were used to compensate for this effect. Experiments show that the proposed compensation method can improve the performance of the automatic bioresorbable vascular scaffold strut detection.

Heuristic Analysis Model of Nitrided Layers’ Formation Consisting of the Image Processing and Analysis and Elements of Artificial Intelligence

PubMed Central

Wójcicki, Tomasz; Nowicki, Michał

2016-01-01

The article presents a selected area of research and development concerning the methods of material analysis based on the automatic image recognition of the investigated metallographic sections. The objectives of the analyses of the materials for gas nitriding technology are described. The methods of the preparation of nitrided layers, the steps of the process and the construction and operation of devices for gas nitriding are given. We discuss the possibility of using the methods of digital images processing in the analysis of the materials, as well as their essential task groups: improving the quality of the images, segmentation, morphological transformations and image recognition. The developed analysis model of the nitrided layers formation, covering image processing and analysis techniques, as well as selected methods of artificial intelligence are presented. The model is divided into stages, which are formalized in order to better reproduce their actions. The validation of the presented method is performed. The advantages and limitations of the developed solution, as well as the possibilities of its practical use, are listed. PMID:28773389

Robust Short-Lag Spatial Coherence Imaging.

PubMed

Nair, Arun Asokan; Tran, Trac Duy; Bell, Muyinatu A Lediju

2018-03-01

Short-lag spatial coherence (SLSC) imaging displays the spatial coherence between backscattered ultrasound echoes instead of their signal amplitudes and is more robust to noise and clutter artifacts when compared with traditional delay-and-sum (DAS) B-mode imaging. However, SLSC imaging does not consider the content of images formed with different lags, and thus does not exploit the differences in tissue texture at each short-lag value. Our proposed method improves SLSC imaging by weighting the addition of lag values (i.e., M-weighting) and by applying robust principal component analysis (RPCA) to search for a low-dimensional subspace for projecting coherence images created with different lag values. The RPCA-based projections are considered to be denoised versions of the originals that are then weighted and added across lags to yield a final robust SLSC (R-SLSC) image. Our approach was tested on simulation, phantom, and in vivo liver data. Relative to DAS B-mode images, the mean contrast, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) improvements with R-SLSC images are 21.22 dB, 2.54, and 2.36, respectively, when averaged over simulated, phantom, and in vivo data and over all lags considered, which corresponds to mean improvements of 96.4%, 121.2%, and 120.5%, respectively. When compared with SLSC images, the corresponding mean improvements with R-SLSC images were 7.38 dB, 1.52, and 1.30, respectively (i.e., mean improvements of 14.5%, 50.5%, and 43.2%, respectively). Results show great promise for smoothing out the tissue texture of SLSC images and enhancing anechoic or hypoechoic target visibility at higher lag values, which could be useful in clinical tasks such as breast cyst visualization, liver vessel tracking, and obese patient imaging.

To boldly glow ... applications of laser scanning confocal microscopy in developmental biology.

PubMed

Paddock, S W

1994-05-01

The laser scanning confocal microscope (LSCM) is now established as an invaluable tool in developmental biology for improved light microscope imaging of fluorescently labelled eggs, embryos and developing tissues. The universal application of the LSCM in biomedical research has stimulated improvements to the microscopes themselves and the synthesis of novel probes for imaging biological structures and physiological processes. Moreover the ability of the LSCM to produce an optical series in perfect register has made computer 3-D reconstruction and analysis of light microscope images a practical option.

Breast cancer histopathology image analysis: a review.

PubMed

Veta, Mitko; Pluim, Josien P W; van Diest, Paul J; Viergever, Max A

2014-05-01

This paper presents an overview of methods that have been proposed for the analysis of breast cancer histopathology images. This research area has become particularly relevant with the advent of whole slide imaging (WSI) scanners, which can perform cost-effective and high-throughput histopathology slide digitization, and which aim at replacing the optical microscope as the primary tool used by pathologist. Breast cancer is the most prevalent form of cancers among women, and image analysis methods that target this disease have a huge potential to reduce the workload in a typical pathology lab and to improve the quality of the interpretation. This paper is meant as an introduction for nonexperts. It starts with an overview of the tissue preparation, staining and slide digitization processes followed by a discussion of the different image processing techniques and applications, ranging from analysis of tissue staining to computer-aided diagnosis, and prognosis of breast cancer patients.

Single-channel stereoscopic ophthalmology microscope based on TRD

NASA Astrophysics Data System (ADS)

Radfar, Edalat; Park, Jihoon; Lee, Sangyeob; Ha, Myungjin; Yu, Sungkon; Jang, Seulki; Jung, Byungjo

2016-03-01

A stereoscopic imaging modality was developed for the application of ophthalmology surgical microscopes. A previous study has already introduced a single-channel stereoscopic video imaging modality based on a transparent rotating deflector (SSVIM-TRD), in which two different view angles, image disparity, are generated by imaging through a transparent rotating deflector (TRD) mounted on a stepping motor and is placed in a lens system. In this case, the image disparity is a function of the refractive index and the rotation angle of TRD. Real-time single-channel stereoscopic ophthalmology microscope (SSOM) based on the TRD is improved by real-time controlling and programming, imaging speed, and illumination method. Image quality assessments were performed to investigate images quality and stability during the TRD operation. Results presented little significant difference in image quality in terms of stability of structural similarity (SSIM). A subjective analysis was performed with 15 blinded observers to evaluate the depth perception improvement and presented significant improvement in the depth perception capability. Along with all evaluation results, preliminary results of rabbit eye imaging presented that the SSOM could be utilized as an ophthalmic operating microscopes to overcome some of the limitations of conventional ones.

Shadow analysis via the C+K Visioline: A technical note.

PubMed

Houser, T; Zerweck, C; Grove, G; Wickett, R

2017-11-01

This research investigated the ability of shadow analysis (via the Courage + Khazaka Visioline and Image Pro Premiere 9.0 software) to accurately assess the differences in skin topography associated with photo aging. Analyses were performed on impressions collected from a microfinish comparator scale (GAR Electroforming) as well a series of impressions collected from the crow's feet region of 9 women who represent each point on the Zerweck Crow's Feet classification scale. Analyses were performed using a Courage + Khazaka Visioline VL 650 as well as Image Pro Premiere 9.0 software. Shadow analysis showed an ability to accurately measure the groove depth when measuring impressions collected from grooves of known depth. Several shadow analysis parameters showed a correlation with the expert grader ratings of crow's feet when averaging measurements taken from the North and South directions. The Max Depth parameter in particular showed a strong correlation with the expert grader's ratings which improved when a more sophisticated analysis was performed using Image Pro Premiere. When used properly, shadow analysis is effective at accurately measuring skin surface impressions for differences in skin topography. Shadow analysis is shown to accurately assess the differences across a range of crow's feet severity correlating to a 0-8 grader scale. The Visioline VL 650 is a good tool for this measurement, with room for improvement in analysis which can be achieved through third party image analysis software. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Shading correction algorithm for cone-beam CT in radiotherapy: extensive clinical validation of image quality improvement

NASA Astrophysics Data System (ADS)

Joshi, K. D.; Marchant, T. E.; Moore, C. J.

2017-03-01

A shading correction algorithm for the improvement of cone-beam CT (CBCT) images (Phys. Med. Biol. 53 5719{33) has been further developed, optimised and validated extensively using 135 clinical CBCT images of patients undergoing radiotherapy treatment of the pelvis, lungs and head and neck. An automated technique has been developed to efficiently analyse the large number of clinical images. Small regions of similar tissue (for example fat tissue) are automatically identified using CT images. The same regions on the corresponding CBCT image are analysed to ensure that they do not contain pixels representing multiple types of tissue. The mean value of all selected pixels and the non-uniformity, defined as the median absolute deviation of the mean values in each small region, are calculated. Comparisons between CT and raw and corrected CBCT images are then made. Analysis of fat regions in pelvis images shows an average difference in mean pixel value between CT and CBCT of 136:0 HU in raw CBCT images, which is reduced to 2:0 HU after the application of the shading correction algorithm. The average difference in non-uniformity of fat pixels is reduced from 33:7 in raw CBCT to 2:8 in shading-corrected CBCT images. Similar results are obtained in the analysis of lung and head and neck images.

Standardisation of DNA quantitation by image analysis: quality control of instrumentation.

PubMed

Puech, M; Giroud, F

1999-05-01

DNA image analysis is frequently performed in clinical practice as a prognostic tool and to improve diagnosis. The precision of prognosis and diagnosis depends on the accuracy of analysis and particularly on the quality of image analysis systems. It has been reported that image analysis systems used for DNA quantification differ widely in their characteristics (Thunissen et al.: Cytometry 27: 21-25, 1997). This induces inter-laboratory variations when the same sample is analysed in different laboratories. In microscopic image analysis, the principal instrumentation errors arise from the optical and electronic parts of systems. They bring about problems of instability, non-linearity, and shading and glare phenomena. The aim of this study is to establish tools and standardised quality control procedures for microscopic image analysis systems. Specific reference standard slides have been developed to control instability, non-linearity, shading and glare phenomena and segmentation efficiency. Some systems have been controlled with these tools and these quality control procedures. Interpretation criteria and accuracy limits of these quality control procedures are proposed according to the conclusions of a European project called PRESS project (Prototype Reference Standard Slide). Beyond these limits, tested image analysis systems are not qualified to realise precise DNA analysis. The different procedures presented in this work determine if an image analysis system is qualified to deliver sufficiently precise DNA measurements for cancer case analysis. If the controlled systems are beyond the defined limits, some recommendations are given to find a solution to the problem.

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.

SU-E-J-59: Feasibility of Markerless Tumor Tracking by Sequential Dual-Energy Fluoroscopy On a Clinical Tumor Tracking System

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

Dhont, J; Poels, K; Verellen, D

2015-06-15

Purpose: To evaluate the feasibility of markerless tumor tracking through the implementation of a novel dual-energy imaging approach into the clinical dynamic tracking (DT) workflow of the Vero SBRT system. Methods: Two sequential 20 s (11 Hz) fluoroscopy sequences were acquired at the start of one fraction for 7 patients treated for primary and metastatic lung cancer with DT on the Vero system. Sequences were acquired using 2 on-board kV imaging systems located at ±45° from the MV beam axis, at respectively 60 kVp (3.2 mAs) and 120 kVp (2.0 mAs). Offline, a normalized cross-correlation algorithm was applied to matchmore » the high (HE) and low energy (LE) images. Per breathing phase (inhale, exhale, maximum inhale and maximum exhale), the 5 best-matching HE and LE couples were extracted for DE subtraction. A contrast analysis according to gross tumor volume was conducted based on contrast-to-noise ratio (CNR). Improved tumor visibility was quantified using an improvement ratio. Results: Using the implanted fiducial as a benchmark, HE-LE sequence matching was effective for 13 out of 14 imaging angles. Overlying bony anatomy was removed on all DE images. With the exception of two imaging angles, the DE images showed no significantly improved tumor visibility compared to HE images, with an improvement ratio averaged over all patients of 1.46 ± 1.64. Qualitatively, it was observed that for those imaging angles that showed no significantly improved CNR, the tumor tissue could not be reliably visualized on neither HE nor DE images due to a total or partial overlap with other soft tissue. Conclusion: Dual-energy subtraction imaging by sequential orthogonal fluoroscopy was shown feasible by implementing an additional LE fluoroscopy sequence. However, for most imaging angles, DE images did not provide improved tumor visibility over single-energy images. Optimizing imaging angles is likely to improve tumor visibility and the efficacy of dual-energy imaging. This work was in part sponsored by corporate funding from BrainLAB AG.(BrainLAB AG, Feldkirchen, Germany)« less

Book Review: Reiner Salzer and Heinz W. Siesler (Eds.): Infrared and Raman spectroscopic imaging, 2nd ed.

DOE PAGES

Moore, David Steven

2015-05-10

This second edition of "Infrared and Raman Spectroscopic Imaging" propels practitioners in that wide-ranging field, as well as other readers, to the current state of the art in a well-produced and full-color, completely revised and updated, volume. This new edition chronicles the expanded application of vibrational spectroscopic imaging from yesterday's time-consuming point-by-point buildup of a hyperspectral image cube, through the improvements afforded by the addition of focal plane arrays and line scan imaging, to methods applicable beyond the diffraction limit, instructs the reader on the improved instrumentation and image and data analysis methods, and expounds on their application to fundamentalmore » biomedical knowledge, food and agricultural surveys, materials science, process and quality control, and many others.« less

Improved Denoising via Poisson Mixture Modeling of Image Sensor Noise.

PubMed

Zhang, Jiachao; Hirakawa, Keigo

2017-04-01

This paper describes a study aimed at comparing the real image sensor noise distribution to the models of noise often assumed in image denoising designs. A quantile analysis in pixel, wavelet transform, and variance stabilization domains reveal that the tails of Poisson, signal-dependent Gaussian, and Poisson-Gaussian models are too short to capture real sensor noise behavior. A new Poisson mixture noise model is proposed to correct the mismatch of tail behavior. Based on the fact that noise model mismatch results in image denoising that undersmoothes real sensor data, we propose a mixture of Poisson denoising method to remove the denoising artifacts without affecting image details, such as edge and textures. Experiments with real sensor data verify that denoising for real image sensor data is indeed improved by this new technique.

Movement Correction Method for Human Brain PET Images: Application to Quantitative Analysis of Dynamic [18F]-FDDNP Scans

PubMed Central

Wardak, Mirwais; Wong, Koon-Pong; Shao, Weber; Dahlbom, Magnus; Kepe, Vladimir; Satyamurthy, Nagichettiar; Small, Gary W.; Barrio, Jorge R.; Huang, Sung-Cheng

2010-01-01

Head movement during a PET scan (especially, dynamic scan) can affect both the qualitative and quantitative aspects of an image, making it difficult to accurately interpret the results. The primary objective of this study was to develop a retrospective image-based movement correction (MC) method and evaluate its implementation on dynamic [18F]-FDDNP PET images of cognitively intact controls and patients with Alzheimer’s disease (AD). Methods Dynamic [18F]-FDDNP PET images, used for in vivo imaging of beta-amyloid plaques and neurofibrillary tangles, were obtained from 12 AD and 9 age-matched controls. For each study, a transmission scan was first acquired for attenuation correction. An accurate retrospective MC method that corrected for transmission-emission misalignment as well as emission-emission misalignment was applied to all studies. No restriction was assumed for zero movement between the transmission scan and first emission scan. Logan analysis with cerebellum as the reference region was used to estimate various regional distribution volume ratio (DVR) values in the brain before and after MC. Discriminant analysis was used to build a predictive model for group membership, using data with and without MC. Results MC improved the image quality and quantitative values in [18F]-FDDNP PET images. In this subject population, medial temporal (MTL) did not show a significant difference between controls and AD before MC. However, after MC, significant differences in DVR values were seen in frontal, parietal, posterior cingulate (PCG), MTL, lateral temporal (LTL), and global between the two groups (P < 0.05). In controls and AD, the variability of regional DVR values (as measured by the coefficient of variation) decreased on average by >18% after MC. Mean DVR separation between controls and ADs was higher in frontal, MTL, LTL and global after MC. Group classification by discriminant analysis based on [18F]-FDDNP DVR values was markedly improved after MC. Conclusion The streamlined and easy to use MC method presented in this work significantly improves the image quality and the measured tracer kinetics of [18F]-FDDNP PET images. The proposed MC method has the potential to be applied to PET studies on patients having other disorders (e.g., Down syndrome and Parkinson’s disease) and to brain PET scans with other molecular imaging probes. PMID:20080894

Whole-tumor histogram analysis of the cerebral blood volume map: tumor volume defined by 11C-methionine positron emission tomography image improves the diagnostic accuracy of cerebral glioma grading.

PubMed

Wu, Rongli; Watanabe, Yoshiyuki; Arisawa, Atsuko; Takahashi, Hiroto; Tanaka, Hisashi; Fujimoto, Yasunori; Watabe, Tadashi; Isohashi, Kayako; Hatazawa, Jun; Tomiyama, Noriyuki

2017-10-01

This study aimed to compare the tumor volume definition using conventional magnetic resonance (MR) and 11C-methionine positron emission tomography (MET/PET) images in the differentiation of the pre-operative glioma grade by using whole-tumor histogram analysis of normalized cerebral blood volume (nCBV) maps. Thirty-four patients with histopathologically proven primary brain low-grade gliomas (n = 15) and high-grade gliomas (n = 19) underwent pre-operative or pre-biopsy MET/PET, fluid-attenuated inversion recovery, dynamic susceptibility contrast perfusion-weighted magnetic resonance imaging, and contrast-enhanced T1-weighted at 3.0 T. The histogram distribution derived from the nCBV maps was obtained by co-registering the whole tumor volume delineated on conventional MR or MET/PET images, and eight histogram parameters were assessed. The mean nCBV value had the highest AUC value (0.906) based on MET/PET images. Diagnostic accuracy significantly improved when the tumor volume was measured from MET/PET images compared with conventional MR images for the parameters of mean, 50th, and 75th percentile nCBV value (p = 0.0246, 0.0223, and 0.0150, respectively). Whole-tumor histogram analysis of CBV map provides more valuable histogram parameters and increases diagnostic accuracy in the differentiation of pre-operative cerebral gliomas when the tumor volume is derived from MET/PET images.

Quantitative imaging biomarkers: the application of advanced image processing and analysis to clinical and preclinical decision making.

PubMed

Prescott, Jeffrey William

2013-02-01

The importance of medical imaging for clinical decision making has been steadily increasing over the last four decades. Recently, there has also been an emphasis on medical imaging for preclinical decision making, i.e., for use in pharamaceutical and medical device development. There is also a drive towards quantification of imaging findings by using quantitative imaging biomarkers, which can improve sensitivity, specificity, accuracy and reproducibility of imaged characteristics used for diagnostic and therapeutic decisions. An important component of the discovery, characterization, validation and application of quantitative imaging biomarkers is the extraction of information and meaning from images through image processing and subsequent analysis. However, many advanced image processing and analysis methods are not applied directly to questions of clinical interest, i.e., for diagnostic and therapeutic decision making, which is a consideration that should be closely linked to the development of such algorithms. This article is meant to address these concerns. First, quantitative imaging biomarkers are introduced by providing definitions and concepts. Then, potential applications of advanced image processing and analysis to areas of quantitative imaging biomarker research are described; specifically, research into osteoarthritis (OA), Alzheimer's disease (AD) and cancer is presented. Then, challenges in quantitative imaging biomarker research are discussed. Finally, a conceptual framework for integrating clinical and preclinical considerations into the development of quantitative imaging biomarkers and their computer-assisted methods of extraction is presented.

[Development of an original computer program FISHMet: use for molecular cytogenetic diagnosis and genome mapping by fluorescent in situ hybridization (FISH)].

PubMed

Iurov, Iu B; Khazatskiĭ, I A; Akindinov, V A; Dovgilov, L V; Kobrinskiĭ, B A; Vorsanova, S G

2000-08-01

Original software FISHMet has been developed and tried for improving the efficiency of diagnosis of hereditary diseases caused by chromosome aberrations and for chromosome mapping by fluorescent in situ hybridization (FISH) method. The program allows creation and analysis of pseudocolor chromosome images and hybridization signals in the Windows 95 system, allows computer analysis and editing of the results of pseudocolor hybridization in situ, including successive imposition of initial black-and-white images created using fluorescent filters (blue, green, and red), and editing of each image individually or of a summary pseudocolor image in BMP, TIFF, and JPEG formats. Components of image computer analysis system (LOMO, Leitz Ortoplan, and Axioplan fluorescent microscopes, COHU 4910 and Sanyo VCB-3512P CCD cameras, Miro-Video, Scion LG-3 and VG-5 image capture maps, and Pentium 100 and Pentium 200 computers) and specialized software for image capture and visualization (Scion Image PC and Video-Cup) have been used with good results in the study.

A light and faster regional convolutional neural network for object detection in optical remote sensing images

NASA Astrophysics Data System (ADS)

Ding, Peng; Zhang, Ye; Deng, Wei-Jian; Jia, Ping; Kuijper, Arjan

2018-07-01

Detection of objects from satellite optical remote sensing images is very important for many commercial and governmental applications. With the development of deep convolutional neural networks (deep CNNs), the field of object detection has seen tremendous advances. Currently, objects in satellite remote sensing images can be detected using deep CNNs. In general, optical remote sensing images contain many dense and small objects, and the use of the original Faster Regional CNN framework does not yield a suitably high precision. Therefore, after careful analysis we adopt dense convoluted networks, a multi-scale representation and various combinations of improvement schemes to enhance the structure of the base VGG16-Net for improving the precision. We propose an approach to reduce the test-time (detection time) and memory requirements. To validate the effectiveness of our approach, we perform experiments using satellite remote sensing image datasets of aircraft and automobiles. The results show that the improved network structure can detect objects in satellite optical remote sensing images more accurately and efficiently.

MULTISCALE TENSOR ANISOTROPIC FILTERING OF FLUORESCENCE MICROSCOPY FOR DENOISING MICROVASCULATURE.

PubMed

Prasath, V B S; Pelapur, R; Glinskii, O V; Glinsky, V V; Huxley, V H; Palaniappan, K

2015-04-01

Fluorescence microscopy images are contaminated by noise and improving image quality without blurring vascular structures by filtering is an important step in automatic image analysis. The application of interest here is to automatically extract the structural components of the microvascular system with accuracy from images acquired by fluorescence microscopy. A robust denoising process is necessary in order to extract accurate vascular morphology information. For this purpose, we propose a multiscale tensor with anisotropic diffusion model which progressively and adaptively updates the amount of smoothing while preserving vessel boundaries accurately. Based on a coherency enhancing flow with planar confidence measure and fused 3D structure information, our method integrates multiple scales for microvasculature preservation and noise removal membrane structures. Experimental results on simulated synthetic images and epifluorescence images show the advantage of our improvement over other related diffusion filters. We further show that the proposed multiscale integration approach improves denoising accuracy of different tensor diffusion methods to obtain better microvasculature segmentation.

Image quality in thoracic 4D cone-beam CT: A sensitivity analysis of respiratory signal, binning method, reconstruction algorithm, and projection angular spacing

PubMed Central

Shieh, Chun-Chien; Kipritidis, John; O’Brien, Ricky T.; Kuncic, Zdenka; Keall, Paul J.

2014-01-01

Purpose: Respiratory signal, binning method, and reconstruction algorithm are three major controllable factors affecting image quality in thoracic 4D cone-beam CT (4D-CBCT), which is widely used in image guided radiotherapy (IGRT). Previous studies have investigated each of these factors individually, but no integrated sensitivity analysis has been performed. In addition, projection angular spacing is also a key factor in reconstruction, but how it affects image quality is not obvious. An investigation of the impacts of these four factors on image quality can help determine the most effective strategy in improving 4D-CBCT for IGRT. Methods: Fourteen 4D-CBCT patient projection datasets with various respiratory motion features were reconstructed with the following controllable factors: (i) respiratory signal (real-time position management, projection image intensity analysis, or fiducial marker tracking), (ii) binning method (phase, displacement, or equal-projection-density displacement binning), and (iii) reconstruction algorithm [Feldkamp–Davis–Kress (FDK), McKinnon–Bates (MKB), or adaptive-steepest-descent projection-onto-convex-sets (ASD-POCS)]. The image quality was quantified using signal-to-noise ratio (SNR), contrast-to-noise ratio, and edge-response width in order to assess noise/streaking and blur. The SNR values were also analyzed with respect to the maximum, mean, and root-mean-squared-error (RMSE) projection angular spacing to investigate how projection angular spacing affects image quality. Results: The choice of respiratory signals was found to have no significant impact on image quality. Displacement-based binning was found to be less prone to motion artifacts compared to phase binning in more than half of the cases, but was shown to suffer from large interbin image quality variation and large projection angular gaps. Both MKB and ASD-POCS resulted in noticeably improved image quality almost 100% of the time relative to FDK. In addition, SNR values were found to increase with decreasing RMSE values of projection angular gaps with strong correlations (r ≈ −0.7) regardless of the reconstruction algorithm used. Conclusions: Based on the authors’ results, displacement-based binning methods, better reconstruction algorithms, and the acquisition of even projection angular views are the most important factors to consider for improving thoracic 4D-CBCT image quality. In view of the practical issues with displacement-based binning and the fact that projection angular spacing is not currently directly controllable, development of better reconstruction algorithms represents the most effective strategy for improving image quality in thoracic 4D-CBCT for IGRT applications at the current stage. PMID:24694143

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

Application of Six Sigma methodology to a diagnostic imaging process.

PubMed

Taner, Mehmet Tolga; Sezen, Bulent; Atwat, Kamal M

2012-01-01

This paper aims to apply the Six Sigma methodology to improve workflow by eliminating the causes of failure in the medical imaging department of a private Turkish hospital. Implementation of the design, measure, analyse, improve and control (DMAIC) improvement cycle, workflow chart, fishbone diagrams and Pareto charts were employed, together with rigorous data collection in the department. The identification of root causes of repeat sessions and delays was followed by failure, mode and effect analysis, hazard analysis and decision tree analysis. The most frequent causes of failure were malfunction of the RIS/PACS system and improper positioning of patients. Subsequent to extensive training of professionals, the sigma level was increased from 3.5 to 4.2. The data were collected over only four months. Six Sigma's data measurement and process improvement methodology is the impetus for health care organisations to rethink their workflow and reduce malpractice. It involves measuring, recording and reporting data on a regular basis. This enables the administration to monitor workflow continuously. The improvements in the workflow under study, made by determining the failures and potential risks associated with radiologic care, will have a positive impact on society in terms of patient safety. Having eliminated repeat examinations, the risk of being exposed to more radiation was also minimised. This paper supports the need to apply Six Sigma and present an evaluation of the process in an imaging department.

A quality quantitative method of silicon direct bonding based on wavelet image analysis

NASA Astrophysics Data System (ADS)

Tan, Xiao; Tao, Zhi; Li, Haiwang; Xu, Tiantong; Yu, Mingxing

2018-04-01

The rapid development of MEMS (micro-electro-mechanical systems) has received significant attention from researchers in various fields and subjects. In particular, the MEMS fabrication process is elaborate and, as such, has been the focus of extensive research inquiries. However, in MEMS fabrication, component bonding is difficult to achieve and requires a complex approach. Thus, improvements in bonding quality are relatively important objectives. A higher quality bond can only be achieved with improved measurement and testing capabilities. In particular, the traditional testing methods mainly include infrared testing, tensile testing, and strength testing, despite the fact that using these methods to measure bond quality often results in low efficiency or destructive analysis. Therefore, this paper focuses on the development of a precise, nondestructive visual testing method based on wavelet image analysis that is shown to be highly effective in practice. The process of wavelet image analysis includes wavelet image denoising, wavelet image enhancement, and contrast enhancement, and as an end result, can display an image with low background noise. In addition, because the wavelet analysis software was developed with MATLAB, it can reveal the bonding boundaries and bonding rates to precisely indicate the bond quality at all locations on the wafer. This work also presents a set of orthogonal experiments that consist of three prebonding factors, the prebonding temperature, the positive pressure value and the prebonding time, which are used to analyze the prebonding quality. This method was used to quantify the quality of silicon-to-silicon wafer bonding, yielding standard treatment quantities that could be practical for large-scale use.

Techniques in helical scanning, dynamic imaging and image segmentation for improved quantitative analysis with X-ray micro-CT

NASA Astrophysics Data System (ADS)

Sheppard, Adrian; Latham, Shane; Middleton, Jill; Kingston, Andrew; Myers, Glenn; Varslot, Trond; Fogden, Andrew; Sawkins, Tim; Cruikshank, Ron; Saadatfar, Mohammad; Francois, Nicolas; Arns, Christoph; Senden, Tim

2014-04-01

This paper reports on recent advances at the micro-computed tomography facility at the Australian National University. Since 2000 this facility has been a significant centre for developments in imaging hardware and associated software for image reconstruction, image analysis and image-based modelling. In 2010 a new instrument was constructed that utilises theoretically-exact image reconstruction based on helical scanning trajectories, allowing higher cone angles and thus better utilisation of the available X-ray flux. We discuss the technical hurdles that needed to be overcome to allow imaging with cone angles in excess of 60°. We also present dynamic tomography algorithms that enable the changes between one moment and the next to be reconstructed from a sparse set of projections, allowing higher speed imaging of time-varying samples. Researchers at the facility have also created a sizeable distributed-memory image analysis toolkit with capabilities ranging from tomographic image reconstruction to 3D shape characterisation. We show results from image registration and present some of the new imaging and experimental techniques that it enables. Finally, we discuss the crucial question of image segmentation and evaluate some recently proposed techniques for automated segmentation.

Three-Dimensional Anatomic Evaluation of the Anterior Cruciate Ligament for Planning Reconstruction

PubMed Central

Hoshino, Yuichi; Kim, Donghwi; Fu, Freddie H.

2012-01-01

Anatomic study related to the anterior cruciate ligament (ACL) reconstruction surgery has been developed in accordance with the progress of imaging technology. Advances in imaging techniques, especially the move from two-dimensional (2D) to three-dimensional (3D) image analysis, substantially contribute to anatomic understanding and its application to advanced ACL reconstruction surgery. This paper introduces previous research about image analysis of the ACL anatomy and its application to ACL reconstruction surgery. Crucial bony landmarks for the accurate placement of the ACL graft can be identified by 3D imaging technique. Additionally, 3D-CT analysis of the ACL insertion site anatomy provides better and more consistent evaluation than conventional “clock-face” reference and roentgenologic quadrant method. Since the human anatomy has a complex three-dimensional structure, further anatomic research using three-dimensional imaging analysis and its clinical application by navigation system or other technologies is warranted for the improvement of the ACL reconstruction. PMID:22567310

Ventilation/perfusion SPECT or SPECT/CT for lung function imaging in patients with pulmonary emphysema?

PubMed

Froeling, Vera; Heimann, Uwe; Huebner, Ralf-Harto; Kroencke, Thomas J; Maurer, Martin H; Doellinger, Felix; Geisel, Dominik; Hamm, Bernd; Brenner, Winfried; Schreiter, Nils F

2015-07-01

To evaluate the utility of attenuation correction (AC) of V/P SPECT images for patients with pulmonary emphysema. Twenty-one patients (mean age 67.6 years) with pulmonary emphysema who underwent V/P SPECT/CT were included. AC/non-AC V/P SPECT images were compared visually and semiquantitatively. Visual comparison of AC/non-AC images was based on a 5-point likert scale. Semiquantitative comparison assessed absolute counts per lung (aCpLu) and lung lobe (aCpLo) for AC/non-AC images using software-based analysis; percentage counts (PC = (aCpLo/aCpLu) × 100) were calculated. Correlation between AC/non-AC V/P SPECT images was analyzed using Spearman's rho correlation coefficient; differences were tested for significance with the Wilcoxon rank sum test. Visual analysis revealed high conformity for AC and non-AC V/P SPECT images. Semiquantitative analysis of PC in AC/non-AC images had an excellent correlation and showed no significant differences in perfusion (ρ = 0.986) or ventilation (ρ = 0.979, p = 0.809) SPECT/CT images. AC of V/P SPECT images for lung lobe-based function imaging in patients with pulmonary emphysema do not improve visual or semiquantitative image analysis.

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.

Energy weighting improves dose efficiency in clinical practice: implementation on a spectral photon-counting mammography system

PubMed Central

Berglund, Johan; Johansson, Henrik; Lundqvist, Mats; Cederström, Björn; Fredenberg, Erik

2014-01-01

Abstract. In x-ray imaging, contrast information content varies with photon energy. It is, therefore, possible to improve image quality by weighting photons according to energy. We have implemented and evaluated so-called energy weighting on a commercially available spectral photon-counting mammography system. The technique was evaluated using computer simulations, phantom experiments, and analysis of screening mammograms. The CNR benefit of energy weighting for a number of relevant target-background combinations measured by the three methods fell in the range of 2.2 to 5.2% when using optimal weight factors. This translates to a potential dose reduction at constant CNR in the range of 4.5 to 11%. We expect the choice of weight factor in practical implementations to be straightforward because (1) the CNR improvement was not very sensitive to weight, (2) the optimal weight was similar for all investigated target-background combinations, (3) aluminum/PMMA phantoms were found to represent clinically relevant tasks well, and (4) the optimal weight could be calculated directly from pixel values in phantom images. Reasonable agreement was found between the simulations and phantom measurements. Manual measurements on microcalcifications and automatic image analysis confirmed that the CNR improvement was detectable in energy-weighted screening mammograms. PMID:26158045

Segmenting Images for a Better Diagnosis

NASA Technical Reports Server (NTRS)

2004-01-01

NASA's Hierarchical Segmentation (HSEG) software has been adapted by Bartron Medical Imaging, LLC, for use in segmentation feature extraction, pattern recognition, and classification of medical images. Bartron acquired licenses from NASA Goddard Space Flight Center for application of the HSEG concept to medical imaging, from the California Institute of Technology/Jet Propulsion Laboratory to incorporate pattern-matching software, and from Kennedy Space Center for data-mining and edge-detection programs. The Med-Seg[TM] united developed by Bartron provides improved diagnoses for a wide range of medical images, including computed tomography scans, positron emission tomography scans, magnetic resonance imaging, ultrasound, digitized Z-ray, digitized mammography, dental X-ray, soft tissue analysis, and moving object analysis. It also can be used in analysis of soft-tissue slides. Bartron's future plans include the application of HSEG technology to drug development. NASA is advancing it's HSEG software to learn more about the Earth's magnetosphere.

Coherent Raman Scattering Microscopy in Biology and Medicine.

PubMed

Zhang, Chi; Zhang, Delong; Cheng, Ji-Xin

2015-01-01

Advancements in coherent Raman scattering (CRS) microscopy have enabled label-free visualization and analysis of functional, endogenous biomolecules in living systems. When compared with spontaneous Raman microscopy, a key advantage of CRS microscopy is the dramatic improvement in imaging speed, which gives rise to real-time vibrational imaging of live biological samples. Using molecular vibrational signatures, recently developed hyperspectral CRS microscopy has improved the readout of chemical information available from CRS images. In this article, we review recent achievements in CRS microscopy, focusing on the theory of the CRS signal-to-noise ratio, imaging speed, technical developments, and applications of CRS imaging in bioscience and clinical settings. In addition, we present possible future directions that the use of this technology may take.

Coherent Raman Scattering Microscopy in Biology and Medicine

PubMed Central

Zhang, Chi; Zhang, Delong; Cheng, Ji-Xin

2016-01-01

Advancements in coherent Raman scattering (CRS) microscopy have enabled label-free visualization and analysis of functional, endogenous biomolecules in living systems. When compared with spontaneous Raman microscopy, a key advantage of CRS microscopy is the dramatic improvement in imaging speed, which gives rise to real-time vibrational imaging of live biological samples. Using molecular vibrational signatures, recently developed hyperspectral CRS microscopy has improved the readout of chemical information available from CRS images. In this article, we review recent achievements in CRS microscopy, focusing on the theory of the CRS signal-to-noise ratio, imaging speed, technical developments, and applications of CRS imaging in bioscience and clinical settings. In addition, we present possible future directions that the use of this technology may take. PMID:26514285

Two- and three-dimensional transvaginal ultrasound with power Doppler angiography and gel infusion sonography for diagnosis of endometrial malignancy.

PubMed

Dueholm, M; Christensen, J W; Rydbjerg, S; Hansen, E S; Ørtoft, G

2015-06-01

To evaluate the diagnostic efficiency of two-dimensional (2D) and three-dimensional (3D) transvaginal ultrasonography, power Doppler angiography (PDA) and gel infusion sonography (GIS) at offline analysis for recognition of malignant endometrium compared with real-time evaluation during scanning, and to determine optimal image parameters at 3D analysis. One hundred and sixty-nine consecutive women with postmenopausal bleeding and endometrial thickness ≥ 5 mm underwent systematic evaluation of endometrial pattern on 2D imaging, and 2D videoclips and 3D volumes were later analyzed offline. Histopathological findings at hysteroscopy or hysterectomy were used as the reference standard. The efficiency of the different techniques for diagnosis of malignancy was calculated and compared. 3D image parameters, endometrial volume and 3D vascular indices were assessed. Optimal 3D image parameters were transformed by logistic regression into a risk of endometrial cancer (REC) score, including scores for body mass index, endometrial thickness and endometrial morphology at gray-scale and PDA and GIS. Offline 2D and 3D analysis were equivalent, but had lower diagnostic performance compared with real-time evaluation during scanning. Their diagnostic performance was not markedly improved by the addition of PDA or GIS, but their efficiency was comparable with that of real-time 2D-GIS in offline examinations of good image quality. On logistic regression, the 3D parameters from the REC-score system had the highest diagnostic efficiency. The area under the curve of the REC-score system at 3D-GIS (0.89) was not improved by inclusion of vascular indices or endometrial volume calculations. Real-time evaluation during scanning is most efficient, but offline 2D and 3D analysis is useful for prediction of endometrial cancer when good image quality can be obtained. The diagnostic efficiency at 3D analysis may be improved by use of REC-scoring systems, without the need for calculation of vascular indices or endometrial volume. The optimal imaging modality appears to be real-time 2D-GIS. Copyright © 2014 ISUOG. Published by John Wiley & Sons Ltd.

The Impact of a New Speckle Holography Analysis on the Galactic Center Orbits Initiative

NASA Astrophysics Data System (ADS)

Mangian, John; Ghez, Andrea; Gautam, Abhimat; Gallego, Laly; Schödel, Rainer; Lu, Jessica; Chen, Zhuo; UCLA Galactic Center Group; W.M. Keck Observatory Staff

2018-01-01

The Galactic Center Orbit Initiative has used two decades of high angular resolution imaging data from the W. M. Keck Observatory to make astrometric measurements of stellar motion around our Galaxy's central supermassive black hole. We present an analysis of a new approach to ten years of speckle imaging data (1995 - 2005) that has been processed with a new holography analysis. This analysis has (1) improved the image quality near the edge of the combined speckle frame and (2) increased the depth of the images and therefore increased the number of sources detected throughout the entire image. By directly comparing each holography analysis, we find a 41% increase in total detected sources and a 81% increase in sources further than 3" from the central black hole (SgrA*). Further, we find a 49% increase in sources of K-band magnitude greater than the old holography limiting magnitude due to the reduction of light halos surrounding bright sources.

Rock images classification by using deep convolution neural network

NASA Astrophysics Data System (ADS)

Cheng, Guojian; Guo, Wenhui

2017-08-01

Granularity analysis is one of the most essential issues in authenticate under microscope. To improve the efficiency and accuracy of traditional manual work, an convolutional neural network based method is proposed for granularity analysis from thin section image, which chooses and extracts features from image samples while build classifier to recognize granularity of input image samples. 4800 samples from Ordos basin are used for experiments under colour spaces of HSV, YCbCr and RGB respectively. On the test dataset, the correct rate in RGB colour space is 98.5%, and it is believable in HSV and YCbCr colour space. The results show that the convolution neural network can classify the rock images with high reliability.

Projections onto Convex Sets Super-Resolution Reconstruction Based on Point Spread Function Estimation of Low-Resolution Remote Sensing Images

PubMed Central

Fan, Chong; Wu, Chaoyun; Li, Grand; Ma, Jun

2017-01-01

To solve the problem on inaccuracy when estimating the point spread function (PSF) of the ideal original image in traditional projection onto convex set (POCS) super-resolution (SR) reconstruction, this paper presents an improved POCS SR algorithm based on PSF estimation of low-resolution (LR) remote sensing images. The proposed algorithm can improve the spatial resolution of the image and benefit agricultural crop visual interpolation. The PSF of the high-resolution (HR) image is unknown in reality. Therefore, analysis of the relationship between the PSF of the HR image and the PSF of the LR image is important to estimate the PSF of the HR image by using multiple LR images. In this study, the linear relationship between the PSFs of the HR and LR images can be proven. In addition, the novel slant knife-edge method is employed, which can improve the accuracy of the PSF estimation of LR images. Finally, the proposed method is applied to reconstruct airborne digital sensor 40 (ADS40) three-line array images and the overlapped areas of two adjacent GF-2 images by embedding the estimated PSF of the HR image to the original POCS SR algorithm. Experimental results show that the proposed method yields higher quality of reconstructed images than that produced by the blind SR method and the bicubic interpolation method. PMID:28208837

Spatiotemporal image correlation analysis of blood flow in branched vessel networks of zebrafish embryos

NASA Astrophysics Data System (ADS)

Ceffa, Nicolo G.; Cesana, Ilaria; Collini, Maddalena; D'Alfonso, Laura; Carra, Silvia; Cotelli, Franco; Sironi, Laura; Chirico, Giuseppe

2017-10-01

Ramification of blood circulation is relevant in a number of physiological and pathological conditions. The oxygen exchange occurs largely in the capillary bed, and the cancer progression is closely linked to the angiogenesis around the tumor mass. Optical microscopy has made impressive improvements in in vivo imaging and dynamic studies based on correlation analysis of time stacks of images. Here, we develop and test advanced methods that allow mapping the flow fields in branched vessel networks at the resolution of 10 to 20 μm. The methods, based on the application of spatiotemporal image correlation spectroscopy and its extension to cross-correlation analysis, are applied here to the case of early stage embryos of zebrafish.

Mariner 6 and 7 picture analysis

NASA Technical Reports Server (NTRS)

Leighton, R. B.

1975-01-01

Analysis of Mariner 6 and 7 far-encounter (FE) pictures is discussed. The purpose of the studies was to devise ways to combine digital data from the full set of FE pictures so as to improve surface resolution, distinguish clouds and haze patches from permanent surface topographic markings, deduce improved values for radius, oblateness, and spin-axis orientation, and produce a composite photographic map of Mars. Attempts to measure and correct camera distortions, locate each image in the frame, and convert image coordinates to martian surface coordinates were highly successful; residual uncertainties in location were considerably less than one pixel. However, analysis of the data to improve the radius, figure, and axial tilt and to produce a composite map was curtailed because of the superior data provided by Mariner 9. The data, programs, and intermediate results are still available (1976), and the project could be resumed with little difficulty.

Optimally weighted least-squares steganalysis

NASA Astrophysics Data System (ADS)

Ker, Andrew D.

2007-02-01

Quantitative steganalysis aims to estimate the amount of payload in a stego object, and such estimators seem to arise naturally in steganalysis of Least Significant Bit (LSB) replacement in digital images. However, as with all steganalysis, the estimators are subject to errors, and their magnitude seems heavily dependent on properties of the cover. In very recent work we have given the first derivation of estimation error, for a certain method of steganalysis (the Least-Squares variant of Sample Pairs Analysis) of LSB replacement steganography in digital images. In this paper we make use of our theoretical results to find an improved estimator and detector. We also extend the theoretical analysis to another (more accurate) steganalysis estimator (Triples Analysis) and hence derive an improved version of that estimator too. Experimental results show that the new steganalyzers have improved accuracy, particularly in the difficult case of never-compressed covers.

Melanoma Diagnosis

NASA Astrophysics Data System (ADS)

Horsch, Alexander

The chapter deals with the diagnosis of the malignant melanoma of the skin. This aggressive type of cancer with steadily growing incidence in white populations can hundred percent be cured if it is detected in an early stage. Imaging techniques, in particular dermoscopy, have contributed significantly to improvement of diagnostic accuracy in clinical settings, achieving sensitivities for melanoma experts of beyond 95% at specificities of 90% and more. Automatic computer analysis of dermoscopy images has, in preliminary studies, achieved classification rates comparable to those of experts. However, the diagnosis of melanoma requires a lot of training and experience, and at the time being, average numbers of lesions excised per histology-proven melanoma are around 30, a number which clearly is too high. Further improvements in computer dermoscopy systems and their competent use in clinical settings certainly have the potential to support efforts of improving this situation. In the chapter, medical basics, current state of melanoma diagnosis, image analysis methods, commercial dermoscopy systems, evaluation of systems, and methods and future directions are presented.

Small lung cancers: improved detection by use of bone suppression imaging--comparison with dual-energy subtraction chest radiography.

PubMed

Li, Feng; Engelmann, Roger; Pesce, Lorenzo L; Doi, Kunio; Metz, Charles E; Macmahon, Heber

2011-12-01

To determine whether use of bone suppression (BS) imaging, used together with a standard radiograph, could improve radiologists' performance for detection of small lung cancers compared with use of standard chest radiographs alone and whether BS imaging would provide accuracy equivalent to that of dual-energy subtraction (DES) radiography. Institutional review board approval was obtained. The requirement for informed consent was waived. The study was HIPAA compliant. Standard and DES chest radiographs of 50 patients with 55 confirmed primary nodular cancers (mean diameter, 20 mm) as well as 30 patients without cancers were included in the observer study. A new BS imaging processing system that can suppress the conspicuity of bones was applied to the standard radiographs to create corresponding BS images. Ten observers, including six experienced radiologists and four radiology residents, indicated their confidence levels regarding the presence or absence of a lung cancer for each lung, first by using a standard image, then a BS image, and finally DES soft-tissue and bone images. Receiver operating characteristic (ROC) analysis was used to evaluate observer performance. The average area under the ROC curve (AUC) for all observers was significantly improved from 0.807 to 0.867 with BS imaging and to 0.916 with DES (both P < .001). The average AUC for the six experienced radiologists was significantly improved from 0.846 with standard images to 0.894 with BS images (P < .001) and from 0.894 to 0.945 with DES images (P = .001). Use of BS imaging together with a standard radiograph can improve radiologists' accuracy for detection of small lung cancers on chest radiographs. Further improvements can be achieved by use of DES radiography but with the requirement for special equipment and a potential small increase in radiation dose. © RSNA, 2011.

Formation of parametric images using mixed-effects models: a feasibility study.

PubMed

Huang, Husan-Ming; Shih, Yi-Yu; Lin, Chieh

2016-03-01

Mixed-effects models have been widely used in the analysis of longitudinal data. By presenting the parameters as a combination of fixed effects and random effects, mixed-effects models incorporating both within- and between-subject variations are capable of improving parameter estimation. In this work, we demonstrate the feasibility of using a non-linear mixed-effects (NLME) approach for generating parametric images from medical imaging data of a single study. By assuming that all voxels in the image are independent, we used simulation and animal data to evaluate whether NLME can improve the voxel-wise parameter estimation. For testing purposes, intravoxel incoherent motion (IVIM) diffusion parameters including perfusion fraction, pseudo-diffusion coefficient and true diffusion coefficient were estimated using diffusion-weighted MR images and NLME through fitting the IVIM model. The conventional method of non-linear least squares (NLLS) was used as the standard approach for comparison of the resulted parametric images. In the simulated data, NLME provides more accurate and precise estimates of diffusion parameters compared with NLLS. Similarly, we found that NLME has the ability to improve the signal-to-noise ratio of parametric images obtained from rat brain data. These data have shown that it is feasible to apply NLME in parametric image generation, and the parametric image quality can be accordingly improved with the use of NLME. With the flexibility to be adapted to other models or modalities, NLME may become a useful tool to improve the parametric image quality in the future. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Geodesic topological analysis of trabecular bone microarchitecture from high-spatial resolution magnetic resonance images.

PubMed

Carballido-Gamio, Julio; Krug, Roland; Huber, Markus B; Hyun, Ben; Eckstein, Felix; Majumdar, Sharmila; Link, Thomas M

2009-02-01

In vivo assessment of trabecular bone microarchitecture could improve the prediction of fracture risk and the efficacy of osteoporosis treatment and prevention. Geodesic topological analysis (GTA) is introduced as a novel technique to quantify the trabecular bone microarchitecture from high-spatial resolution magnetic resonance (MR) images. Trabecular bone parameters that quantify the scale, topology, and anisotropy of the trabecular bone network in terms of its junctions are the result of GTA. The reproducibility of GTA was tested with in vivo images of human distal tibiae and radii (n = 6) at 1.5 Tesla; and its ability to discriminate between subjects with and without vertebral fracture was assessed with ex vivo images of human calcanei at 1.5 and 3.0 Tesla (n = 30). GTA parameters yielded an average reproducibility of 4.8%, and their individual areas under the curve (AUC) of the receiver operating characteristic curve analysis for fracture discrimination performed better at 3.0 than at 1.5 Tesla reaching values of up to 0.78 (p < 0.001). Logistic regression analysis demonstrated that fracture discrimination was improved by combining GTA parameters, and that GTA combined with bone mineral density (BMD) allow for better discrimination than BMD alone (AUC = 0.95; p < 0.001). Results indicate that GTA can substantially contribute in studies of osteoporosis involving imaging of the trabecular bone microarchitecture. Copyright 2009 Wiley-Liss, Inc.

White matter microstructure and developmental improvement of hyperactive/impulsive symptoms in attention-deficit/hyperactivity disorder.

PubMed

Francx, Winke; Zwiers, Marcel P; Mennes, Maarten; Oosterlaan, Jaap; Heslenfeld, Dirk; Hoekstra, Pieter J; Hartman, Catharina A; Franke, Barbara; Faraone, Stephen V; O'Dwyer, Laurence; Buitelaar, Jan K

2015-12-01

A developmental improvement of symptoms in attention-deficit/hyperactivity disorder (ADHD) is frequently reported, but the underlying neurobiological substrate has not been identified. The aim of this study was to determine whether white matter microstructure is related to developmental improvement of ADHD symptoms. A cross-sectional magnetic resonance imaging (MRI) analysis was embedded in a prospective follow-up of an adolescent cohort of ADHD and control subjects (NeuroIMAGE). Mean age at baseline was 11.9 years, mean interval of follow-up was 5.9 years. About 75.3% of the original cohort was retained successfully. Data of 101 participants with ADHD combined type at baseline and 40 healthy controls were analysed. ADHD symptoms were measured with semistructured, investigator-based interviews and Conners' questionnaires, on the basis of DSM-IV criteria. Fractional anisotropy (FA) and mean diffusivity (MD) indices of white matter microstructure were measured using whole brain diffusion tensor imaging at follow-up only. In a dimensional analysis FA and MD were related to change in ADHD symptoms. To link this analysis to DSM-IV diagnoses, a post hoc categorical group analysis was conducted comparing participants with persistent (n = 59) versus remittent (n = 42) ADHD and controls. Over time, participants with ADHD showed improvement mainly in hyperactive/impulsive symptoms. This improvement was associated with lower FA and higher MD values in the left corticospinal tract at follow-up. Findings of the dimensional and the categorical analysis strongly converged. Changes in inattentive symptoms over time were minimal and not related to white matter microstructure. The corticospinal tract is important in the control of voluntary movements, suggesting the importance of the motor system in the persistence of hyperactive/impulsive symptoms. © 2015 Association for Child and Adolescent Mental Health.

White matter microstructure and developmental improvement of hyperactive/impulsive symptoms in Attention-Deficit/Hyperactivity Disorder

PubMed Central

Francx, Winke; Zwiers, Marcel P.; Mennes, Maarten; Oosterlaan, Jaap; Heslenfeld, Dirk; Hoekstra, Pieter J.; Hartman, Catharina A.; Franke, Barbara; Faraone, Stephen V.; O’Dwyer, Laurence; Buitelaar, Jan K.

2014-01-01

Background A developmental improvement of symptoms in Attention-Deficit/Hyperactivity Disorder (ADHD) is frequently reported, but the underlying neurobiological substrate has not been identified. The aim of this study was to determine whether white matter microstructure is related to developmental improvement of ADHD symptoms. Methods A cross-sectional Magnetic Resonance Imaging (MRI) analysis was embedded in a prospective follow-up of an adolescent cohort of ADHD and control subjects (NeuroIMAGE). Mean age at baseline was 11.9 years, mean interval of follow-up was 5.9 years. 75.3% of the original cohort was retained successfully. Data of 101 participants with ADHD combined type at baseline and 40 healthy controls was analysed. ADHD symptoms were measured with semi-structured, investigator-based interviews and Conners' questionnaires, on the basis of DSM-IV criteria. Fractional anisotropy (FA) and mean diffusivity (MD) indices of white matter microstructure were measured using whole brain diffusion tensor imaging at follow-up only. In a dimensional analysis FA and MD were related to change in ADHD symptoms. To link this analysis to DSM-IV diagnoses, a post-hoc categorical group analysis was conducted comparing participants with persistent (n=59) versus remittent (n=42) ADHD and controls. Results Over time, participants with ADHD showed improvement mainly in hyperactive/impulsive symptoms. This improvement was associated with lower FA and higher MD values in the left corticospinal tract at follow-up. Findings of the dimensional and the categorical analysis strongly converged. Changes in inattentive symptoms over time were minimal and not related to white matter microstructure. Conclusions The corticospinal tract is important in the control of voluntary movements, suggesting the importance of the motor system in the persistence of hyperactive/impulsive symptoms. PMID:25581343

Diagnostic Performance of Mammographic Texture Analysis in the Differential Diagnosis of Benign and Malignant Breast Tumors.

PubMed

Li, Zhiming; Yu, Lan; Wang, Xin; Yu, Haiyang; Gao, Yuanxiang; Ren, Yande; Wang, Gang; Zhou, Xiaoming

2017-11-09

The purpose of this study was to investigate the diagnostic performance of mammographic texture analysis in the differential diagnosis of benign and malignant breast tumors. Digital mammography images were obtained from the Picture Archiving and Communication System at our institute. Texture features of mammographic images were calculated. Mann-Whitney U test was used to identify differences between the benign and malignant group. The receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic performance of texture features. Significant differences of texture features of histogram, gray-level co-occurrence matrix (GLCM) and run length matrix (RLM) were found between the benign and malignant breast group (P < .05). The area under the ROC (AUROC) of histogram, GLCM, and RLM were 0.800, 0.787, and 0.761, with no differences between them (P > .05). The AUROCs of imaging-based diagnosis, texture analysis, and imaging-based diagnosis combined with texture analysis were 0.873, 0.863, and 0.961, respectively. When imaging-based diagnosis was combined with texture analysis, the AUROC was higher than that of imaging-based diagnosis or texture analysis (P < .05). Mammographic texture analysis is a reliable technique for differential diagnosis of benign and malignant breast tumors. Furthermore, the combination of imaging-based diagnosis and texture analysis can significantly improve diagnostic performance. Copyright © 2017 Elsevier Inc. All rights reserved.

A novel x-ray detector design with higher DQE and reduced aliasing: Theoretical analysis of x-ray reabsoprtion in detector converter material

NASA Astrophysics Data System (ADS)

Nano, Tomi; Escartin, Terenz; Karim, Karim S.; Cunningham, Ian A.

2016-03-01

The ability to improve visualization of structural information in digital radiography without increasing radiation exposures requires improved image quality across all spatial frequencies, especially at high frequencies. The detective quantum efficiency (DQE) as a function of spatial frequency quantifies image quality given by an x-ray detector. We present a method of increasing DQE at high spatial frequencies by improving the modulation transfer function (MTF) and reducing noise aliasing. The Apodized Aperature Pixel (AAP) design uses a detector with micro-elements to synthesize desired pixels and provide higher DQE than conventional detector designs. A cascaded system analysis (CSA) that incorporates x-ray interactions is used for comparison of the theoretical MTF, noise power spectrum (NPS), and DQE. Signal and noise transfer through the converter material is shown to consist of correlated an uncorrelated terms. The AAP design was shown to improve the DQE of both material types that have predominantly correlated transfer (such as CsI) and predominantly uncorrelated transfer (such as Se). Improvement in the MTF by 50% and the DQE by 100% at the sampling cut-off frequency is obtained when uncorrelated transfer is prevalent through the converter material. Optimizing high-frequency DQE results in improved image contrast and visualization of small structures and fine-detail.

Strategy for reliable strain measurement in InAs/GaAs materials from high-resolution Z-contrast STEM images

NASA Astrophysics Data System (ADS)

Vatanparast, Maryam; Vullum, Per Erik; Nord, Magnus; Zuo, Jian-Min; Reenaas, Turid W.; Holmestad, Randi

2017-09-01

Geometric phase analysis (GPA), a fast and simple Fourier space method for strain analysis, can give useful information on accumulated strain and defect propagation in multiple layers of semiconductors, including quantum dot materials. In this work, GPA has been applied to high resolution Z-contrast scanning transmission electron microscopy (STEM) images. Strain maps determined from different g vectors of these images are compared to each other, in order to analyze and assess the GPA technique in terms of accuracy. The SmartAlign tool has been used to improve the STEM image quality getting more reliable results. Strain maps from template matching as a real space approach are compared with strain maps from GPA, and it is discussed that a real space analysis is a better approach than GPA for aberration corrected STEM images.

Segment fusion of ToF-SIMS images.

PubMed

Milillo, Tammy M; Miller, Mary E; Fischione, Remo; Montes, Angelina; Gardella, Joseph A

2016-06-08

The imaging capabilities of time-of-flight secondary ion mass spectrometry (ToF-SIMS) have not been used to their full potential in the analysis of polymer and biological samples. Imaging has been limited by the size of the dataset and the chemical complexity of the sample being imaged. Pixel and segment based image fusion algorithms commonly used in remote sensing, ecology, geography, and geology provide a way to improve spatial resolution and classification of biological images. In this study, a sample of Arabidopsis thaliana was treated with silver nanoparticles and imaged with ToF-SIMS. These images provide insight into the uptake mechanism for the silver nanoparticles into the plant tissue, giving new understanding to the mechanism of uptake of heavy metals in the environment. The Munechika algorithm was programmed in-house and applied to achieve pixel based fusion, which improved the spatial resolution of the image obtained. Multispectral and quadtree segment or region based fusion algorithms were performed using ecognition software, a commercially available remote sensing software suite, and used to classify the images. The Munechika fusion improved the spatial resolution for the images containing silver nanoparticles, while the segment fusion allowed classification and fusion based on the tissue types in the sample, suggesting potential pathways for the uptake of the silver nanoparticles.

A novel method to measure conspicuous facial pores using computer analysis of digital-camera-captured images: the effect of glycolic acid chemical peeling.

PubMed

Kakudo, Natsuko; Kushida, Satoshi; Tanaka, Nobuko; Minakata, Tatsuya; Suzuki, Kenji; Kusumoto, Kenji

2011-11-01

Chemical peeling is becoming increasingly popular for skin rejuvenation in dermatological esthetic surgery. Conspicuous facial pores are one of the most frequently encountered skin problems in women of all ages. This study was performed to analyze the effectiveness of reducing conspicuous facial pores using glycolic acid chemical peeling (GACP) based on a novel computer analysis of digital-camera-captured images. GACP was performed a total of five times at 2-week intervals in 22 healthy women. Computerized image analysis of conspicuous, open, and darkened facial pores was performed using the Robo Skin Analyzer CS 50. The number of conspicuous facial pores decreased significantly in 19 (86%) of the 22 subjects, with a mean improvement rate of 34.6%. The number of open pores decreased significantly in 16 (72%) of the subjects, with a mean improvement rate of 11.0%. The number of darkened pores decreased significantly in 18 (81%) of the subjects, with a mean improvement rate of 34.3%. GACP significantly reduces the number of conspicuous facial pores. The Robo Skin Analyzer CS 50 is useful for the quantification and analysis of 'pore enlargement', a subtle finding in dermatological esthetic surgery. © 2011 John Wiley & Sons A/S.

Development of a Reference Image Collection Library for Histopathology Image Processing, Analysis and Decision Support Systems Research.

PubMed

Kostopoulos, Spiros; Ravazoula, Panagiota; Asvestas, Pantelis; Kalatzis, Ioannis; Xenogiannopoulos, George; Cavouras, Dionisis; Glotsos, Dimitris

2017-06-01

Histopathology image processing, analysis and computer-aided diagnosis have been shown as effective assisting tools towards reliable and intra-/inter-observer invariant decisions in traditional pathology. Especially for cancer patients, decisions need to be as accurate as possible in order to increase the probability of optimal treatment planning. In this study, we propose a new image collection library (HICL-Histology Image Collection Library) comprising 3831 histological images of three different diseases, for fostering research in histopathology image processing, analysis and computer-aided diagnosis. Raw data comprised 93, 116 and 55 cases of brain, breast and laryngeal cancer respectively collected from the archives of the University Hospital of Patras, Greece. The 3831 images were generated from the most representative regions of the pathology, specified by an experienced histopathologist. The HICL Image Collection is free for access under an academic license at http://medisp.bme.teiath.gr/hicl/ . Potential exploitations of the proposed library may span over a board spectrum, such as in image processing to improve visualization, in segmentation for nuclei detection, in decision support systems for second opinion consultations, in statistical analysis for investigation of potential correlations between clinical annotations and imaging findings and, generally, in fostering research on histopathology image processing and analysis. To the best of our knowledge, the HICL constitutes the first attempt towards creation of a reference image collection library in the field of traditional histopathology, publicly and freely available to the scientific community.

Fast and objective detection and analysis of structures in downhole images

NASA Astrophysics Data System (ADS)

Wedge, Daniel; Holden, Eun-Jung; Dentith, Mike; Spadaccini, Nick

2017-09-01

Downhole acoustic and optical televiewer images, and formation microimager (FMI) logs are important datasets for structural and geotechnical analyses for the mineral and petroleum industries. Within these data, dipping planar structures appear as sinusoids, often in incomplete form and in abundance. Their detection is a labour intensive and hence expensive task and as such is a significant bottleneck in data processing as companies may have hundreds of kilometres of logs to process each year. We present an image analysis system that harnesses the power of automated image analysis and provides an interactive user interface to support the analysis of televiewer images by users with different objectives. Our algorithm rapidly produces repeatable, objective results. We have embedded it in an interactive workflow to complement geologists' intuition and experience in interpreting data to improve efficiency and assist, rather than replace the geologist. The main contributions include a new image quality assessment technique for highlighting image areas most suited to automated structure detection and for detecting boundaries of geological zones, and a novel sinusoid detection algorithm for detecting and selecting sinusoids with given confidence levels. Further tools are provided to perform rapid analysis of and further detection of structures e.g. as limited to specific orientations.

A fast global fitting algorithm for fluorescence lifetime imaging microscopy based on image segmentation.

PubMed

Pelet, S; Previte, M J R; Laiho, L H; So, P T C

2004-10-01

Global fitting algorithms have been shown to improve effectively the accuracy and precision of the analysis of fluorescence lifetime imaging microscopy data. Global analysis performs better than unconstrained data fitting when prior information exists, such as the spatial invariance of the lifetimes of individual fluorescent species. The highly coupled nature of global analysis often results in a significantly slower convergence of the data fitting algorithm as compared with unconstrained analysis. Convergence speed can be greatly accelerated by providing appropriate initial guesses. Realizing that the image morphology often correlates with fluorophore distribution, a global fitting algorithm has been developed to assign initial guesses throughout an image based on a segmentation analysis. This algorithm was tested on both simulated data sets and time-domain lifetime measurements. We have successfully measured fluorophore distribution in fibroblasts stained with Hoechst and calcein. This method further allows second harmonic generation from collagen and elastin autofluorescence to be differentiated in fluorescence lifetime imaging microscopy images of ex vivo human skin. On our experimental measurement, this algorithm increased convergence speed by over two orders of magnitude and achieved significantly better fits. Copyright 2004 Biophysical Society

Computed Tomography Imaging of a Hip Prosthesis Using Iterative Model-Based Reconstruction and Orthopaedic Metal Artefact Reduction: A Quantitative Analysis.

PubMed

Wellenberg, Ruud H H; Boomsma, Martijn F; van Osch, Jochen A C; Vlassenbroek, Alain; Milles, Julien; Edens, Mireille A; Streekstra, Geert J; Slump, Cornelis H; Maas, Mario

To quantify the combined use of iterative model-based reconstruction (IMR) and orthopaedic metal artefact reduction (O-MAR) in reducing metal artefacts and improving image quality in a total hip arthroplasty phantom. Scans acquired at several dose levels and kVps were reconstructed with filtered back-projection (FBP), iterative reconstruction (iDose) and IMR, with and without O-MAR. Computed tomography (CT) numbers, noise levels, signal-to-noise-ratios and contrast-to-noise-ratios were analysed. Iterative model-based reconstruction results in overall improved image quality compared to iDose and FBP (P < 0.001). Orthopaedic metal artefact reduction is most effective in reducing severe metal artefacts improving CT number accuracy by 50%, 60%, and 63% (P < 0.05) and reducing noise by 1%, 62%, and 85% (P < 0.001) whereas improving signal-to-noise-ratios by 27%, 47%, and 46% (P < 0.001) and contrast-to-noise-ratios by 16%, 25%, and 19% (P < 0.001) with FBP, iDose, and IMR, respectively. The combined use of IMR and O-MAR strongly improves overall image quality and strongly reduces metal artefacts in the CT imaging of a total hip arthroplasty phantom.

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.

Green light may improve diagnostic accuracy of nailfold capillaroscopy with a simple digital videomicroscope.

PubMed

Weekenstroo, Harm H A; Cornelissen, Bart M W; Bernelot Moens, Hein J

2015-06-01

Nailfold capillaroscopy is a non-invasive and safe technique for the analysis of microangiopathologies. Imaging quality of widely used simple videomicroscopes is poor. The use of green illumination instead of the commonly used white light may improve contrast. The aim of the study was to compare the effect of green illumination with white illumination, regarding capillary density, the number of microangiopathologies, and sensitivity and specificity for systemic sclerosis. Five rheumatologists have evaluated 80 images; 40 images acquired with green light, and 40 images acquired with white light. A larger number of microangiopathologies were found in images acquired with green light than in images acquired with white light. This results in slightly higher sensitivity with green light in comparison with white light, without reducing the specificity. These findings suggest that green instead of white illumination may facilitate evaluation of capillaroscopic images obtained with a low-cost digital videomicroscope.

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.

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.

Improving temporal resolution and speed sensitivity of laser speckle contrast analysis imaging based on noise reduction with an anisotropic diffusion filter

NASA Astrophysics Data System (ADS)

Song, Lipei; Wang, Xueyan; Zhang, Ru; Zhang, Kuanshou; Zhou, Zhen; Elson, Daniel S.

2018-07-01

The fluctuation of contrast caused by statistical noise degenerates the temporal/spatial resolution of laser speckle contrast imaging (LSCI) and limits the maximum speed when imaging. In this study, we investigated the application of the anisotropic diffusion filter (ADF) to temporal LSCI and found that the edge magnitude parameter of the ADF can be determined by the mean of the contrast image. Because the edge magnitude parameter is usually denoted as K, we term this the K-constant ADF (KC-ADF) and show that temporal sensitivity is improved when imaging because of the enhanced signal-to-noise ratio when using the KC-ADF in small-animal experiments. The cardiac cycle of a rat as high as 390 bpm can be imaged with an industrial camera.

Displacement measurement with nanoscale resolution using a coded micro-mark and digital image correlation

NASA Astrophysics Data System (ADS)

Huang, Wei; Ma, Chengfu; Chen, Yuhang

2014-12-01

A method for simple and reliable displacement measurement with nanoscale resolution is proposed. The measurement is realized by combining a common optical microscopy imaging of a specially coded nonperiodic microstructure, namely two-dimensional zero-reference mark (2-D ZRM), and subsequent correlation analysis of the obtained image sequence. The autocorrelation peak contrast of the ZRM code is maximized with well-developed artificial intelligence algorithms, which enables robust and accurate displacement determination. To improve the resolution, subpixel image correlation analysis is employed. Finally, we experimentally demonstrate the quasi-static and dynamic displacement characterization ability of a micro 2-D ZRM.

Geologic Measurements using Rover Images: Lessons from Pathfinder with Application to Mars 2001

NASA Technical Reports Server (NTRS)

Bridges, N. T.; Haldemann, A. F. C.; Herkenhoff, K. E.

1999-01-01

The Pathfinder Sojourner rover successfully acquired images that provided important and exciting information on the geology of Mars. This included the documentation of rock textures, barchan dunes, soil crusts, wind tails, and ventifacts. It is expected that the Marie Curie rover cameras will also successfully return important information on landing site geology. Critical to a proper analysis of these images will be a rigorous determination of rover location and orientation. Here, the methods that were used to compute rover position for Sojourner image analysis are reviewed. Based on this experience, specific recommendations are made that should improve this process on the '01 mission.

A Novel Anti-Spoofing Solution for Iris Recognition Toward Cosmetic Contact Lens Attack Using Spectral ICA Analysis.

PubMed

Hsieh, Sheng-Hsun; Li, Yung-Hui; Wang, Wei; Tien, Chung-Hao

2018-03-06

In this study, we maneuvered a dual-band spectral imaging system to capture an iridal image from a cosmetic-contact-lens-wearing subject. By using the independent component analysis to separate individual spectral primitives, we successfully distinguished the natural iris texture from the cosmetic contact lens (CCL) pattern, and restored the genuine iris patterns from the CCL-polluted image. Based on a database containing 200 test image pairs from 20 CCL-wearing subjects as the proof of concept, the recognition accuracy (False Rejection Rate: FRR) was improved from FRR = 10.52% to FRR = 0.57% with the proposed ICA anti-spoofing scheme.

Tissue classification for laparoscopic image understanding based on multispectral texture analysis

NASA Astrophysics Data System (ADS)

Zhang, Yan; Wirkert, Sebastian J.; Iszatt, Justin; Kenngott, Hannes; Wagner, Martin; Mayer, Benjamin; Stock, Christian; Clancy, Neil T.; Elson, Daniel S.; Maier-Hein, Lena

2016-03-01

Intra-operative tissue classification is one of the prerequisites for providing context-aware visualization in computer-assisted minimally invasive surgeries. As many anatomical structures are difficult to differentiate in conventional RGB medical images, we propose a classification method based on multispectral image patches. In a comprehensive ex vivo study we show (1) that multispectral imaging data is superior to RGB data for organ tissue classification when used in conjunction with widely applied feature descriptors and (2) that combining the tissue texture with the reflectance spectrum improves the classification performance. Multispectral tissue analysis could thus evolve as a key enabling technique in computer-assisted laparoscopy.

Towards a framework for agent-based image analysis of remote-sensing data

PubMed Central

Hofmann, Peter; Lettmayer, Paul; Blaschke, Thomas; Belgiu, Mariana; Wegenkittl, Stefan; Graf, Roland; Lampoltshammer, Thomas Josef; Andrejchenko, Vera

2015-01-01

Object-based image analysis (OBIA) as a paradigm for analysing remotely sensed image data has in many cases led to spatially and thematically improved classification results in comparison to pixel-based approaches. Nevertheless, robust and transferable object-based solutions for automated image analysis capable of analysing sets of images or even large image archives without any human interaction are still rare. A major reason for this lack of robustness and transferability is the high complexity of image contents: Especially in very high resolution (VHR) remote-sensing data with varying imaging conditions or sensor characteristics, the variability of the objects’ properties in these varying images is hardly predictable. The work described in this article builds on so-called rule sets. While earlier work has demonstrated that OBIA rule sets bear a high potential of transferability, they need to be adapted manually, or classification results need to be adjusted manually in a post-processing step. In order to automate these adaptation and adjustment procedures, we investigate the coupling, extension and integration of OBIA with the agent-based paradigm, which is exhaustively investigated in software engineering. The aims of such integration are (a) autonomously adapting rule sets and (b) image objects that can adopt and adjust themselves according to different imaging conditions and sensor characteristics. This article focuses on self-adapting image objects and therefore introduces a framework for agent-based image analysis (ABIA). PMID:27721916

Towards a framework for agent-based image analysis of remote-sensing data.

PubMed

Hofmann, Peter; Lettmayer, Paul; Blaschke, Thomas; Belgiu, Mariana; Wegenkittl, Stefan; Graf, Roland; Lampoltshammer, Thomas Josef; Andrejchenko, Vera

2015-04-03

Object-based image analysis (OBIA) as a paradigm for analysing remotely sensed image data has in many cases led to spatially and thematically improved classification results in comparison to pixel-based approaches. Nevertheless, robust and transferable object-based solutions for automated image analysis capable of analysing sets of images or even large image archives without any human interaction are still rare. A major reason for this lack of robustness and transferability is the high complexity of image contents: Especially in very high resolution (VHR) remote-sensing data with varying imaging conditions or sensor characteristics, the variability of the objects' properties in these varying images is hardly predictable. The work described in this article builds on so-called rule sets. While earlier work has demonstrated that OBIA rule sets bear a high potential of transferability, they need to be adapted manually, or classification results need to be adjusted manually in a post-processing step. In order to automate these adaptation and adjustment procedures, we investigate the coupling, extension and integration of OBIA with the agent-based paradigm, which is exhaustively investigated in software engineering. The aims of such integration are (a) autonomously adapting rule sets and (b) image objects that can adopt and adjust themselves according to different imaging conditions and sensor characteristics. This article focuses on self-adapting image objects and therefore introduces a framework for agent-based image analysis (ABIA).

Factors associated with the improvement of vocal fold movement: an analysis of LEMG and laryngeal CT parameters.

PubMed

Mengsteab, Paulos Y; Kwon, Jeong-Yi; Han, Tai Ryoon; Kwon, Tack Kyun; Kim, Deok-Ho; Kim, Sang Jun

2015-02-01

The aim of this study is to elucidate the relationship of laryngeal electromyography (LEMG) and computed tomographic (CT) parameters to improve the prognosis of recurrent laryngeal nerve injury. 22 patients clinically suspected of having recurrent laryngeal nerve injury were examined with LEMG and CT studies. Bilateral thyroarytenoid (TA) muscles were examined and findings were interpreted by a single blind technique. Laryngeal CT image analysis of the ventricle dilation symmetry determined TA muscle atrophy. Finally, a follow-up laryngoscopic examination determined improvement of vocal fold movement. Ventricle dilation symmetry and the dichotomized TA muscle atrophy parameter significantly relate to the improvement of vocal fold movement (χ(2)=4.029, P=0.039, and χ(2)=3.912, P=0.048, respectively). When the severity of vocal fold impairment was classified as severe TA muscle atrophy or none/discrete MUAP recruitment, it was found to significantly relate with the improvement of vocal fold movement (χ(2)=6.712, P=.010). From this study, image analysis of the ventricle dilation symmetry to determine the severity of TA muscle atrophy shows promise for the improved prognosis of vocal fold immobility. Copyright © 2014 Elsevier Ltd. All rights reserved.

MRI artifact reduction and quality improvement in the upper abdomen with PROPELLER and prospective acquisition correction (PACE) technique.

PubMed

Hirokawa, Yuusuke; Isoda, Hiroyoshi; Maetani, Yoji S; Arizono, Shigeki; Shimada, Kotaro; Togashi, Kaori

2008-10-01

The purpose of this study was to evaluate the effectiveness of the periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER [BLADE in the MR systems from Siemens Medical Solutions]) with a respiratory compensation technique for motion correction, image noise reduction, improved sharpness of liver edge, and image quality of the upper abdomen. Twenty healthy adult volunteers with a mean age of 28 years (age range, 23-42 years) underwent upper abdominal MRI with a 1.5-T scanner. For each subject, fat-saturated T2-weighted turbo spin-echo (TSE) sequences with respiratory compensation (prospective acquisition correction [PACE]) were performed with and without the BLADE technique. Ghosting artifact, artifacts except ghosting artifact such as respiratory motion and bowel movement, sharpness of liver edge, image noise, and overall image quality were evaluated visually by three radiologists using a 5-point scale for qualitative analysis. The Wilcoxon's signed rank test was used to determine whether a significant difference existed between images with and without BLADE. A p value less than 0.05 was considered to be statistically significant. In the BLADE images, image artifacts, sharpness of liver edge, image noise, and overall image quality were significantly improved (p < 0.001). With the BLADE technique, T2-weighted TSE images of the upper abdomen could provide reduced image artifacts including ghosting artifact and image noise and provide better image quality.

Fourier Transform Infrared (FT-IR) and Laser Ablation Inductively Coupled Plasma-Mass Spectrometry (LA-ICP-MS) Imaging of Cerebral Ischemia: Combined Analysis of Rat Brain Thin Cuts Toward Improved Tissue Classification.

PubMed

Balbekova, Anna; Lohninger, Hans; van Tilborg, Geralda A F; Dijkhuizen, Rick M; Bonta, Maximilian; Limbeck, Andreas; Lendl, Bernhard; Al-Saad, Khalid A; Ali, Mohamed; Celikic, Minja; Ofner, Johannes

2018-02-01

Microspectroscopic techniques are widely used to complement histological studies. Due to recent developments in the field of chemical imaging, combined chemical analysis has become attractive. This technique facilitates a deepened analysis compared to single techniques or side-by-side analysis. In this study, rat brains harvested one week after induction of photothrombotic stroke were investigated. Adjacent thin cuts from rats' brains were imaged using Fourier transform infrared (FT-IR) microspectroscopy and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The LA-ICP-MS data were normalized using an internal standard (a thin gold layer). The acquired hyperspectral data cubes were fused and subjected to multivariate analysis. Brain regions affected by stroke as well as unaffected gray and white matter were identified and classified using a model based on either partial least squares discriminant analysis (PLS-DA) or random decision forest (RDF) algorithms. The RDF algorithm demonstrated the best results for classification. Improved classification was observed in the case of fused data in comparison to individual data sets (either FT-IR or LA-ICP-MS). Variable importance analysis demonstrated that both molecular and elemental content contribute to the improved RDF classification. Univariate spectral analysis identified biochemical properties of the assigned tissue types. Classification of multisensor hyperspectral data sets using an RDF algorithm allows access to a novel and in-depth understanding of biochemical processes and solid chemical allocation of different brain regions.

Use of a hybrid iterative reconstruction technique to reduce image noise and improve image quality in obese patients undergoing computed tomographic pulmonary angiography.

PubMed

Kligerman, Seth; Mehta, Dhruv; Farnadesh, Mahmmoudreza; Jeudy, Jean; Olsen, Kathryn; White, Charles

2013-01-01

To determine whether an iterative reconstruction (IR) technique (iDose, Philips Healthcare) can reduce image noise and improve image quality in obese patients undergoing computed tomographic pulmonary angiography (CTPA). The study was Health Insurance Portability and Accountability Act compliant and approved by our institutional review board. A total of 33 obese patients (average body mass index: 42.7) underwent CTPA studies following standard departmental protocols. The data were reconstructed with filtered back projection (FBP) and 3 iDose strengths (iDoseL1, iDoseL3, and iDoseL5) for a total of 132 studies. FBP data were collected from 33 controls (average body mass index: 22) undergoing CTPA. Regions of interest were drawn at 6 identical levels in the pulmonary artery (PA), from the main PA to a subsegmental branch, in both the control group and study groups using each algorithm. Noise and attenuation were measured at all PA levels. Three thoracic radiologists graded each study on a scale of 1 (very poor) to 5 (ideal) by 4 categories: image quality, noise, PA enhancement, and "plastic" appearance. Statistical analysis was performed using an unpaired t test, 1-way analysis of variance, and linear weighted κ. Compared with the control group, there was significantly higher noise with FBP, iDoseL1, and iDoseL3 algorithms (P<0.001) in the study group. There was no significant difference between the noise in the control group and iDoseL5 algorithm in the study group. Analysis within the study group showed a significant and progressive decrease in noise and increase in the contrast-to-noise ratio as the level of IR was increased (P<0.001). Compared with FBP, readers graded overall image quality as being higher using iDoseL1 (P=0.0018), iDoseL3 (P<0.001), and iDoseL5 (P<0.001). Compared with FBP, there was subjective improvement in image noise and PA enhancement with increasing levels of iDose. The use of an IR technique leads to qualitative and quantitative improvements in image noise and image quality in obese patients undergoing CTPA.

Segmentation of radiologic images with self-organizing maps: the segmentation problem transformed into a classification task

NASA Astrophysics Data System (ADS)

Pelikan, Erich; Vogelsang, Frank; Tolxdorff, Thomas

1996-04-01

The texture-based segmentation of x-ray images of focal bone lesions using topological maps is introduced. Texture characteristics are described by image-point correlation of feature images to feature vectors. For the segmentation, the topological map is labeled using an improved labeling strategy. Results of the technique are demonstrated on original and synthetic x-ray images and quantified with the aid of quality measures. In addition, a classifier-specific contribution analysis is applied for assessing the feature space.

Particle Morphology Analysis of Biomass Material Based on Improved Image Processing Method

PubMed Central

Lu, Zhaolin

2017-01-01

Particle morphology, including size and shape, is an important factor that significantly influences the physical and chemical properties of biomass material. Based on image processing technology, a method was developed to process sample images, measure particle dimensions, and analyse the particle size and shape distributions of knife-milled wheat straw, which had been preclassified into five nominal size groups using mechanical sieving approach. Considering the great variation of particle size from micrometer to millimeter, the powders greater than 250 μm were photographed by a flatbed scanner without zoom function, and the others were photographed using a scanning electron microscopy (SEM) with high-image resolution. Actual imaging tests confirmed the excellent effect of backscattered electron (BSE) imaging mode of SEM. Particle aggregation is an important factor that affects the recognition accuracy of the image processing method. In sample preparation, the singulated arrangement and ultrasonic dispersion methods were used to separate powders into particles that were larger and smaller than the nominal size of 250 μm. In addition, an image segmentation algorithm based on particle geometrical information was proposed to recognise the finer clustered powders. Experimental results demonstrated that the improved image processing method was suitable to analyse the particle size and shape distributions of ground biomass materials and solve the size inconsistencies in sieving analysis. PMID:28298925

Detection of human brain tumor infiltration with multimodal multiscale optical analysis

NASA Astrophysics Data System (ADS)

Poulon, Fanny; Metais, Camille; Jamme, Frederic; Zanello, Marc; Varlet, Pascale; Devaux, Bertrand; Refregiers, Matthieu; Abi Haidar, Darine

2017-02-01

Brain tumor surgeries are facing major challenges to improve patients' quality of life. The extent of resection while preserving surrounding eloquent brain areas is necessary to equilibrate the onco-functional. A tool able to increase the accuracy of tissue analysis and to deliver an immediate diagnostic on tumor, could drastically improve actual surgeries and patient survival rates. To achieve such performances a complete optical study, ranging from ultraviolet to infrared, of biopsies has been started by our group. Four different contrasts were used: 1) spectral analysis covering the DUV to IR range, 2) two photon fluorescence lifetime imaging and one photon time domain measurement, 3) second harmonic generation imaging and 4) fluorescence imaging using DUV to IR, one and two photon excitation. All these measurements were done on the endogenous fluorescence of tissues to avoid any bias and further clinical complication due to the introduction of external markers. The different modalities are then crossed to build a matrix of criteria to discriminate tumorous tissues. The results of multimodal optical analysis on human biopsies were compared to the gold standard histopathology.

Research on respiratory motion correction method based on liver contrast-enhanced ultrasound images of single mode

NASA Astrophysics Data System (ADS)

Zhang, Ji; Li, Tao; Zheng, Shiqiang; Li, Yiyong

2015-03-01

To reduce the effects of respiratory motion in the quantitative analysis based on liver contrast-enhanced ultrasound (CEUS) image sequencesof single mode. The image gating method and the iterative registration method using model image were adopted to register liver contrast-enhanced ultrasound image sequences of single mode. The feasibility of the proposed respiratory motion correction method was explored preliminarily using 10 hepatocellular carcinomas CEUS cases. The positions of the lesions in the time series of 2D ultrasound images after correction were visually evaluated. Before and after correction, the quality of the weighted sum of transit time (WSTT) parametric images were also compared, in terms of the accuracy and spatial resolution. For the corrected and uncorrected sequences, their mean deviation values (mDVs) of time-intensity curve (TIC) fitting derived from CEUS sequences were measured. After the correction, the positions of the lesions in the time series of 2D ultrasound images were almost invariant. In contrast, the lesions in the uncorrected images all shifted noticeably. The quality of the WSTT parametric maps derived from liver CEUS image sequences were improved more greatly. Moreover, the mDVs of TIC fitting derived from CEUS sequences after the correction decreased by an average of 48.48+/-42.15. The proposed correction method could improve the accuracy of quantitative analysis based on liver CEUS image sequences of single mode, which would help in enhancing the differential diagnosis efficiency of liver tumors.

GRAPE: a graphical pipeline environment for image analysis in adaptive magnetic resonance imaging.

PubMed

Gabr, Refaat E; Tefera, Getaneh B; Allen, William J; Pednekar, Amol S; Narayana, Ponnada A

2017-03-01

We present a platform, GRAphical Pipeline Environment (GRAPE), to facilitate the development of patient-adaptive magnetic resonance imaging (MRI) protocols. GRAPE is an open-source project implemented in the Qt C++ framework to enable graphical creation, execution, and debugging of real-time image analysis algorithms integrated with the MRI scanner. The platform provides the tools and infrastructure to design new algorithms, and build and execute an array of image analysis routines, and provides a mechanism to include existing analysis libraries, all within a graphical environment. The application of GRAPE is demonstrated in multiple MRI applications, and the software is described in detail for both the user and the developer. GRAPE was successfully used to implement and execute three applications in MRI of the brain, performed on a 3.0-T MRI scanner: (i) a multi-parametric pipeline for segmenting the brain tissue and detecting lesions in multiple sclerosis (MS), (ii) patient-specific optimization of the 3D fluid-attenuated inversion recovery MRI scan parameters to enhance the contrast of brain lesions in MS, and (iii) an algebraic image method for combining two MR images for improved lesion contrast. GRAPE allows graphical development and execution of image analysis algorithms for inline, real-time, and adaptive MRI applications.

Direct laser additive fabrication system with image feedback control

DOEpatents

Griffith, Michelle L.; Hofmeister, William H.; Knorovsky, Gerald A.; MacCallum, Danny O.; Schlienger, M. Eric; Smugeresky, John E.

2002-01-01

A closed-loop, feedback-controlled direct laser fabrication system is disclosed. The feedback refers to the actual growth conditions obtained by real-time analysis of thermal radiation images. The resulting system can fabricate components with severalfold improvement in dimensional tolerances and surface finish.

Comparison of implosion core metrics: A 10 ps dilation X-ray imager vs a 100 ps gated microchannel plate [Comparison of implosion core shape observations, 10 ps dilation X-ray imager vs 100 ps gated microchannel plate

DOE PAGES

Nagel, S. R.; Benedetti, L. R.; Bradley, D. K.; ...

2016-08-05

The dilation x-ray imager (DIXI) is a high-speed x-ray framing camera that uses the pulse-dilation technique to achieve a temporal resolution of less than 10 ps. This is a 10× improvement over conventional framing cameras currently employed on the National Ignition Facility (NIF) (100 ps resolution), and otherwise only achievable with 1D streaked imaging. A side effect of the dramatically reduced gate width is the comparatively lower detected signal level. Therefore we implement a Poisson noise reduction with non-local principal component analysis method to improve the robustness of the DIXI data analysis. Furthermore, we present results on ignition-relevant experiments atmore » the NIF using DIXI. In particular we focus on establishing that/when DIXI gives reliable shape metrics (P 0, P 2 and P 4 Legendre modes, and their temporal evolution/swings).« less

Comparison of implosion core metrics: A 10 ps dilation X-ray imager vs a 100 ps gated microchannel plate [Comparison of implosion core shape observations, 10 ps dilation X-ray imager vs 100 ps gated microchannel plate

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

Nagel, S. R.; Benedetti, L. R.; Bradley, D. K.

The dilation x-ray imager (DIXI) is a high-speed x-ray framing camera that uses the pulse-dilation technique to achieve a temporal resolution of less than 10 ps. This is a 10× improvement over conventional framing cameras currently employed on the National Ignition Facility (NIF) (100 ps resolution), and otherwise only achievable with 1D streaked imaging. A side effect of the dramatically reduced gate width is the comparatively lower detected signal level. Therefore we implement a Poisson noise reduction with non-local principal component analysis method to improve the robustness of the DIXI data analysis. Furthermore, we present results on ignition-relevant experiments atmore » the NIF using DIXI. In particular we focus on establishing that/when DIXI gives reliable shape metrics (P 0, P 2 and P 4 Legendre modes, and their temporal evolution/swings).« less

Color Retinal Image Enhancement Based on Luminosity and Contrast Adjustment.

PubMed

Zhou, Mei; Jin, Kai; Wang, Shaoze; Ye, Juan; Qian, Dahong

2018-03-01

Many common eye diseases and cardiovascular diseases can be diagnosed through retinal imaging. However, due to uneven illumination, image blurring, and low contrast, retinal images with poor quality are not useful for diagnosis, especially in automated image analyzing systems. Here, we propose a new image enhancement method to improve color retinal image luminosity and contrast. A luminance gain matrix, which is obtained by gamma correction of the value channel in the HSV (hue, saturation, and value) color space, is used to enhance the R, G, and B (red, green and blue) channels, respectively. Contrast is then enhanced in the luminosity channel of L * a * b * color space by CLAHE (contrast-limited adaptive histogram equalization). Image enhancement by the proposed method is compared to other methods by evaluating quality scores of the enhanced images. The performance of the method is mainly validated on a dataset of 961 poor-quality retinal images. Quality assessment (range 0-1) of image enhancement of this poor dataset indicated that our method improved color retinal image quality from an average of 0.0404 (standard deviation 0.0291) up to an average of 0.4565 (standard deviation 0.1000). The proposed method is shown to achieve superior image enhancement compared to contrast enhancement in other color spaces or by other related methods, while simultaneously preserving image naturalness. This method of color retinal image enhancement may be employed to assist ophthalmologists in more efficient screening of retinal diseases and in development of improved automated image analysis for clinical diagnosis.

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

Utility of Dual-Energy CT-based Monochromatic Imaging in the Assessment of Myocardial Delayed Enhancement in Patients with Cardiomyopathy.

PubMed

Chang, Suyon; Han, Kyunghwa; Youn, Jong-Chan; Im, Dong Jin; Kim, Jin Young; Suh, Young Joo; Hong, Yoo Jin; Hur, Jin; Kim, Young Jin; Choi, Byoung Wook; Lee, Hye-Jeong

2018-05-01

Purpose To investigate the diagnostic utility of dual-energy computed tomography (CT)-based monochromatic imaging for myocardial delayed enhancement (MDE) assessment in patients with cardiomyopathy. Materials and Methods The institutional review board approved this prospective study, and informed consent was obtained from all participants who were enrolled in the study. Forty patients (27 men and 13 women; mean age, 56 years ± 15 [standard deviation]; age range, 22-81 years) with cardiomyopathy underwent cardiac magnetic resonance (MR) imaging and dual-energy CT. Conventional (120-kV) and monochromatic (60-, 70-, and 80-keV) images were reconstructed from the dual-energy CT acquisition. Subjective quality score, contrast-to-noise ratio (CNR), and beam-hardening artifacts were compared pairwise with the Friedman test at post hoc analysis. With cardiac MR imaging as the reference standard, diagnostic performance of dual-energy CT in MDE detection and its predictive ability for pattern classification were compared pairwise by using logistic regression analysis with the generalized estimating equation in a per-segment analysis. The Bland-Altman method was used to find agreement between cardiac MR imaging and CT in MDE quantification. Results Among the monochromatic images, 70-keV CT images resulted in higher subjective quality (mean score, 3.38 ± 0.54 vs 3.15 ± 0.43; P = .0067), higher CNR (mean, 4.26 ± 1.38 vs 3.93 ± 1.33; P = .0047), and a lower value for beam-hardening artifacts (mean, 3.47 ± 1.56 vs 4.15 ± 1.67; P < .0001) when compared with conventional CT. When compared with conventional CT, 70-keV CT showed improved diagnostic performance for MDE detection (sensitivity, 94.6% vs 90.4% [P = .0032]; specificity, 96.0% vs 94.0% [P = .0031]; and accuracy, 95.6% vs 92.7% [P < .0001]) and improved predictive ability for pattern classification (subendocardial, 91.5% vs 84.3% [P = .0111]; epicardial, 94.3% vs 73.5% [P = .0001]; transmural, 93.0% vs 77.7% [P = .0018]; mesocardial, 85.4% vs 69.2% [P = .0047]; and patchy. 84.4% vs 78.4% [P = .1514]). For MDE quantification, 70-keV CT showed a small bias 0.1534% (95% limits of agreement: -4.7013, 5.0080). Conclusion Dual-energy CT-based 70-keV monochromatic images improve MDE assessment in patients with cardiomyopathy via improved image quality and CNR and reduced beam-hardening artifacts when compared with conventional CT images. © RSNA, 2017 Online supplemental material is available for this article.

Automated image alignment for 2D gel electrophoresis in a high-throughput proteomics pipeline.

PubMed

Dowsey, Andrew W; Dunn, Michael J; Yang, Guang-Zhong

2008-04-01

The quest for high-throughput proteomics has revealed a number of challenges in recent years. Whilst substantial improvements in automated protein separation with liquid chromatography and mass spectrometry (LC/MS), aka 'shotgun' proteomics, have been achieved, large-scale open initiatives such as the Human Proteome Organization (HUPO) Brain Proteome Project have shown that maximal proteome coverage is only possible when LC/MS is complemented by 2D gel electrophoresis (2-DE) studies. Moreover, both separation methods require automated alignment and differential analysis to relieve the bioinformatics bottleneck and so make high-throughput protein biomarker discovery a reality. The purpose of this article is to describe a fully automatic image alignment framework for the integration of 2-DE into a high-throughput differential expression proteomics pipeline. The proposed method is based on robust automated image normalization (RAIN) to circumvent the drawbacks of traditional approaches. These use symbolic representation at the very early stages of the analysis, which introduces persistent errors due to inaccuracies in modelling and alignment. In RAIN, a third-order volume-invariant B-spline model is incorporated into a multi-resolution schema to correct for geometric and expression inhomogeneity at multiple scales. The normalized images can then be compared directly in the image domain for quantitative differential analysis. Through evaluation against an existing state-of-the-art method on real and synthetically warped 2D gels, the proposed analysis framework demonstrates substantial improvements in matching accuracy and differential sensitivity. High-throughput analysis is established through an accelerated GPGPU (general purpose computation on graphics cards) implementation. Supplementary material, software and images used in the validation are available at http://www.proteomegrid.org/rain/.

Faster tissue interface analysis from Raman microscopy images using compressed factorisation

NASA Astrophysics Data System (ADS)

Palmer, Andrew D.; Bannerman, Alistair; Grover, Liam; Styles, Iain B.

2013-06-01

The structure of an artificial ligament was examined using Raman microscopy in combination with novel data analysis. Basis approximation and compressed principal component analysis are shown to provide efficient compression of confocal Raman microscopy images, alongside powerful methods for unsupervised analysis. This scheme allows the acceleration of data mining, such as principal component analysis, as they can be performed on the compressed data representation, providing a decrease in the factorisation time of a single image from five minutes to under a second. Using this workflow the interface region between a chemically engineered ligament construct and a bone-mimic anchor was examined. Natural ligament contains a striated interface between the bone and tissue that provides improved mechanical load tolerance, a similar interface was found in the ligament construct.

Design and validation of Segment--freely available software for cardiovascular image analysis.

PubMed

Heiberg, Einar; Sjögren, Jane; Ugander, Martin; Carlsson, Marcus; Engblom, Henrik; Arheden, Håkan

2010-01-11

Commercially available software for cardiovascular image analysis often has limited functionality and frequently lacks the careful validation that is required for clinical studies. We have already implemented a cardiovascular image analysis software package and released it as freeware for the research community. However, it was distributed as a stand-alone application and other researchers could not extend it by writing their own custom image analysis algorithms. We believe that the work required to make a clinically applicable prototype can be reduced by making the software extensible, so that researchers can develop their own modules or improvements. Such an initiative might then serve as a bridge between image analysis research and cardiovascular research. The aim of this article is therefore to present the design and validation of a cardiovascular image analysis software package (Segment) and to announce its release in a source code format. Segment can be used for image analysis in magnetic resonance imaging (MRI), computed tomography (CT), single photon emission computed tomography (SPECT) and positron emission tomography (PET). Some of its main features include loading of DICOM images from all major scanner vendors, simultaneous display of multiple image stacks and plane intersections, automated segmentation of the left ventricle, quantification of MRI flow, tools for manual and general object segmentation, quantitative regional wall motion analysis, myocardial viability analysis and image fusion tools. Here we present an overview of the validation results and validation procedures for the functionality of the software. We describe a technique to ensure continued accuracy and validity of the software by implementing and using a test script that tests the functionality of the software and validates the output. The software has been made freely available for research purposes in a source code format on the project home page http://segment.heiberg.se. Segment is a well-validated comprehensive software package for cardiovascular image analysis. It is freely available for research purposes provided that relevant original research publications related to the software are cited.

The research on medical image classification algorithm based on PLSA-BOW model.

PubMed

Cao, C H; Cao, H L

2016-04-29

With the rapid development of modern medical imaging technology, medical image classification has become more important for medical diagnosis and treatment. To solve the existence of polysemous words and synonyms problem, this study combines the word bag model with PLSA (Probabilistic Latent Semantic Analysis) and proposes the PLSA-BOW (Probabilistic Latent Semantic Analysis-Bag of Words) model. In this paper we introduce the bag of words model in text field to image field, and build the model of visual bag of words model. The method enables the word bag model-based classification method to be further improved in accuracy. The experimental results show that the PLSA-BOW model for medical image classification can lead to a more accurate classification.

Deep Learning in Medical Image Analysis.

PubMed

Shen, Dinggang; Wu, Guorong; Suk, Heung-Il

2017-06-21

This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications. We introduce the fundamentals of deep learning methods and review their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on. We conclude by discussing research issues and suggesting future directions for further improvement.

Pixel-level multisensor image fusion based on matrix completion and robust principal component analysis

NASA Astrophysics Data System (ADS)

Wang, Zhuozheng; Deller, J. R.; Fleet, Blair D.

2016-01-01

Acquired digital images are often corrupted by a lack of camera focus, faulty illumination, or missing data. An algorithm is presented for fusion of multiple corrupted images of a scene using the lifting wavelet transform. The method employs adaptive fusion arithmetic based on matrix completion and self-adaptive regional variance estimation. Characteristics of the wavelet coefficients are used to adaptively select fusion rules. Robust principal component analysis is applied to low-frequency image components, and regional variance estimation is applied to high-frequency components. Experiments reveal that the method is effective for multifocus, visible-light, and infrared image fusion. Compared with traditional algorithms, the new algorithm not only increases the amount of preserved information and clarity but also improves robustness.

Image quality enhancement for skin cancer optical diagnostics

NASA Astrophysics Data System (ADS)

Bliznuks, Dmitrijs; Kuzmina, Ilona; Bolocko, Katrina; Lihachev, Alexey

2017-12-01

The research presents image quality analysis and enhancement proposals in biophotonic area. The sources of image problems are reviewed and analyzed. The problems with most impact in biophotonic area are analyzed in terms of specific biophotonic task - skin cancer diagnostics. The results point out that main problem for skin cancer analysis is the skin illumination problems. Since it is often not possible to prevent illumination problems, the paper proposes image post processing algorithm - low frequency filtering. Practical results show diagnostic results improvement after using proposed filter. Along that, filter do not reduces diagnostic results' quality for images without illumination defects. Current filtering algorithm requires empirical tuning of filter parameters. Further work needed to test the algorithm in other biophotonic applications and propose automatic filter parameter selection.

New Colors for Histology: Optimized Bivariate Color Maps Increase Perceptual Contrast in Histological Images

PubMed Central

Kather, Jakob Nikolas; Weis, Cleo-Aron; Marx, Alexander; Schuster, Alexander K.; Schad, Lothar R.; Zöllner, Frank Gerrit

2015-01-01

Background Accurate evaluation of immunostained histological images is required for reproducible research in many different areas and forms the basis of many clinical decisions. The quality and efficiency of histopathological evaluation is limited by the information content of a histological image, which is primarily encoded as perceivable contrast differences between objects in the image. However, the colors of chromogen and counterstain used for histological samples are not always optimally distinguishable, even under optimal conditions. Methods and Results In this study, we present a method to extract the bivariate color map inherent in a given histological image and to retrospectively optimize this color map. We use a novel, unsupervised approach based on color deconvolution and principal component analysis to show that the commonly used blue and brown color hues in Hematoxylin—3,3’-Diaminobenzidine (DAB) images are poorly suited for human observers. We then demonstrate that it is possible to construct improved color maps according to objective criteria and that these color maps can be used to digitally re-stain histological images. Validation To validate whether this procedure improves distinguishability of objects and background in histological images, we re-stain phantom images and N = 596 large histological images of immunostained samples of human solid tumors. We show that perceptual contrast is improved by a factor of 2.56 in phantom images and up to a factor of 2.17 in sets of histological tumor images. Context Thus, we provide an objective and reliable approach to measure object distinguishability in a given histological image and to maximize visual information available to a human observer. This method could easily be incorporated in digital pathology image viewing systems to improve accuracy and efficiency in research and diagnostics. PMID:26717571

New Colors for Histology: Optimized Bivariate Color Maps Increase Perceptual Contrast in Histological Images.

PubMed

Kather, Jakob Nikolas; Weis, Cleo-Aron; Marx, Alexander; Schuster, Alexander K; Schad, Lothar R; Zöllner, Frank Gerrit

2015-01-01

Accurate evaluation of immunostained histological images is required for reproducible research in many different areas and forms the basis of many clinical decisions. The quality and efficiency of histopathological evaluation is limited by the information content of a histological image, which is primarily encoded as perceivable contrast differences between objects in the image. However, the colors of chromogen and counterstain used for histological samples are not always optimally distinguishable, even under optimal conditions. In this study, we present a method to extract the bivariate color map inherent in a given histological image and to retrospectively optimize this color map. We use a novel, unsupervised approach based on color deconvolution and principal component analysis to show that the commonly used blue and brown color hues in Hematoxylin-3,3'-Diaminobenzidine (DAB) images are poorly suited for human observers. We then demonstrate that it is possible to construct improved color maps according to objective criteria and that these color maps can be used to digitally re-stain histological images. To validate whether this procedure improves distinguishability of objects and background in histological images, we re-stain phantom images and N = 596 large histological images of immunostained samples of human solid tumors. We show that perceptual contrast is improved by a factor of 2.56 in phantom images and up to a factor of 2.17 in sets of histological tumor images. Thus, we provide an objective and reliable approach to measure object distinguishability in a given histological image and to maximize visual information available to a human observer. This method could easily be incorporated in digital pathology image viewing systems to improve accuracy and efficiency in research and diagnostics.

Blind deconvolution with principal components analysis for wide-field and small-aperture telescopes

NASA Astrophysics Data System (ADS)

Jia, Peng; Sun, Rongyu; Wang, Weinan; Cai, Dongmei; Liu, Huigen

2017-09-01

Telescopes with a wide field of view (greater than 1°) and small apertures (less than 2 m) are workhorses for observations such as sky surveys and fast-moving object detection, and play an important role in time-domain astronomy. However, images captured by these telescopes are contaminated by optical system aberrations, atmospheric turbulence, tracking errors and wind shear. To increase the quality of images and maximize their scientific output, we propose a new blind deconvolution algorithm based on statistical properties of the point spread functions (PSFs) of these telescopes. In this new algorithm, we first construct the PSF feature space through principal component analysis, and then classify PSFs from a different position and time using a self-organizing map. According to the classification results, we divide images of the same PSF types and select these PSFs to construct a prior PSF. The prior PSF is then used to restore these images. To investigate the improvement that this algorithm provides for data reduction, we process images of space debris captured by our small-aperture wide-field telescopes. Comparing the reduced results of the original images and the images processed with the standard Richardson-Lucy method, our method shows a promising improvement in astrometry accuracy.

Brightness-preserving fuzzy contrast enhancement scheme for the detection and classification of diabetic retinopathy disease.

PubMed

Datta, Niladri Sekhar; Dutta, Himadri Sekhar; Majumder, Koushik

2016-01-01

The contrast enhancement of retinal image plays a vital role for the detection of microaneurysms (MAs), which are an early sign of diabetic retinopathy disease. A retinal image contrast enhancement method has been presented to improve the MA detection technique. The success rate on low-contrast noisy retinal image analysis shows the importance of the proposed method. Overall, 587 retinal input images are tested for performance analysis. The average sensitivity and specificity are obtained as 95.94% and 99.21%, respectively. The area under curve is found as 0.932 for the receiver operating characteristics analysis. The classifications of diabetic retinopathy disease are also performed here. The experimental results show that the overall MA detection method performs better than the current state-of-the-art MA detection algorithms.

Point-of-care and point-of-procedure optical imaging technologies for primary care and global health

PubMed Central

Boppart, Stephen A.; Richards-Kortum, Rebecca

2015-01-01

Leveraging advances in consumer electronics and wireless telecommunications, low-cost, portable optical imaging devices have the potential to improve screening and detection of disease at the point of care in primary health care settings in both low- and high-resource countries. Similarly, real-time optical imaging technologies can improve diagnosis and treatment at the point of procedure by circumventing the need for biopsy and analysis by expert pathologists, who are scarce in developing countries. Although many optical imaging technologies have been translated from bench to bedside, industry support is needed to commercialize and broadly disseminate these from the patient level to the population level to transform the standard of care. This review provides an overview of promising optical imaging technologies, the infrastructure needed to integrate them into widespread clinical use, and the challenges that must be addressed to harness the potential of these technologies to improve health care systems around the world. PMID:25210062

Point-of-care and point-of-procedure optical imaging technologies for primary care and global health.

PubMed

Boppart, Stephen A; Richards-Kortum, Rebecca

2014-09-10

Leveraging advances in consumer electronics and wireless telecommunications, low-cost, portable optical imaging devices have the potential to improve screening and detection of disease at the point of care in primary health care settings in both low- and high-resource countries. Similarly, real-time optical imaging technologies can improve diagnosis and treatment at the point of procedure by circumventing the need for biopsy and analysis by expert pathologists, who are scarce in developing countries. Although many optical imaging technologies have been translated from bench to bedside, industry support is needed to commercialize and broadly disseminate these from the patient level to the population level to transform the standard of care. This review provides an overview of promising optical imaging technologies, the infrastructure needed to integrate them into widespread clinical use, and the challenges that must be addressed to harness the potential of these technologies to improve health care systems around the world. Copyright © 2014, American Association for the Advancement of Science.

Near Global Mosaic of Mercury

NASA Astrophysics Data System (ADS)

Becker, K. J.; Robinson, M. S.; Becker, T. L.; Weller, L. A.; Turner, S.; Nguyen, L.; Selby, C.; Denevi, B. W.; Murchie, S. L.; McNutt, R. L.; Solomon, S. C.

2009-12-01

In 2008 the MESSENGER spacecraft made two close flybys (M1 and M2) of Mercury and imaged about 74% of the planet at a resolution of 1 km per pixel, and at higher resolution for smaller portions of the planet. The Mariner 10 spacecraft imaged about 42% of Mercury’s surface more than 30 years ago. Combining image data collected by the two missions yields coverage of about 83% of Mercury’s surface. MESSENGER will perform its third and final flyby of Mercury (M3) on 29 September 2009. This will yield approximately 86% coverage of Mercury, leaving only the north and south polar regions yet to be imaged by MESSENGER after orbit insertion in March 2011. A new global mosaic of Mercury was constructed using 325 images containing 3566 control points (8110 measures) from M1 and 225 images containing 1465 control points (3506 measures) from M2. The M3 flyby is shifted in subsolar longitude only by 4° from M2, so the added coverage is very small. However, this small slice of Mercury fills a gore in the mosaic between the M1 and M2 data and allows a complete cartographic tie around the equator. We will run a new bundle block adjustment with the additional images acquired from M3. This new edition of the MESSENGER Mercury Dual Imaging System (MDIS) Narrow Angle Camera (NAC) global mosaic of Mercury includes many improvements since the M2 flyby in October 2008. A new distortion model for the NAC camera greatly improves the image-to-image registration. Optical distortion correction is independent of pointing error correction, and both are required for a mosaic of high quality. The new distortion model alone reduced residual pointing errors for both flybys significantly; residual pixel error improved from 0.71 average (3.7 max) to 0.13 average (1.7 max) for M1 and from 0.72 average (4.8 max.) to 0.17 average (3.5 max) for M2. Analysis quantifying pivot motor position has led to development of a new model that improves accuracy of the pivot platform attitude. This model improves the accuracy of pointing knowledge and reduces overall registration errors between adjacent images. The net effect of these improvements is an overall offset of up to 10 km in some locations across the mosaic. In addition, the radiometric calibration process for the NAC has been improved to yield a better dynamic range across the mosaic by 20%. The new global mosaic of Mercury will be used in scientific analysis and aid in planning observation sequences leading up to and including orbit insertion of the MESSENGER spacecraft in 2011.

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.

Automated analysis of high-content microscopy data with deep learning.

PubMed

Kraus, Oren Z; Grys, Ben T; Ba, Jimmy; Chong, Yolanda; Frey, Brendan J; Boone, Charles; Andrews, Brenda J

2017-04-18

Existing computational pipelines for quantitative analysis of high-content microscopy data rely on traditional machine learning approaches that fail to accurately classify more than a single dataset without substantial tuning and training, requiring extensive analysis. Here, we demonstrate that the application of deep learning to biological image data can overcome the pitfalls associated with conventional machine learning classifiers. Using a deep convolutional neural network (DeepLoc) to analyze yeast cell images, we show improved performance over traditional approaches in the automated classification of protein subcellular localization. We also demonstrate the ability of DeepLoc to classify highly divergent image sets, including images of pheromone-arrested cells with abnormal cellular morphology, as well as images generated in different genetic backgrounds and in different laboratories. We offer an open-source implementation that enables updating DeepLoc on new microscopy datasets. This study highlights deep learning as an important tool for the expedited analysis of high-content microscopy data. © 2017 The Authors. Published under the terms of the CC BY 4.0 license.

Improved cardiac motion detection from ultrasound images using TDIOF: a combined B-mode/ tissue Doppler approach

NASA Astrophysics Data System (ADS)

Tavakoli, Vahid; Stoddard, Marcus F.; Amini, Amir A.

2013-03-01

Quantitative motion analysis of echocardiographic images helps clinicians with the diagnosis and therapy of patients suffering from cardiac disease. Quantitative analysis is usually based on TDI (Tissue Doppler Imaging) or speckle tracking. These methods are based on two independent techniques - the Doppler Effect and image registration, respectively. In order to increase the accuracy of the speckle tracking technique and cope with the angle dependency of TDI, herein, a combined approach dubbed TDIOF (Tissue Doppler Imaging Optical Flow) is proposed. TDIOF is formulated based on the combination of B-mode and Doppler energy terms in an optical flow framework and minimized using algebraic equations. In this paper, we report on validations with simulated, physical cardiac phantom, and in-vivo patient data. It is shown that the additional Doppler term is able to increase the accuracy of speckle tracking, the basis for several commercially available echocardiography analysis techniques.

Combined spectral-domain optical coherence tomography and hyperspectral imaging applied for tissue analysis: Preliminary results

NASA Astrophysics Data System (ADS)

Dontu, S.; Miclos, S.; Savastru, D.; Tautan, M.

2017-09-01

In recent years many optoelectronic techniques have been developed for improvement and the development of devices for tissue analysis. Spectral-Domain Optical Coherence Tomography (SD-OCT) is a new medical interferometric imaging modality that provides depth resolved tissue structure information with resolution in the μm range. However, SD-OCT has its own limitations and cannot offer the biochemical information of the tissue. These data can be obtained with hyperspectral imaging, a non-invasive, sensitive and real time technique. In the present study we have combined Spectral-Domain Optical Coherence Tomography (SD-OCT) with Hyperspectral imaging (HSI) for tissue analysis. The Spectral-Domain Optical Coherence Tomography (SD-OCT) and Hyperspectral imaging (HSI) are two methods that have demonstrated significant potential in this context. Preliminary results using different tissue have highlighted the capabilities of this technique of combinations.

A scene-analysis approach to remote sensing. [San Francisco, California

NASA Technical Reports Server (NTRS)

Tenenbaum, J. M. (Principal Investigator); Fischler, M. A.; Wolf, H. C.

1978-01-01

The author has identified the following significant results. Geometric correspondance between a sensed image and a symbolic map is established in an initial stage of processing by adjusting parameters of a sensed model so that the image features predicted from the map optimally match corresponding features extracted from the sensed image. Information in the map is then used to constrain where to look in an image, what to look for, and how to interpret what is seen. For simple monitoring tasks involving multispectral classification, these constraints significantly reduce computation, simplify interpretation, and improve the utility of the resulting information. Previously intractable tasks requiring spatial and textural analysis may become straightforward in the context established by the map knowledge. The use of map-guided image analysis in monitoring the volume of water in a reservoir, the number of boxcars in a railyard, and the number of ships in a harbor is demonstrated.

SEM Imaging and Chemical Analysis of Aerosol Particles from Surface and Hi-altitudes in New Jersey.

NASA Astrophysics Data System (ADS)

Bandamede, M.; Boaggio, K.; Bancroft, L.; Hurler, K.; Magee, N. B.

2016-12-01

We report on Scanning Electron Microscopy analysis of aerosol particle morphology and chemistry. The work includes the first comparative SEM analysis of aerosol particles captured by balloon at high altitude. The particles were acquired in an urban/suburban environment in central New-Jersey. Particles were sampled from near the surface using ambient air filtration and at high-altitudes using a novel balloon-borne instrument (ICE-Ball, see abstract by K. Boaggio). Particle images and 3D geometry are acquired by a Hitachi SU-5000 SEM, with resolution to approximately 3 nm. Elemental analysis on particles is provided by Energy Dispersive X-Ray Spectroscopy (EDS, EDAX, Inc.). Uncoated imaging is conducted in low vacuum within the variable-pressure SEM, which provides improved detection and analysis of light-element compositions including Carbon. Preliminary results suggest that some similar particle types and chemical species are sampled at both surface and high-altitude. However, as expected, particle morphologies, concentrations, chemistry, and apparent origin vary significantly at different altitudes and under different atmospheric flow regimes. Improved characterization of high-altitude aerosol particles, and differences from surface particulate composition, may advance inputs for atmospheric cloud and radiation models.

Image quality and radiation dose of brain computed tomography in children: effects of decreasing tube voltage from 120 kVp to 80 kVp.

PubMed

Park, Ji Eun; Choi, Young Hun; Cheon, Jung-Eun; Kim, Woo Sun; Kim, In-One; Cho, Hyun Suk; Ryu, Young Jin; Kim, Yu Jin

2017-05-01

Computed tomography (CT) has generated public concern associated with radiation exposure, especially for children. Lowering the tube voltage is one strategy to reduce radiation dose. To assess the image quality and radiation dose of non-enhanced brain CT scans acquired at 80 kilo-voltage peak (kVp) compared to those at 120 kVp in children. Thirty children who had undergone both 80- and 120-kVp non-enhanced brain CT were enrolled. For quantitative analysis, the mean attenuation of white and gray matter, attenuation difference, noise, signal-to-noise ratio, contrast-to-noise ratio and posterior fossa artifact index were measured. For qualitative analysis, noise, gray-white matter differentiation, artifact and overall image quality were scored. Radiation doses were evaluated by CT dose index, dose-length product and effective dose. The mean attenuations of gray and white matter and contrast-to-noise ratio were significantly increased at 80 kVp, while parameters related to image noise, i.e. noise, signal-to-noise ratio and posterior fossa artifact index were higher at 80 kVp than at 120 kVp. In qualitative analysis, 80-kVp images showed improved gray-white differentiation but more artifacts compared to 120-kVp images. Subjective image noise and overall image quality scores were similar between the two scans. Radiation dose parameters were significantly lower at 80 kVp than at 120 kVp. In pediatric non-enhanced brain CT scans, a decrease in tube voltage from 120 kVp to 80 kVp resulted in improved gray-white matter contrast, comparable image quality and decreased radiation dose.

Improved classification and visualization of healthy and pathological hard dental tissues by modeling specular reflections in NIR hyperspectral images

NASA Astrophysics Data System (ADS)

Usenik, Peter; Bürmen, Miran; Fidler, Aleš; Pernuš, Franjo; Likar, Boštjan

2012-03-01

Despite major improvements in dental healthcare and technology, dental caries remains one of the most prevalent chronic diseases of modern society. The initial stages of dental caries are characterized by demineralization of enamel crystals, commonly known as white spots, which are difficult to diagnose. Near-infrared (NIR) hyperspectral imaging is a new promising technique for early detection of demineralization which can classify healthy and pathological dental tissues. However, due to non-ideal illumination of the tooth surface the hyperspectral images can exhibit specular reflections, in particular around the edges and the ridges of the teeth. These reflections significantly affect the performance of automated classification and visualization methods. Cross polarized imaging setup can effectively remove the specular reflections, however is due to the complexity and other imaging setup limitations not always possible. In this paper, we propose an alternative approach based on modeling the specular reflections of hard dental tissues, which significantly improves the classification accuracy in the presence of specular reflections. The method was evaluated on five extracted human teeth with corresponding gold standard for 6 different healthy and pathological hard dental tissues including enamel, dentin, calculus, dentin caries, enamel caries and demineralized regions. Principal component analysis (PCA) was used for multivariate local modeling of healthy and pathological dental tissues. The classification was performed by employing multiple discriminant analysis. Based on the obtained results we believe the proposed method can be considered as an effective alternative to the complex cross polarized imaging setups.

Advanced image collection, information extraction, and change detection in support of NN-20 broad area search and analysis

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

Petrie, G.M.; Perry, E.M.; Kirkham, R.R.

1997-09-01

This report describes the work performed at the Pacific Northwest National Laboratory (PNNL) for the U.S. Department of Energy`s Office of Nonproliferation and National Security, Office of Research and Development (NN-20). The work supports the NN-20 Broad Area Search and Analysis, a program initiated by NN-20 to improve the detection and classification of undeclared weapons facilities. Ongoing PNNL research activities are described in three main components: image collection, information processing, and change analysis. The Multispectral Airborne Imaging System, which was developed to collect georeferenced imagery in the visible through infrared regions of the spectrum, and flown on a light aircraftmore » platform, will supply current land use conditions. The image information extraction software (dynamic clustering and end-member extraction) uses imagery, like the multispectral data collected by the PNNL multispectral system, to efficiently generate landcover information. The advanced change detection uses a priori (benchmark) information, current landcover conditions, and user-supplied rules to rank suspect areas by probable risk of undeclared facilities or proliferation activities. These components, both separately and combined, provide important tools for improving the detection of undeclared facilities.« less

Calibration of HST wide field camera for quantitative analysis of faint galaxy images

NASA Technical Reports Server (NTRS)

Ratnatunga, Kavan U.; Griffiths, Richard E.; Casertano, Stefano; Neuschaefer, Lyman W.; Wyckoff, Eric W.

1994-01-01

We present the methods adopted to optimize the calibration of images obtained with the Hubble Space Telescope (HST) Wide Field Camera (WFC) (1991-1993). Our main goal is to improve quantitative measurement of faint images, with special emphasis on the faint (I approximately 20-24 mag) stars and galaxies observed as a part of the Medium-Deep Survey. Several modifications to the standard calibration procedures have been introduced, including improved bias and dark images, and a new supersky flatfield obtained by combining a large number of relatively object-free Medium-Deep Survey exposures of random fields. The supersky flat has a pixel-to-pixel rms error of about 2.0% in F555W and of 2.4% in F785LP; large-scale variations are smaller than 1% rms. Overall, our modifications improve the quality of faint images with respect to the standard calibration by about a factor of five in photometric accuracy and about 0.3 mag in sensitivity, corresponding to about a factor of two in observing time. The relevant calibration images have been made available to the scientific community.

Land use/cover classification in the Brazilian Amazon using satellite images.

PubMed

Lu, Dengsheng; Batistella, Mateus; Li, Guiying; Moran, Emilio; Hetrick, Scott; Freitas, Corina da Costa; Dutra, Luciano Vieira; Sant'anna, Sidnei João Siqueira

2012-09-01

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation-based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi-resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical-based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.

Land use/cover classification in the Brazilian Amazon using satellite images

PubMed Central

Lu, Dengsheng; Batistella, Mateus; Li, Guiying; Moran, Emilio; Hetrick, Scott; Freitas, Corina da Costa; Dutra, Luciano Vieira; Sant’Anna, Sidnei João Siqueira

2013-01-01

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation-based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi-resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical-based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data. PMID:24353353

Computer aided detection system for Osteoporosis using low dose thoracic 3D CT images

NASA Astrophysics Data System (ADS)

Tsuji, Daisuke; Matsuhiro, Mikio; Suzuki, Hidenobu; Kawata, Yoshiki; Niki, Noboru; Nakano, Yasutaka; Harada, Masafumi; Kusumoto, Masahiko; Tsuchida, Takaaki; Eguchi, Kenji; Kaneko, Masahiro

2018-02-01

The patient of osteoporosis is about 13 million people in Japan and it is one of healthy life problems in the aging society. It is necessary to do early stage detection and treatment in order to prevent the osteoporosis. Multi-slice CT technology has been improving the three dimensional (3D) image analysis with higher resolution and shorter scan time. The 3D image analysis of thoracic vertebra can be used for supporting to diagnosis of osteoporosis. This analysis can be used for lung cancer detection at the same time. We develop method of shape analysis and CT values of spongy bone for the detection osteoporosis. Osteoporosis and lung cancer screening show high extraction rate by the thoracic vertebral evaluation CT images. In addition, we created standard pattern of CT value per thoracic vertebra for male age group using 298 low dose data.

Computer-aided diagnosis for osteoporosis using chest 3D CT images

NASA Astrophysics Data System (ADS)

Yoneda, K.; Matsuhiro, M.; Suzuki, H.; Kawata, Y.; Niki, N.; Nakano, Y.; Ohmatsu, H.; Kusumoto, M.; Tsuchida, T.; Eguchi, K.; Kaneko, M.

2016-03-01

The patients of osteoporosis comprised of about 13 million people in Japan and it is one of the problems the aging society has. In order to prevent the osteoporosis, it is necessary to do early detection and treatment. Multi-slice CT technology has been improving the three dimensional (3-D) image analysis with higher body axis resolution and shorter scan time. The 3-D image analysis using multi-slice CT images of thoracic vertebra can be used as a support to diagnose osteoporosis and at the same time can be used for lung cancer diagnosis which may lead to early detection. We develop automatic extraction and partitioning algorithm for spinal column by analyzing vertebral body structure, and the analysis algorithm of the vertebral body using shape analysis and a bone density measurement for the diagnosis of osteoporosis. Osteoporosis diagnosis support system obtained high extraction rate of the thoracic vertebral in both normal and low doses.

Image quality in thoracic 4D cone-beam CT: A sensitivity analysis of respiratory signal, binning method, reconstruction algorithm, and projection angular spacing

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

Shieh, Chun-Chien; Kipritidis, John; O’Brien, Ricky T.

Purpose: Respiratory signal, binning method, and reconstruction algorithm are three major controllable factors affecting image quality in thoracic 4D cone-beam CT (4D-CBCT), which is widely used in image guided radiotherapy (IGRT). Previous studies have investigated each of these factors individually, but no integrated sensitivity analysis has been performed. In addition, projection angular spacing is also a key factor in reconstruction, but how it affects image quality is not obvious. An investigation of the impacts of these four factors on image quality can help determine the most effective strategy in improving 4D-CBCT for IGRT. Methods: Fourteen 4D-CBCT patient projection datasets withmore » various respiratory motion features were reconstructed with the following controllable factors: (i) respiratory signal (real-time position management, projection image intensity analysis, or fiducial marker tracking), (ii) binning method (phase, displacement, or equal-projection-density displacement binning), and (iii) reconstruction algorithm [Feldkamp–Davis–Kress (FDK), McKinnon–Bates (MKB), or adaptive-steepest-descent projection-onto-convex-sets (ASD-POCS)]. The image quality was quantified using signal-to-noise ratio (SNR), contrast-to-noise ratio, and edge-response width in order to assess noise/streaking and blur. The SNR values were also analyzed with respect to the maximum, mean, and root-mean-squared-error (RMSE) projection angular spacing to investigate how projection angular spacing affects image quality. Results: The choice of respiratory signals was found to have no significant impact on image quality. Displacement-based binning was found to be less prone to motion artifacts compared to phase binning in more than half of the cases, but was shown to suffer from large interbin image quality variation and large projection angular gaps. Both MKB and ASD-POCS resulted in noticeably improved image quality almost 100% of the time relative to FDK. In addition, SNR values were found to increase with decreasing RMSE values of projection angular gaps with strong correlations (r ≈ −0.7) regardless of the reconstruction algorithm used. Conclusions: Based on the authors’ results, displacement-based binning methods, better reconstruction algorithms, and the acquisition of even projection angular views are the most important factors to consider for improving thoracic 4D-CBCT image quality. In view of the practical issues with displacement-based binning and the fact that projection angular spacing is not currently directly controllable, development of better reconstruction algorithms represents the most effective strategy for improving image quality in thoracic 4D-CBCT for IGRT applications at the current stage.« less

An improved contrast enhancement algorithm for infrared images based on adaptive double plateaus histogram equalization

NASA Astrophysics Data System (ADS)

Li, Shuo; Jin, Weiqi; Li, Li; Li, Yiyang

2018-05-01

Infrared thermal images can reflect the thermal-radiation distribution of a particular scene. However, the contrast of the infrared images is usually low. Hence, it is generally necessary to enhance the contrast of infrared images in advance to facilitate subsequent recognition and analysis. Based on the adaptive double plateaus histogram equalization, this paper presents an improved contrast enhancement algorithm for infrared thermal images. In the proposed algorithm, the normalized coefficient of variation of the histogram, which characterizes the level of contrast enhancement, is introduced as feedback information to adjust the upper and lower plateau thresholds. The experiments on actual infrared images show that compared to the three typical contrast-enhancement algorithms, the proposed algorithm has better scene adaptability and yields better contrast-enhancement results for infrared images with more dark areas or a higher dynamic range. Hence, it has high application value in contrast enhancement, dynamic range compression, and digital detail enhancement for infrared thermal images.

Theory and applications of structured light single pixel imaging

NASA Astrophysics Data System (ADS)

Stokoe, Robert J.; Stockton, Patrick A.; Pezeshki, Ali; Bartels, Randy A.

2018-02-01

Many single-pixel imaging techniques have been developed in recent years. Though the methods of image acquisition vary considerably, the methods share unifying features that make general analysis possible. Furthermore, the methods developed thus far are based on intuitive processes that enable simple and physically-motivated reconstruction algorithms, however, this approach may not leverage the full potential of single-pixel imaging. We present a general theoretical framework of single-pixel imaging based on frame theory, which enables general, mathematically rigorous analysis. We apply our theoretical framework to existing single-pixel imaging techniques, as well as provide a foundation for developing more-advanced methods of image acquisition and reconstruction. The proposed frame theoretic framework for single-pixel imaging results in improved noise robustness, decrease in acquisition time, and can take advantage of special properties of the specimen under study. By building on this framework, new methods of imaging with a single element detector can be developed to realize the full potential associated with single-pixel imaging.

Computerized image analysis for quantitative neuronal phenotyping in zebrafish.

PubMed

Liu, Tianming; Lu, Jianfeng; Wang, Ye; Campbell, William A; Huang, Ling; Zhu, Jinmin; Xia, Weiming; Wong, Stephen T C

2006-06-15

An integrated microscope image analysis pipeline is developed for automatic analysis and quantification of phenotypes in zebrafish with altered expression of Alzheimer's disease (AD)-linked genes. We hypothesize that a slight impairment of neuronal integrity in a large number of zebrafish carrying the mutant genotype can be detected through the computerized image analysis method. Key functionalities of our zebrafish image processing pipeline include quantification of neuron loss in zebrafish embryos due to knockdown of AD-linked genes, automatic detection of defective somites, and quantitative measurement of gene expression levels in zebrafish with altered expression of AD-linked genes or treatment with a chemical compound. These quantitative measurements enable the archival of analyzed results and relevant meta-data. The structured database is organized for statistical analysis and data modeling to better understand neuronal integrity and phenotypic changes of zebrafish under different perturbations. Our results show that the computerized analysis is comparable to manual counting with equivalent accuracy and improved efficacy and consistency. Development of such an automated data analysis pipeline represents a significant step forward to achieve accurate and reproducible quantification of neuronal phenotypes in large scale or high-throughput zebrafish imaging studies.

Land-cover classification in a moist tropical region of Brazil with Landsat TM imagery.

PubMed

Li, Guiying; Lu, Dengsheng; Moran, Emilio; Hetrick, Scott

2011-01-01

This research aims to improve land-cover classification accuracy in a moist tropical region in Brazil by examining the use of different remote sensing-derived variables and classification algorithms. Different scenarios based on Landsat Thematic Mapper (TM) spectral data and derived vegetation indices and textural images, and different classification algorithms - maximum likelihood classification (MLC), artificial neural network (ANN), classification tree analysis (CTA), and object-based classification (OBC), were explored. The results indicated that a combination of vegetation indices as extra bands into Landsat TM multispectral bands did not improve the overall classification performance, but the combination of textural images was valuable for improving vegetation classification accuracy. In particular, the combination of both vegetation indices and textural images into TM multispectral bands improved overall classification accuracy by 5.6% and kappa coefficient by 6.25%. Comparison of the different classification algorithms indicated that CTA and ANN have poor classification performance in this research, but OBC improved primary forest and pasture classification accuracies. This research indicates that use of textural images or use of OBC are especially valuable for improving the vegetation classes such as upland and liana forest classes having complex stand structures and having relatively large patch sizes.

Land-cover classification in a moist tropical region of Brazil with Landsat TM imagery

PubMed Central

LI, GUIYING; LU, DENGSHENG; MORAN, EMILIO; HETRICK, SCOTT

2011-01-01

This research aims to improve land-cover classification accuracy in a moist tropical region in Brazil by examining the use of different remote sensing-derived variables and classification algorithms. Different scenarios based on Landsat Thematic Mapper (TM) spectral data and derived vegetation indices and textural images, and different classification algorithms – maximum likelihood classification (MLC), artificial neural network (ANN), classification tree analysis (CTA), and object-based classification (OBC), were explored. The results indicated that a combination of vegetation indices as extra bands into Landsat TM multispectral bands did not improve the overall classification performance, but the combination of textural images was valuable for improving vegetation classification accuracy. In particular, the combination of both vegetation indices and textural images into TM multispectral bands improved overall classification accuracy by 5.6% and kappa coefficient by 6.25%. Comparison of the different classification algorithms indicated that CTA and ANN have poor classification performance in this research, but OBC improved primary forest and pasture classification accuracies. This research indicates that use of textural images or use of OBC are especially valuable for improving the vegetation classes such as upland and liana forest classes having complex stand structures and having relatively large patch sizes. PMID:22368311

Rapid 3D Reconstruction for Image Sequence Acquired from UAV Camera

PubMed Central

Qu, Yufu; Huang, Jianyu; Zhang, Xuan

2018-01-01

In order to reconstruct three-dimensional (3D) structures from an image sequence captured by unmanned aerial vehicles’ camera (UAVs) and improve the processing speed, we propose a rapid 3D reconstruction method that is based on an image queue, considering the continuity and relevance of UAV camera images. The proposed approach first compresses the feature points of each image into three principal component points by using the principal component analysis method. In order to select the key images suitable for 3D reconstruction, the principal component points are used to estimate the interrelationships between images. Second, these key images are inserted into a fixed-length image queue. The positions and orientations of the images are calculated, and the 3D coordinates of the feature points are estimated using weighted bundle adjustment. With this structural information, the depth maps of these images can be calculated. Next, we update the image queue by deleting some of the old images and inserting some new images into the queue, and a structural calculation of all the images can be performed by repeating the previous steps. Finally, a dense 3D point cloud can be obtained using the depth–map fusion method. The experimental results indicate that when the texture of the images is complex and the number of images exceeds 100, the proposed method can improve the calculation speed by more than a factor of four with almost no loss of precision. Furthermore, as the number of images increases, the improvement in the calculation speed will become more noticeable. PMID:29342908

Rapid 3D Reconstruction for Image Sequence Acquired from UAV Camera.

PubMed

Qu, Yufu; Huang, Jianyu; Zhang, Xuan

2018-01-14

In order to reconstruct three-dimensional (3D) structures from an image sequence captured by unmanned aerial vehicles' camera (UAVs) and improve the processing speed, we propose a rapid 3D reconstruction method that is based on an image queue, considering the continuity and relevance of UAV camera images. The proposed approach first compresses the feature points of each image into three principal component points by using the principal component analysis method. In order to select the key images suitable for 3D reconstruction, the principal component points are used to estimate the interrelationships between images. Second, these key images are inserted into a fixed-length image queue. The positions and orientations of the images are calculated, and the 3D coordinates of the feature points are estimated using weighted bundle adjustment. With this structural information, the depth maps of these images can be calculated. Next, we update the image queue by deleting some of the old images and inserting some new images into the queue, and a structural calculation of all the images can be performed by repeating the previous steps. Finally, a dense 3D point cloud can be obtained using the depth-map fusion method. The experimental results indicate that when the texture of the images is complex and the number of images exceeds 100, the proposed method can improve the calculation speed by more than a factor of four with almost no loss of precision. Furthermore, as the number of images increases, the improvement in the calculation speed will become more noticeable.

Automated image analysis of placental villi and syncytial knots in histological sections.

PubMed

Kidron, Debora; Vainer, Ifat; Fisher, Yael; Sharony, Reuven

2017-05-01

Delayed villous maturation and accelerated villous maturation diagnosed in histologic sections are morphologic manifestations of pathophysiological conditions. The inter-observer agreement among pathologists in assessing these conditions is moderate at best. We investigated whether automated image analysis of placental villi and syncytial knots could improve standardization in diagnosing these conditions. Placentas of antepartum fetal death at or near term were diagnosed as normal, delayed or accelerated villous maturation. Histologic sections of 5 cases per group were photographed at ×10 magnification. Automated image analysis of villi and syncytial knots was performed, using ImageJ public domain software. Analysis of hundreds of histologic images was carried out within minutes on a personal computer, using macro commands. Compared to normal placentas, villi from delayed maturation were larger and fewer, with fewer and smaller syncytial knots. Villi from accelerated maturation were smaller. The data were further analyzed according to horizontal placental zones and groups of villous size. Normal placentas can be discriminated from placentas of delayed or accelerated villous maturation using automated image analysis. Automated image analysis of villi and syncytial knots is not equivalent to interpretation by the human eye. Each method has advantages and disadvantages in assessing the 2-dimensional histologic sections representing the complex, 3-dimensional villous tree. Image analysis of placentas provides quantitative data that might help in standardizing and grading of placentas for diagnostic and research purposes. Copyright © 2017 Elsevier Ltd. All rights reserved.

Combined use of iterative reconstruction and monochromatic imaging in spinal fusion CT images.

PubMed

Wang, Fengdan; Zhang, Yan; Xue, Huadan; Han, Wei; Yang, Xianda; Jin, Zhengyu; Zwar, Richard

2017-01-01

Spinal fusion surgery is an important procedure for treating spinal diseases and computed tomography (CT) is a critical tool for postoperative evaluation. However, CT image quality is considerably impaired by metal artifacts and image noise. To explore whether metal artifacts and image noise can be reduced by combining two technologies, adaptive statistical iterative reconstruction (ASIR) and monochromatic imaging generated by gemstone spectral imaging (GSI) dual-energy CT. A total of 51 patients with 318 spinal pedicle screws were prospectively scanned by dual-energy CT using fast kV-switching GSI between 80 and 140 kVp. Monochromatic GSI images at 110 keV were reconstructed either without or with various levels of ASIR (30%, 50%, 70%, and 100%). The quality of five sets of images was objectively and subjectively assessed. With objective image quality assessment, metal artifacts decreased when increasing levels of ASIR were applied (P < 0.001). Moreover, adding ASIR to GSI also decreased image noise (P < 0.001) and improved the signal-to-noise ratio (P < 0.001). The subjective image quality analysis showed good inter-reader concordance, with intra-class correlation coefficients between 0.89 and 0.99. The visualization of peri-implant soft tissue was improved at higher ASIR levels (P < 0.001). Combined use of ASIR and GSI decreased image noise and improved image quality in post-spinal fusion CT scans. Optimal results were achieved with ASIR levels ≥70%. © The Foundation Acta Radiologica 2016.

Improved automatic adjustment of density and contrast in FCR system using neural network

NASA Astrophysics Data System (ADS)

Takeo, Hideya; Nakajima, Nobuyoshi; Ishida, Masamitsu; Kato, Hisatoyo

1994-05-01

FCR system has an automatic adjustment of image density and contrast by analyzing the histogram of image data in the radiation field. Advanced image recognition methods proposed in this paper can improve the automatic adjustment performance, in which neural network technology is used. There are two methods. Both methods are basically used 3-layer neural network with back propagation. The image data are directly input to the input-layer in one method and the histogram data is input in the other method. The former is effective to the imaging menu such as shoulder joint in which the position of interest region occupied on the histogram changes by difference of positioning and the latter is effective to the imaging menu such as chest-pediatrics in which the histogram shape changes by difference of positioning. We experimentally confirm the validity of these methods (about the automatic adjustment performance) as compared with the conventional histogram analysis methods.

Metric Learning to Enhance Hyperspectral Image Segmentation

NASA Technical Reports Server (NTRS)

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

2013-01-01

Unsupervised hyperspectral image segmentation can reveal spatial trends that show the physical structure of the scene to an analyst. They highlight borders and reveal areas of homogeneity and change. Segmentations are independently helpful for object recognition, and assist with automated production of symbolic maps. Additionally, a good segmentation can dramatically reduce the number of effective spectra in an image, enabling analyses that would otherwise be computationally prohibitive. Specifically, using an over-segmentation of the image instead of individual pixels can reduce noise and potentially improve the results of statistical post-analysis. In this innovation, a metric learning approach is presented to improve the performance of unsupervised hyperspectral image segmentation. The prototype demonstrations attempt a superpixel segmentation in which the image is conservatively over-segmented; that is, the single surface features may be split into multiple segments, but each individual segment, or superpixel, is ensured to have homogenous mineralogy.

Quantitative Image Informatics for Cancer Research (QIICR) | Informatics Technology for Cancer Research (ITCR)

Cancer.gov

Imaging has enormous untapped potential to improve cancer research through software to extract and process morphometric and functional biomarkers. In the era of non-cytotoxic treatment agents, multi- modality image-guided ablative therapies and rapidly evolving computational resources, quantitative imaging software can be transformative in enabling minimally invasive, objective and reproducible evaluation of cancer treatment response. Post-processing algorithms are integral to high-throughput analysis and fine- grained differentiation of multiple molecular targets.

[A spatial adaptive algorithm for endmember extraction on multispectral remote sensing image].

PubMed

Zhu, Chang-Ming; Luo, Jian-Cheng; Shen, Zhan-Feng; Li, Jun-Li; Hu, Xiao-Dong

2011-10-01

Due to the problem that the convex cone analysis (CCA) method can only extract limited endmember in multispectral imagery, this paper proposed a new endmember extraction method by spatial adaptive spectral feature analysis in multispectral remote sensing image based on spatial clustering and imagery slice. Firstly, in order to remove spatial and spectral redundancies, the principal component analysis (PCA) algorithm was used for lowering the dimensions of the multispectral data. Secondly, iterative self-organizing data analysis technology algorithm (ISODATA) was used for image cluster through the similarity of the pixel spectral. And then, through clustering post process and litter clusters combination, we divided the whole image data into several blocks (tiles). Lastly, according to the complexity of image blocks' landscape and the feature of the scatter diagrams analysis, the authors can determine the number of endmembers. Then using hourglass algorithm extracts endmembers. Through the endmember extraction experiment on TM multispectral imagery, the experiment result showed that the method can extract endmember spectra form multispectral imagery effectively. What's more, the method resolved the problem of the amount of endmember limitation and improved accuracy of the endmember extraction. The method has provided a new way for multispectral image endmember extraction.

PlantCV v2: Image analysis software for high-throughput plant phenotyping

PubMed Central

Abbasi, Arash; Berry, Jeffrey C.; Callen, Steven T.; Chavez, Leonardo; Doust, Andrew N.; Feldman, Max J.; Gilbert, Kerrigan B.; Hodge, John G.; Hoyer, J. Steen; Lin, Andy; Liu, Suxing; Lizárraga, César; Lorence, Argelia; Miller, Michael; Platon, Eric; Tessman, Monica; Sax, Tony

2017-01-01

Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning. PMID:29209576

PlantCV v2: Image analysis software for high-throughput plant phenotyping.

PubMed

Gehan, Malia A; Fahlgren, Noah; Abbasi, Arash; Berry, Jeffrey C; Callen, Steven T; Chavez, Leonardo; Doust, Andrew N; Feldman, Max J; Gilbert, Kerrigan B; Hodge, John G; Hoyer, J Steen; Lin, Andy; Liu, Suxing; Lizárraga, César; Lorence, Argelia; Miller, Michael; Platon, Eric; Tessman, Monica; Sax, Tony

2017-01-01

Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.

PlantCV v2: Image analysis software for high-throughput plant phenotyping

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

Gehan, Malia A.; Fahlgren, Noah; Abbasi, Arash

Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here in this paper we present the details andmore » rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.« less

Adjacent slice prostate cancer prediction to inform MALDI imaging biomarker analysis

NASA Astrophysics Data System (ADS)

Chuang, Shao-Hui; Sun, Xiaoyan; Cazares, Lisa; Nyalwidhe, Julius; Troyer, Dean; Semmes, O. John; Li, Jiang; McKenzie, Frederic D.

2010-03-01

Prostate cancer is the second most common type of cancer among men in US [1]. Traditionally, prostate cancer diagnosis is made by the analysis of prostate-specific antigen (PSA) levels and histopathological images of biopsy samples under microscopes. Proteomic biomarkers can improve upon these methods. MALDI molecular spectra imaging is used to visualize protein/peptide concentrations across biopsy samples to search for biomarker candidates. Unfortunately, traditional processing methods require histopathological examination on one slice of a biopsy sample while the adjacent slice is subjected to the tissue destroying desorption and ionization processes of MALDI. The highest confidence tumor regions gained from the histopathological analysis are then mapped to the MALDI spectra data to estimate the regions for biomarker identification from the MALDI imaging. This paper describes a process to provide a significantly better estimate of the cancer tumor to be mapped onto the MALDI imaging spectra coordinates using the high confidence region to predict the true area of the tumor on the adjacent MALDI imaged slice.

Self-adaptive relevance feedback based on multilevel image content analysis

NASA Astrophysics Data System (ADS)

Gao, Yongying; Zhang, Yujin; Fu, Yu

2001-01-01

In current content-based image retrieval systems, it is generally accepted that obtaining high-level image features is a key to improve the querying. Among the related techniques, relevance feedback has become a hot research aspect because it combines the information from the user to refine the querying results. In practice, many methods have been proposed to achieve the goal of relevance feedback. In this paper, a new scheme for relevance feedback is proposed. Unlike previous methods for relevance feedback, our scheme provides a self-adaptive operation. First, based on multi- level image content analysis, the relevant images from the user could be automatically analyzed in different levels and the querying could be modified in terms of different analysis results. Secondly, to make it more convenient to the user, the procedure of relevance feedback could be led with memory or without memory. To test the performance of the proposed method, a practical semantic-based image retrieval system has been established, and the querying results gained by our self-adaptive relevance feedback are given.

Self-adaptive relevance feedback based on multilevel image content analysis

NASA Astrophysics Data System (ADS)

Gao, Yongying; Zhang, Yujin; Fu, Yu

2000-12-01

In current content-based image retrieval systems, it is generally accepted that obtaining high-level image features is a key to improve the querying. Among the related techniques, relevance feedback has become a hot research aspect because it combines the information from the user to refine the querying results. In practice, many methods have been proposed to achieve the goal of relevance feedback. In this paper, a new scheme for relevance feedback is proposed. Unlike previous methods for relevance feedback, our scheme provides a self-adaptive operation. First, based on multi- level image content analysis, the relevant images from the user could be automatically analyzed in different levels and the querying could be modified in terms of different analysis results. Secondly, to make it more convenient to the user, the procedure of relevance feedback could be led with memory or without memory. To test the performance of the proposed method, a practical semantic-based image retrieval system has been established, and the querying results gained by our self-adaptive relevance feedback are given.

PlantCV v2: Image analysis software for high-throughput plant phenotyping

DOE PAGES

Gehan, Malia A.; Fahlgren, Noah; Abbasi, Arash; ...

2017-12-01

Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here in this paper we present the details andmore » rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.« less

Performing Quantitative Imaging Acquisition, Analysis and Visualization Using the Best of Open Source and Commercial Software Solutions.

PubMed

Shenoy, Shailesh M

2016-07-01

A challenge in any imaging laboratory, especially one that uses modern techniques, is to achieve a sustainable and productive balance between using open source and commercial software to perform quantitative image acquisition, analysis and visualization. In addition to considering the expense of software licensing, one must consider factors such as the quality and usefulness of the software's support, training and documentation. Also, one must consider the reproducibility with which multiple people generate results using the same software to perform the same analysis, how one may distribute their methods to the community using the software and the potential for achieving automation to improve productivity.

MIDAS - A microcomputer-based image display and analysis system with full Landsat frame processing capabilities

NASA Technical Reports Server (NTRS)

Hofman, L. B.; Erickson, W. K.; Donovan, W. E.

1984-01-01

Image Display and Analysis Systems (MIDAS) developed at NASA/Ames for the analysis of Landsat MSS images is described. The MIDAS computer power and memory, graphics, resource-sharing, expansion and upgrade, environment and maintenance, and software/user-interface requirements are outlined; the implementation hardware (including 32-bit microprocessor, 512K error-correcting RAM, 70 or 140-Mbyte formatted disk drive, 512 x 512 x 24 color frame buffer, and local-area-network transceiver) and applications software (ELAS, CIE, and P-EDITOR) are characterized; and implementation problems, performance data, and costs are examined. Planned improvements in MIDAS hardware and design goals and areas of exploration for MIDAS software are discussed.

Image processing and machine learning for fully automated probabilistic evaluation of medical images.

PubMed

Sajn, Luka; Kukar, Matjaž

2011-12-01

The paper presents results of our long-term study on using image processing and data mining methods in a medical imaging. Since evaluation of modern medical images is becoming increasingly complex, advanced analytical and decision support tools are involved in integration of partial diagnostic results. Such partial results, frequently obtained from tests with substantial imperfections, are integrated into ultimate diagnostic conclusion about the probability of disease for a given patient. We study various topics such as improving the predictive power of clinical tests by utilizing pre-test and post-test probabilities, texture representation, multi-resolution feature extraction, feature construction and data mining algorithms that significantly outperform medical practice. Our long-term study reveals three significant milestones. The first improvement was achieved by significantly increasing post-test diagnostic probabilities with respect to expert physicians. The second, even more significant improvement utilizes multi-resolution image parametrization. Machine learning methods in conjunction with the feature subset selection on these parameters significantly improve diagnostic performance. However, further feature construction with the principle component analysis on these features elevates results to an even higher accuracy level that represents the third milestone. With the proposed approach clinical results are significantly improved throughout the study. The most significant result of our study is improvement in the diagnostic power of the whole diagnostic process. Our compound approach aids, but does not replace, the physician's judgment and may assist in decisions on cost effectiveness of tests. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Medical image computing for computer-supported diagnostics and therapy. Advances and perspectives.

PubMed

Handels, H; Ehrhardt, J

2009-01-01

Medical image computing has become one of the most challenging fields in medical informatics. In image-based diagnostics of the future software assistance will become more and more important, and image analysis systems integrating advanced image computing methods are needed to extract quantitative image parameters to characterize the state and changes of image structures of interest (e.g. tumors, organs, vessels, bones etc.) in a reproducible and objective way. Furthermore, in the field of software-assisted and navigated surgery medical image computing methods play a key role and have opened up new perspectives for patient treatment. However, further developments are needed to increase the grade of automation, accuracy, reproducibility and robustness. Moreover, the systems developed have to be integrated into the clinical workflow. For the development of advanced image computing systems methods of different scientific fields have to be adapted and used in combination. The principal methodologies in medical image computing are the following: image segmentation, image registration, image analysis for quantification and computer assisted image interpretation, modeling and simulation as well as visualization and virtual reality. Especially, model-based image computing techniques open up new perspectives for prediction of organ changes and risk analysis of patients and will gain importance in diagnostic and therapy of the future. From a methodical point of view the authors identify the following future trends and perspectives in medical image computing: development of optimized application-specific systems and integration into the clinical workflow, enhanced computational models for image analysis and virtual reality training systems, integration of different image computing methods, further integration of multimodal image data and biosignals and advanced methods for 4D medical image computing. The development of image analysis systems for diagnostic support or operation planning is a complex interdisciplinary process. Image computing methods enable new insights into the patient's image data and have the future potential to improve medical diagnostics and patient treatment.

Retrospective single center study of the efficacy of large spot 532 nm laser for the treatment of facial capillary malformations in 44 patients with the use of three-dimensional image analysis.

PubMed

Kwiek, Bartłomiej; Rożalski, Michał; Kowalewski, Cezary; Ambroziak, Marcin

2017-10-01

We wanted to asses the efficacy of large spot 532 nm laser for the treatment of facial capillary malformations with the use of three-dimensional (3D) image analysis. Retrospective single center study on previously non-treated patients with facial capillary malformations (CM) was performed. A total of 44 consecutive Caucasian patients aged 5-66 were included. Patients had 3D photography performed before and after and had at least one single session of treatment with 532 nm neodymium-doped yttrium aluminum garnet (Nd:YAG) laser with contact cooling, fluencies ranging from 8 to 11.5 J/cm 2 , pulse duration ranging from 5 to 9 milliseconds and spot size ranging from 5 to 10 mm. Objective analysis of percentage improvement based on 3D digital assessment of combined color and area improvement (global clearance effect [GCE]) were performed. Median maximal improvement achieved during the treatment (GCE max ) was 70.4%. Mean number of laser procedures required to achieve this improvement was 7.1 (ranging from 2 to 14)). Improvement of minimum 25% (GCE 25) was achieved by all patients, of minimum 50% (GCE 50) by 77.3%, of minimum 75% (GCE 75) by 38.6%, and of minimum 90% (GCE 90) by 13.64. Large spot 532 nm laser is highly effective in the treatment of facial CM. 3D color and area image analysis provides an objective method to compare different methods of facial CM treatment in future studies. Lasers Surg. Med. 49:743-749, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Validation of an improved 'diffeomorphic demons' algorithm for deformable image registration in image-guided radiation therapy.

PubMed

Zhou, Lu; Zhou, Linghong; Zhang, Shuxu; Zhen, Xin; Yu, Hui; Zhang, Guoqian; Wang, Ruihao

2014-01-01

Deformable image registration (DIR) was widely used in radiation therapy, such as in automatic contour generation, dose accumulation, tumor growth or regression analysis. To achieve higher registration accuracy and faster convergence, an improved 'diffeomorphic demons' registration algorithm was proposed and validated. Based on Brox et al.'s gradient constancy assumption and Malis's efficient second-order minimization (ESM) algorithm, a grey value gradient similarity term and a transformation error term were added into the demons energy function, and a formula was derived to calculate the update of transformation field. The limited Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm was used to optimize the energy function so that the iteration number could be determined automatically. The proposed algorithm was validated using mathematically deformed images and physically deformed phantom images. Compared with the original 'diffeomorphic demons' algorithm, the registration method proposed achieve a higher precision and a faster convergence speed. Due to the influence of different scanning conditions in fractionated radiation, the density range of the treatment image and the planning image may be different. In such a case, the improved demons algorithm can achieve faster and more accurate radiotherapy.

Star centroiding error compensation for intensified star sensors.

PubMed

Jiang, Jie; Xiong, Kun; Yu, Wenbo; Yan, Jinyun; Zhang, Guangjun

2016-12-26

A star sensor provides high-precision attitude information by capturing a stellar image; however, the traditional star sensor has poor dynamic performance, which is attributed to its low sensitivity. Regarding the intensified star sensor, the image intensifier is utilized to improve the sensitivity, thereby further improving the dynamic performance of the star sensor. However, the introduction of image intensifier results in star centroiding accuracy decrease, further influencing the attitude measurement precision of the star sensor. A star centroiding error compensation method for intensified star sensors is proposed in this paper to reduce the influences. First, the imaging model of the intensified detector, which includes the deformation parameter of the optical fiber panel, is established based on the orthographic projection through the analysis of errors introduced by the image intensifier. Thereafter, the position errors at the target points based on the model are obtained by using the Levenberg-Marquardt (LM) optimization method. Last, the nearest trigonometric interpolation method is presented to compensate for the arbitrary centroiding error of the image plane. Laboratory calibration result and night sky experiment result show that the compensation method effectively eliminates the error introduced by the image intensifier, thus remarkably improving the precision of the intensified star sensors.

Computer-Aided Diagnostic System For Mass Survey Chest Images

NASA Astrophysics Data System (ADS)

Yasuda, Yoshizumi; Kinoshita, Yasuhiro; Emori, Yasufumi; Yoshimura, Hitoshi

1988-06-01

In order to support screening of chest radiographs on mass survey, a computer-aided diagnostic system that automatically detects abnormality of candidate images using a digital image analysis technique has been developed. Extracting boundary lines of lung fields and examining their shapes allowed various kind of abnormalities to be detected. Correction and expansion were facilitated by describing the system control, image analysis control and judgement of abnormality in the rule type programing language. In the experiments using typical samples of student's radiograms, good results were obtained for the detection of abnormal shape of lung field, cardiac hypertrophy and scoliosis. As for the detection of diaphragmatic abnormality, relatively good results were obtained but further improvements will be necessary.

A Novel Anti-Spoofing Solution for Iris Recognition Toward Cosmetic Contact Lens Attack Using Spectral ICA Analysis

PubMed Central

Hsieh, Sheng-Hsun; Wang, Wei; Tien, Chung-Hao

2018-01-01

In this study, we maneuvered a dual-band spectral imaging system to capture an iridal image from a cosmetic-contact-lens-wearing subject. By using the independent component analysis to separate individual spectral primitives, we successfully distinguished the natural iris texture from the cosmetic contact lens (CCL) pattern, and restored the genuine iris patterns from the CCL-polluted image. Based on a database containing 200 test image pairs from 20 CCL-wearing subjects as the proof of concept, the recognition accuracy (False Rejection Rate: FRR) was improved from FRR = 10.52% to FRR = 0.57% with the proposed ICA anti-spoofing scheme. PMID:29509692

Method for measuring anterior chamber volume by image analysis

NASA Astrophysics Data System (ADS)

Zhai, Gaoshou; Zhang, Junhong; Wang, Ruichang; Wang, Bingsong; Wang, Ningli

2007-12-01

Anterior chamber volume (ACV) is very important for an oculist to make rational pathological diagnosis as to patients who have some optic diseases such as glaucoma and etc., yet it is always difficult to be measured accurately. In this paper, a method is devised to measure anterior chamber volumes based on JPEG-formatted image files that have been transformed from medical images using the anterior-chamber optical coherence tomographer (AC-OCT) and corresponding image-processing software. The corresponding algorithms for image analysis and ACV calculation are implemented in VC++ and a series of anterior chamber images of typical patients are analyzed, while anterior chamber volumes are calculated and are verified that they are in accord with clinical observation. It shows that the measurement method is effective and feasible and it has potential to improve accuracy of ACV calculation. Meanwhile, some measures should be taken to simplify the handcraft preprocess working as to images.

Plasma properties of hot coronal loops utilizing coordinated SMM and solar research rocket observations

NASA Technical Reports Server (NTRS)

Moses, J. Daniel

1989-01-01

Three improvements in photographic x-ray imaging techniques for solar astronomy are presented. The testing and calibration of a new film processor was conducted; the resulting product will allow photometric development of sounding rocket flight film immediately upon recovery at the missile range. Two fine grained photographic films were calibrated and flight tested to provide alternative detector choices when the need for high resolution is greater than the need for high sensitivity. An analysis technique used to obtain the characteristic curve directly from photographs of UV solar spectra were applied to the analysis of soft x-ray photographic images. The resulting procedure provides a more complete and straightforward determination of the parameters describing the x-ray characteristic curve than previous techniques. These improvements fall into the category of refinements instead of revolutions, indicating the fundamental suitability of the photographic process for x-ray imaging in solar astronomy.

Developing an Automated Science Analysis System for Mars Surface Exploration for MSL and Beyond

NASA Technical Reports Server (NTRS)

Gulick, V. C.; Hart, S. D.; Shi, X.; Siegel, V. L.

2004-01-01

We are developing an automated science analysis system that could be utilized by robotic or human explorers on Mars (or even in remote locations on Earth) to improve the quality and quantity of science data returned. Three components of this system (our rock, layer, and horizon detectors) [1] have been incorporated into the JPL CLARITY system for possible use by MSL and future Mars robotic missions. Two other components include a multi-spectral image compression (SPEC) algorithm for pancam-type images with multiple filters and image fusion algorithms that identify the in focus regions of individual images in an image focal series [2]. Recently, we have been working to combine image and spectral data, and other knowledge to identify both rocks and minerals. Here we present our progress on developing an igneous rock detection system.

Computer system for scanning tunneling microscope automation

NASA Astrophysics Data System (ADS)

Aguilar, M.; García, A.; Pascual, P. J.; Presa, J.; Santisteban, A.

1987-03-01

A computerized system for the automation of a scanning tunneling microscope is presented. It is based on an IBM personal computer (PC) either an XT or an AT, which performs the control, data acquisition and storage operations, displays the STM "images" in real time, and provides image processing tools for the restoration and analysis of data. It supports different data acquisition and control cards and image display cards. The software has been designed in a modular way to allow the replacement of these cards and other equipment improvements as well as the inclusion of user routines for data analysis.

Quantitative digital image analysis of chromogenic assays for high throughput screening of alpha-amylase mutant libraries.

PubMed

Shankar, Manoharan; Priyadharshini, Ramachandran; Gunasekaran, Paramasamy

2009-08-01

An image analysis-based method for high throughput screening of an alpha-amylase mutant library using chromogenic assays was developed. Assays were performed in microplates and high resolution images of the assay plates were read using the Virtual Microplate Reader (VMR) script to quantify the concentration of the chromogen. This method is fast and sensitive in quantifying 0.025-0.3 mg starch/ml as well as 0.05-0.75 mg glucose/ml. It was also an effective screening method for improved alpha-amylase activity with a coefficient of variance of 18%.

Analysing magnetism using scanning SQUID microscopy.

PubMed

Reith, P; Renshaw Wang, X; Hilgenkamp, H

2017-12-01

Scanning superconducting quantum interference device microscopy (SSM) is a scanning probe technique that images local magnetic flux, which allows for mapping of magnetic fields with high field and spatial accuracy. Many studies involving SSM have been published in the last few decades, using SSM to make qualitative statements about magnetism. However, quantitative analysis using SSM has received less attention. In this work, we discuss several aspects of interpreting SSM images and methods to improve quantitative analysis. First, we analyse the spatial resolution and how it depends on several factors. Second, we discuss the analysis of SSM scans and the information obtained from the SSM data. Using simulations, we show how signals evolve as a function of changing scan height, SQUID loop size, magnetization strength, and orientation. We also investigated 2-dimensional autocorrelation analysis to extract information about the size, shape, and symmetry of magnetic features. Finally, we provide an outlook on possible future applications and improvements.

Analysing magnetism using scanning SQUID microscopy

NASA Astrophysics Data System (ADS)

Reith, P.; Renshaw Wang, X.; Hilgenkamp, H.

2017-12-01

Scanning superconducting quantum interference device microscopy (SSM) is a scanning probe technique that images local magnetic flux, which allows for mapping of magnetic fields with high field and spatial accuracy. Many studies involving SSM have been published in the last few decades, using SSM to make qualitative statements about magnetism. However, quantitative analysis using SSM has received less attention. In this work, we discuss several aspects of interpreting SSM images and methods to improve quantitative analysis. First, we analyse the spatial resolution and how it depends on several factors. Second, we discuss the analysis of SSM scans and the information obtained from the SSM data. Using simulations, we show how signals evolve as a function of changing scan height, SQUID loop size, magnetization strength, and orientation. We also investigated 2-dimensional autocorrelation analysis to extract information about the size, shape, and symmetry of magnetic features. Finally, we provide an outlook on possible future applications and improvements.

Improved medical image fusion based on cascaded PCA and shift invariant wavelet transforms.

PubMed

Reena Benjamin, J; Jayasree, T

2018-02-01

In the medical field, radiologists need more informative and high-quality medical images to diagnose diseases. Image fusion plays a vital role in the field of biomedical image analysis. It aims to integrate the complementary information from multimodal images, producing a new composite image which is expected to be more informative for visual perception than any of the individual input images. The main objective of this paper is to improve the information, to preserve the edges and to enhance the quality of the fused image using cascaded principal component analysis (PCA) and shift invariant wavelet transforms. A novel image fusion technique based on cascaded PCA and shift invariant wavelet transforms is proposed in this paper. PCA in spatial domain extracts relevant information from the large dataset based on eigenvalue decomposition, and the wavelet transform operating in the complex domain with shift invariant properties brings out more directional and phase details of the image. The significance of maximum fusion rule applied in dual-tree complex wavelet transform domain enhances the average information and morphological details. The input images of the human brain of two different modalities (MRI and CT) are collected from whole brain atlas data distributed by Harvard University. Both MRI and CT images are fused using cascaded PCA and shift invariant wavelet transform method. The proposed method is evaluated based on three main key factors, namely structure preservation, edge preservation, contrast preservation. The experimental results and comparison with other existing fusion methods show the superior performance of the proposed image fusion framework in terms of visual and quantitative evaluations. In this paper, a complex wavelet-based image fusion has been discussed. The experimental results demonstrate that the proposed method enhances the directional features as well as fine edge details. Also, it reduces the redundant details, artifacts, distortions.

Peripheral Quantitative CT (pQCT) Using a Dedicated Extremity Cone-Beam CT Scanner

PubMed Central

Muhit, A. A.; Arora, S.; Ogawa, M.; Ding, Y.; Zbijewski, W.; Stayman, J. W.; Thawait, G.; Packard, N.; Senn, R.; Yang, D.; Yorkston, J.; Bingham, C.O.; Means, K.; Carrino, J. A.; Siewerdsen, J. H.

2014-01-01

Purpose We describe the initial assessment of the peripheral quantitative CT (pQCT) imaging capabilities of a cone-beam CT (CBCT) scanner dedicated to musculoskeletal extremity imaging. The aim is to accurately measure and quantify bone and joint morphology using information automatically acquired with each CBCT scan, thereby reducing the need for a separate pQCT exam. Methods A prototype CBCT scanner providing isotropic, sub-millimeter spatial resolution and soft-tissue contrast resolution comparable or superior to standard multi-detector CT (MDCT) has been developed for extremity imaging, including the capability for weight-bearing exams and multi-mode (radiography, fluoroscopy, and volumetric) imaging. Assessment of pQCT performance included measurement of bone mineral density (BMD), morphometric parameters of subchondral bone architecture, and joint space analysis. Measurements employed phantoms, cadavers, and patients from an ongoing pilot study imaged with the CBCT prototype (at various acquisition, calibration, and reconstruction techniques) in comparison to MDCT (using pQCT protocols for analysis of BMD) and micro-CT (for analysis of subchondral morphometry). Results The CBCT extremity scanner yielded BMD measurement within ±2–3% error in both phantom studies and cadaver extremity specimens. Subchondral bone architecture (bone volume fraction, trabecular thickness, degree of anisotropy, and structure model index) exhibited good correlation with gold standard micro-CT (error ~5%), surpassing the conventional limitations of spatial resolution in clinical MDCT scanners. Joint space analysis demonstrated the potential for sensitive 3D joint space mapping beyond that of qualitative radiographic scores in application to non-weight-bearing versus weight-bearing lower extremities and assessment of phalangeal joint space integrity in the upper extremities. Conclusion The CBCT extremity scanner demonstrated promising initial results in accurate pQCT analysis from images acquired with each CBCT scan. Future studies will include improved x-ray scatter correction and image reconstruction techniques to further improve accuracy and to correlate pQCT metrics with known pathology. PMID:25076823

Adapting the ISO 20462 softcopy ruler method for online image quality studies

NASA Astrophysics Data System (ADS)

Burns, Peter D.; Phillips, Jonathan B.; Williams, Don

2013-01-01

In this paper we address the problem of Image Quality Assessment of no reference metrics, focusing on JPEG corrupted images. In general no reference metrics are not able to measure with the same performance the distortions within their possible range and with respect to different image contents. The crosstalk between content and distortion signals influences the human perception. We here propose two strategies to improve the correlation between subjective and objective quality data. The first strategy is based on grouping the images according to their spatial complexity. The second one is based on a frequency analysis. Both the strategies are tested on two databases available in the literature. The results show an improvement in the correlations between no reference metrics and psycho-visual data, evaluated in terms of the Pearson Correlation Coefficient.

Note: An improved 3D imaging system for electron-electron coincidence measurements

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

Lin, Yun Fei; Lee, Suk Kyoung; Adhikari, Pradip

We demonstrate an improved imaging system that can achieve highly efficient 3D detection of two electrons in coincidence. The imaging system is based on a fast frame complementary metal-oxide semiconductor camera and a high-speed waveform digitizer. We have shown previously that this detection system is capable of 3D detection of ions and electrons with good temporal and spatial resolution. Here, we show that with a new timing analysis algorithm, this system can achieve an unprecedented dead-time (<0.7 ns) and dead-space (<1 mm) when detecting two electrons. A true zero dead-time detection is also demonstrated.

Creating the "desired mindset": Philip Morris's efforts to improve its corporate image among women.

PubMed

McDaniel, Patricia A; Malone, Ruth E

2009-01-01

Through analysis of tobacco company documents, we explored how and why Philip Morris sought to enhance its corporate image among American women. Philip Morris regarded women as an influential political group. To improve its image among women, while keeping tobacco off their organizational agendas, the company sponsored women's groups and programs. It also sought to appeal to women it defined as "active moms" by advertising its commitment to domestic violence victims. It was more successful in securing women's organizations as allies than active moms. Increasing tobacco's visibility as a global women's health issue may require addressing industry influence.

Note: An improved 3D imaging system for electron-electron coincidence measurements

NASA Astrophysics Data System (ADS)

Lin, Yun Fei; Lee, Suk Kyoung; Adhikari, Pradip; Herath, Thushani; Lingenfelter, Steven; Winney, Alexander H.; Li, Wen

2015-09-01

We demonstrate an improved imaging system that can achieve highly efficient 3D detection of two electrons in coincidence. The imaging system is based on a fast frame complementary metal-oxide semiconductor camera and a high-speed waveform digitizer. We have shown previously that this detection system is capable of 3D detection of ions and electrons with good temporal and spatial resolution. Here, we show that with a new timing analysis algorithm, this system can achieve an unprecedented dead-time (<0.7 ns) and dead-space (<1 mm) when detecting two electrons. A true zero dead-time detection is also demonstrated.

The Advanced Gamma-ray Imaging System (AGIS): A Nanosecond Time Scale Stereoscopic Array Trigger System.

NASA Astrophysics Data System (ADS)

Krennrich, Frank; Buckley, J.; Byrum, K.; Dawson, J.; Drake, G.; Horan, D.; Krawzcynski, H.; Schroedter, M.

2008-04-01

Imaging atmospheric Cherenkov telescope arrays (VERITAS, HESS) have shown unprecedented background suppression capabilities for reducing cosmic-ray induced air showers, muons and night sky background fluctuations. Next-generation arrays with on the order of 100 telescopes offer larger collection areas, provide the possibility to see the air shower from more view points on the ground, have the potential to improve the sensitivity and give additional background suppression. Here we discuss the design of a fast array trigger system that has the potential to perform a real time image analysis allowing substantially improved background rate suppression at the trigger level.

Optical coherence tomography – current and future applications

PubMed Central

Adhi, Mehreen; Duker, Jay S.

2013-01-01

Purpose of review Optical coherence tomography (OCT) has revolutionized the clinical practice of ophthalmology. It is a noninvasive imaging technique that provides high-resolution, cross-sectional images of the retina, retinal nerve fiber layer and the optic nerve head. This review discusses the present applications of the commercially available spectral-domain OCT (SD-OCT) systems in the diagnosis and management of retinal diseases, with particular emphasis on choroidal imaging. Future directions of OCT technology and their potential clinical uses are discussed. Recent findings Analysis of the choroidal thickness in healthy eyes and disease states such as age-related macular degeneration, central serous chorioretinopathy, diabetic retinopathy and inherited retinal dystrophies has been successfully achieved using SD-OCT devices with software improvements. Future OCT innovations such as longer-wavelength OCT systems including the swept-source technology, along with Doppler OCT and en-face imaging, may improve the detection of subtle microstructural changes in chorioretinal diseases by improving imaging of the choroid. Summary Advances in OCT technology provide for better understanding of pathogenesis, improved monitoring of progression and assistance in quantifying response to treatment modalities in diseases of the posterior segment of the eye. Further improvements in both hardware and software technologies should further advance the clinician’s ability to assess and manage chorioretinal diseases. PMID:23429598

Cell nuclei and cytoplasm joint segmentation using the sliding band filter.

PubMed

Quelhas, Pedro; Marcuzzo, Monica; Mendonça, Ana Maria; Campilho, Aurélio

2010-08-01

Microscopy cell image analysis is a fundamental tool for biological research. In particular, multivariate fluorescence microscopy is used to observe different aspects of cells in cultures. It is still common practice to perform analysis tasks by visual inspection of individual cells which is time consuming, exhausting and prone to induce subjective bias. This makes automatic cell image analysis essential for large scale, objective studies of cell cultures. Traditionally the task of automatic cell analysis is approached through the use of image segmentation methods for extraction of cells' locations and shapes. Image segmentation, although fundamental, is neither an easy task in computer vision nor is it robust to image quality changes. This makes image segmentation for cell detection semi-automated requiring frequent tuning of parameters. We introduce a new approach for cell detection and shape estimation in multivariate images based on the sliding band filter (SBF). This filter's design makes it adequate to detect overall convex shapes and as such it performs well for cell detection. Furthermore, the parameters involved are intuitive as they are directly related to the expected cell size. Using the SBF filter we detect cells' nucleus and cytoplasm location and shapes. Based on the assumption that each cell has the same approximate shape center in both nuclei and cytoplasm fluorescence channels, we guide cytoplasm shape estimation by the nuclear detections improving performance and reducing errors. Then we validate cell detection by gathering evidence from nuclei and cytoplasm channels. Additionally, we include overlap correction and shape regularization steps which further improve the estimated cell shapes. The approach is evaluated using two datasets with different types of data: a 20 images benchmark set of simulated cell culture images, containing 1000 simulated cells; a 16 images Drosophila melanogaster Kc167 dataset containing 1255 cells, stained for DNA and actin. Both image datasets present a difficult problem due to the high variability of cell shapes and frequent cluster overlap between cells. On the Drosophila dataset our approach achieved a precision/recall of 95%/69% and 82%/90% for nuclei and cytoplasm detection respectively and an overall accuracy of 76%.

Regional fringe analysis for improving depth measurement in phase-shifting fringe projection profilometry

NASA Astrophysics Data System (ADS)

Chien, Kuang-Che Chang; Tu, Han-Yen; Hsieh, Ching-Huang; Cheng, Chau-Jern; Chang, Chun-Yen

2018-01-01

This study proposes a regional fringe analysis (RFA) method to detect the regions of a target object in captured shifted images to improve depth measurement in phase-shifting fringe projection profilometry (PS-FPP). In the RFA method, region-based segmentation is exploited to segment the de-fringed image of a target object, and a multi-level fuzzy-based classification with five presented features is used to analyze and discriminate the regions of an object from the segmented regions, which were associated with explicit fringe information. Then, in the experiment, the performance of the proposed method is tested and evaluated on 26 test cases made of five types of materials. The qualitative and quantitative results demonstrate that the proposed RFA method can effectively detect the desired regions of an object to improve depth measurement in the PS-FPP system.

The influence of biological and technical factors on quantitative analysis of amyloid PET: Points to consider and recommendations for controlling variability in longitudinal data.

PubMed

Schmidt, Mark E; Chiao, Ping; Klein, Gregory; Matthews, Dawn; Thurfjell, Lennart; Cole, Patricia E; Margolin, Richard; Landau, Susan; Foster, Norman L; Mason, N Scott; De Santi, Susan; Suhy, Joyce; Koeppe, Robert A; Jagust, William

2015-09-01

In vivo imaging of amyloid burden with positron emission tomography (PET) provides a means for studying the pathophysiology of Alzheimer's and related diseases. Measurement of subtle changes in amyloid burden requires quantitative analysis of image data. Reliable quantitative analysis of amyloid PET scans acquired at multiple sites and over time requires rigorous standardization of acquisition protocols, subject management, tracer administration, image quality control, and image processing and analysis methods. We review critical points in the acquisition and analysis of amyloid PET, identify ways in which technical factors can contribute to measurement variability, and suggest methods for mitigating these sources of noise. Improved quantitative accuracy could reduce the sample size necessary to detect intervention effects when amyloid PET is used as a treatment end point and allow more reliable interpretation of change in amyloid burden and its relationship to clinical course. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

SU-E-J-110: A Novel Level Set Active Contour Algorithm for Multimodality Joint Segmentation/Registration Using the Jensen-Rényi Divergence.

PubMed

Markel, D; Naqa, I El; Freeman, C; Vallières, M

2012-06-01

To present a novel joint segmentation/registration for multimodality image-guided and adaptive radiotherapy. A major challenge to this framework is the sensitivity of many segmentation or registration algorithms to noise. Presented is a level set active contour based on the Jensen-Renyi (JR) divergence to achieve improved noise robustness in a multi-modality imaging space. To present a novel joint segmentation/registration for multimodality image-guided and adaptive radiotherapy. A major challenge to this framework is the sensitivity of many segmentation or registration algorithms to noise. Presented is a level set active contour based on the Jensen-Renyi (JR) divergence to achieve improved noise robustness in a multi-modality imaging space. It was found that JR divergence when used for segmentation has an improved robustness to noise compared to using mutual information, or other entropy-based metrics. The MI metric failed at around 2/3 the noise power than the JR divergence. The JR divergence metric is useful for the task of joint segmentation/registration of multimodality images and shows improved results compared entropy based metric. The algorithm can be easily modified to incorporate non-intensity based images, which would allow applications into multi-modality and texture analysis. © 2012 American Association of Physicists in Medicine.

Improving the scalability of hyperspectral imaging applications on heterogeneous platforms using adaptive run-time data compression

NASA Astrophysics Data System (ADS)

Plaza, Antonio; Plaza, Javier; Paz, Abel

2010-10-01

Latest generation remote sensing instruments (called hyperspectral imagers) are now able to generate hundreds of images, corresponding to different wavelength channels, for the same area on the surface of the Earth. In previous work, we have reported that the scalability of parallel processing algorithms dealing with these high-dimensional data volumes is affected by the amount of data to be exchanged through the communication network of the system. However, large messages are common in hyperspectral imaging applications since processing algorithms are pixel-based, and each pixel vector to be exchanged through the communication network is made up of hundreds of spectral values. Thus, decreasing the amount of data to be exchanged could improve the scalability and parallel performance. In this paper, we propose a new framework based on intelligent utilization of wavelet-based data compression techniques for improving the scalability of a standard hyperspectral image processing chain on heterogeneous networks of workstations. This type of parallel platform is quickly becoming a standard in hyperspectral image processing due to the distributed nature of collected hyperspectral data as well as its flexibility and low cost. Our experimental results indicate that adaptive lossy compression can lead to improvements in the scalability of the hyperspectral processing chain without sacrificing analysis accuracy, even at sub-pixel precision levels.

Enhancement of automated blood flow estimates (ENABLE) from arterial spin-labeled MRI.

PubMed

Shirzadi, Zahra; Stefanovic, Bojana; Chappell, Michael A; Ramirez, Joel; Schwindt, Graeme; Masellis, Mario; Black, Sandra E; MacIntosh, Bradley J

2018-03-01

To validate a multiparametric automated algorithm-ENhancement of Automated Blood fLow Estimates (ENABLE)-that identifies useful and poor arterial spin-labeled (ASL) difference images in multiple postlabeling delay (PLD) acquisitions and thereby improve clinical ASL. ENABLE is a sort/check algorithm that uses a linear combination of ASL quality features. ENABLE uses simulations to determine quality weighting factors based on an unconstrained nonlinear optimization. We acquired a set of 6-PLD ASL images with 1.5T or 3.0T systems among 98 healthy elderly and adults with mild cognitive impairment or dementia. We contrasted signal-to-noise ratio (SNR) of cerebral blood flow (CBF) images obtained with ENABLE vs. conventional ASL analysis. In a subgroup, we validated our CBF estimates with single-photon emission computed tomography (SPECT) CBF images. ENABLE produced significantly increased SNR compared to a conventional ASL analysis (Wilcoxon signed-rank test, P < 0.0001). We also found the similarity between ASL and SPECT was greater when using ENABLE vs. conventional ASL analysis (n = 51, Wilcoxon signed-rank test, P < 0.0001) and this similarity was strongly related to ASL SNR (t = 24, P < 0.0001). These findings suggest that ENABLE improves CBF image quality from multiple PLD ASL in dementia cohorts at either 1.5T or 3.0T, achieved by multiparametric quality features that guided postprocessing of dementia ASL. 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:647-655. © 2017 International Society for Magnetic Resonance in Medicine.

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.

Dynamic CT perfusion imaging of the myocardium: a technical note on improvement of image quality.

PubMed

Muenzel, Daniela; Kabus, Sven; Gramer, Bettina; Leber, Vivian; Vembar, Mani; Schmitt, Holger; Wildgruber, Moritz; Fingerle, Alexander A; Rummeny, Ernst J; Huber, Armin; Noël, Peter B

2013-01-01

To improve image and diagnostic quality in dynamic CT myocardial perfusion imaging (MPI) by using motion compensation and a spatio-temporal filter. Dynamic CT MPI was performed using a 256-slice multidetector computed tomography scanner (MDCT). Data from two different patients-with and without myocardial perfusion defects-were evaluated to illustrate potential improvements for MPI (institutional review board approved). Three datasets for each patient were generated: (i) original data (ii) motion compensated data and (iii) motion compensated data with spatio-temporal filtering performed. In addition to the visual assessment of the tomographic slices, noise and contrast-to-noise-ratio (CNR) were measured for all data. Perfusion analysis was performed using time-density curves with regions-of-interest (ROI) placed in normal and hypoperfused myocardium. Precision in definition of normal and hypoperfused areas was determined in corresponding coloured perfusion maps. The use of motion compensation followed by spatio-temporal filtering resulted in better alignment of the cardiac volumes over time leading to a more consistent perfusion quantification and improved detection of the extend of perfusion defects. Additionally image noise was reduced by 78.5%, with CNR improvements by a factor of 4.7. The average effective radiation dose estimate was 7.1±1.1 mSv. The use of motion compensation and spatio-temporal smoothing will result in improved quantification of dynamic CT MPI using a latest generation CT scanner.

Comparison of three‐dimensional analysis and stereological techniques for quantifying lithium‐ion battery electrode microstructures

PubMed Central

TAIWO, OLUWADAMILOLA O.; FINEGAN, DONAL P.; EASTWOOD, DAVID S.; FIFE, JULIE L.; BROWN, LEON D.; DARR, JAWWAD A.; LEE, PETER D.; BRETT, DANIEL J.L.

2016-01-01

Summary Lithium‐ion battery performance is intrinsically linked to electrode microstructure. Quantitative measurement of key structural parameters of lithium‐ion battery electrode microstructures will enable optimization as well as motivate systematic numerical studies for the improvement of battery performance. With the rapid development of 3‐D imaging techniques, quantitative assessment of 3‐D microstructures from 2‐D image sections by stereological methods appears outmoded; however, in spite of the proliferation of tomographic imaging techniques, it remains significantly easier to obtain two‐dimensional (2‐D) data sets. In this study, stereological prediction and three‐dimensional (3‐D) analysis techniques for quantitative assessment of key geometric parameters for characterizing battery electrode microstructures are examined and compared. Lithium‐ion battery electrodes were imaged using synchrotron‐based X‐ray tomographic microscopy. For each electrode sample investigated, stereological analysis was performed on reconstructed 2‐D image sections generated from tomographic imaging, whereas direct 3‐D analysis was performed on reconstructed image volumes. The analysis showed that geometric parameter estimation using 2‐D image sections is bound to be associated with ambiguity and that volume‐based 3‐D characterization of nonconvex, irregular and interconnected particles can be used to more accurately quantify spatially‐dependent parameters, such as tortuosity and pore‐phase connectivity. PMID:26999804

Comparison of three-dimensional analysis and stereological techniques for quantifying lithium-ion battery electrode microstructures.

PubMed

Taiwo, Oluwadamilola O; Finegan, Donal P; Eastwood, David S; Fife, Julie L; Brown, Leon D; Darr, Jawwad A; Lee, Peter D; Brett, Daniel J L; Shearing, Paul R

2016-09-01

Lithium-ion battery performance is intrinsically linked to electrode microstructure. Quantitative measurement of key structural parameters of lithium-ion battery electrode microstructures will enable optimization as well as motivate systematic numerical studies for the improvement of battery performance. With the rapid development of 3-D imaging techniques, quantitative assessment of 3-D microstructures from 2-D image sections by stereological methods appears outmoded; however, in spite of the proliferation of tomographic imaging techniques, it remains significantly easier to obtain two-dimensional (2-D) data sets. In this study, stereological prediction and three-dimensional (3-D) analysis techniques for quantitative assessment of key geometric parameters for characterizing battery electrode microstructures are examined and compared. Lithium-ion battery electrodes were imaged using synchrotron-based X-ray tomographic microscopy. For each electrode sample investigated, stereological analysis was performed on reconstructed 2-D image sections generated from tomographic imaging, whereas direct 3-D analysis was performed on reconstructed image volumes. The analysis showed that geometric parameter estimation using 2-D image sections is bound to be associated with ambiguity and that volume-based 3-D characterization of nonconvex, irregular and interconnected particles can be used to more accurately quantify spatially-dependent parameters, such as tortuosity and pore-phase connectivity. © 2016 The Authors. Journal of Microscopy published by John Wiley & Sons Ltd on behalf of Royal Microscopical Society.

LAS - LAND ANALYSIS SYSTEM, VERSION 5.0

NASA Technical Reports Server (NTRS)

Pease, P. B.

1994-01-01

The Land Analysis System (LAS) is an image analysis system designed to manipulate and analyze digital data in raster format and provide the user with a wide spectrum of functions and statistical tools for analysis. LAS offers these features under VMS with optional image display capabilities for IVAS and other display devices as well as the X-Windows environment. LAS provides a flexible framework for algorithm development as well as for the processing and analysis of image data. Users may choose between mouse-driven commands or the traditional command line input mode. LAS functions include supervised and unsupervised image classification, film product generation, geometric registration, image repair, radiometric correction and image statistical analysis. Data files accepted by LAS include formats such as Multi-Spectral Scanner (MSS), Thematic Mapper (TM) and Advanced Very High Resolution Radiometer (AVHRR). The enhanced geometric registration package now includes both image to image and map to map transformations. The over 200 LAS functions fall into image processing scenario categories which include: arithmetic and logical functions, data transformations, fourier transforms, geometric registration, hard copy output, image restoration, intensity transformation, multispectral and statistical analysis, file transfer, tape profiling and file management among others. Internal improvements to the LAS code have eliminated the VAX VMS dependencies and improved overall system performance. The maximum LAS image size has been increased to 20,000 lines by 20,000 samples with a maximum of 256 bands per image. The catalog management system used in earlier versions of LAS has been replaced by a more streamlined and maintenance-free method of file management. This system is not dependent on VAX/VMS and relies on file naming conventions alone to allow the use of identical LAS file names on different operating systems. While the LAS code has been improved, the original capabilities of the system have been preserved. These include maintaining associated image history, session logging, and batch, asynchronous and interactive mode of operation. The LAS application programs are integrated under version 4.1 of an interface called the Transportable Applications Executive (TAE). TAE 4.1 has four modes of user interaction: menu, direct command, tutor (or help), and dynamic tutor. In addition TAE 4.1 allows the operation of LAS functions using mouse-driven commands under the TAE-Facelift environment provided with TAE 4.1. These modes of operation allow users, from the beginner to the expert, to exercise specific application options. LAS is written in C-language and FORTRAN 77 for use with DEC VAX computers running VMS with approximately 16Mb of physical memory. This program runs under TAE 4.1. Since TAE 4.1 is not a current version of TAE, TAE 4.1 is included within the LAS distribution. Approximately 130,000 blocks (65Mb) of disk storage space are necessary to store the source code and files generated by the installation procedure for LAS and 44,000 blocks (22Mb) of disk storage space are necessary for TAE 4.1 installation. The only other dependencies for LAS are the subroutine libraries for the specific display device(s) that will be used with LAS/DMS (e.g. X-Windows and/or IVAS). The standard distribution medium for LAS is a set of two 9track 6250 BPI magnetic tapes in DEC VAX BACKUP format. It is also available on a set of two TK50 tape cartridges in DEC VAX BACKUP format. This program was developed in 1986 and last updated in 1992.

Quantitative assessment of image motion blur in diffraction images of moving biological cells

NASA Astrophysics Data System (ADS)

Wang, He; Jin, Changrong; Feng, Yuanming; Qi, Dandan; Sa, Yu; Hu, Xin-Hua

2016-02-01

Motion blur (MB) presents a significant challenge for obtaining high-contrast image data from biological cells with a polarization diffraction imaging flow cytometry (p-DIFC) method. A new p-DIFC experimental system has been developed to evaluate the MB and its effect on image analysis using a time-delay-integration (TDI) CCD camera. Diffraction images of MCF-7 and K562 cells have been acquired with different speed-mismatch ratios and compared to characterize MB quantitatively. Frequency analysis of the diffraction images shows that the degree of MB can be quantified by bandwidth variations of the diffraction images along the motion direction. The analytical results were confirmed by the p-DIFC image data acquired at different speed-mismatch ratios and used to validate a method of numerical simulation of MB on blur-free diffraction images, which provides a useful tool to examine the blurring effect on diffraction images acquired from the same cell. These results provide insights on the dependence of diffraction image on MB and allow significant improvement on rapid biological cell assay with the p-DIFC method.

Noise removal using factor analysis of dynamic structures: application to cardiac gated studies.

PubMed

Bruyant, P P; Sau, J; Mallet, J J

1999-10-01

Factor analysis of dynamic structures (FADS) facilitates the extraction of relevant data, usually with physiologic meaning, from a dynamic set of images. The result of this process is a set of factor images and curves plus some residual activity. The set of factor images and curves can be used to retrieve the original data with reduced noise using an inverse factor analysis process (iFADS). This improvement in image quality is expected because the inverse process does not use the residual activity, assumed to be made of noise. The goal of this work is to quantitate and assess the efficiency of this method on gated cardiac images. A computer simulation of a planar cardiac gated study was performed. The simulated images were added with noise and processed by the FADS-iFADS program. The signal-to-noise ratios (SNRs) were compared between original and processed data. Planar gated cardiac studies from 10 patients were tested. The data processed by FADS-iFADS were subtracted to the original data. The result of the substraction was studied to evaluate its noisy nature. The SNR is about five times greater after the FADS-iFADS process. The difference between original and processed data is noise only, i.e., processed data equals original data minus some white noise. The FADS-iFADS process is successful in the removal of an important part of the noise and therefore is a tool to improve the image quality of cardiac images. This tool does not decrease the spatial resolution (compared with smoothing filters) and does not lose details (compared with frequential filters). Once the number of factors is chosen, this method is not operator dependent.

Comparison of histomorphometrical data obtained with two different image analysis methods.

PubMed

Ballerini, Lucia; Franke-Stenport, Victoria; Borgefors, Gunilla; Johansson, Carina B

2007-08-01

A common way to determine tissue acceptance of biomaterials is to perform histomorphometrical analysis on histologically stained sections from retrieved samples with surrounding tissue, using various methods. The "time and money consuming" methods and techniques used are often "in house standards". We address light microscopic investigations of bone tissue reactions on un-decalcified cut and ground sections of threaded implants. In order to screen sections and generate results faster, the aim of this pilot project was to compare results generated with the in-house standard visual image analysis tool (i.e., quantifications and judgements done by the naked eye) with a custom made automatic image analysis program. The histomorphometrical bone area measurements revealed no significant differences between the methods but the results of the bony contacts varied significantly. The raw results were in relative agreement, i.e., the values from the two methods were proportional to each other: low bony contact values in the visual method corresponded to low values with the automatic method. With similar resolution images and further improvements of the automatic method this difference should become insignificant. A great advantage using the new automatic image analysis method is that it is time saving--analysis time can be significantly reduced.

PIRATE: pediatric imaging response assessment and targeting environment

NASA Astrophysics Data System (ADS)

Glenn, Russell; Zhang, Yong; Krasin, Matthew; Hua, Chiaho

2010-02-01

By combining the strengths of various imaging modalities, the multimodality imaging approach has potential to improve tumor staging, delineation of tumor boundaries, chemo-radiotherapy regime design, and treatment response assessment in cancer management. To address the urgent needs for efficient tools to analyze large-scale clinical trial data, we have developed an integrated multimodality, functional and anatomical imaging analysis software package for target definition and therapy response assessment in pediatric radiotherapy (RT) patients. Our software provides quantitative tools for automated image segmentation, region-of-interest (ROI) histogram analysis, spatial volume-of-interest (VOI) analysis, and voxel-wise correlation across modalities. To demonstrate the clinical applicability of this software, histogram analyses were performed on baseline and follow-up 18F-fluorodeoxyglucose (18F-FDG) PET images of nine patients with rhabdomyosarcoma enrolled in an institutional clinical trial at St. Jude Children's Research Hospital. In addition, we combined 18F-FDG PET, dynamic-contrast-enhanced (DCE) MR, and anatomical MR data to visualize the heterogeneity in tumor pathophysiology with the ultimate goal of adaptive targeting of regions with high tumor burden. Our software is able to simultaneously analyze multimodality images across multiple time points, which could greatly speed up the analysis of large-scale clinical trial data and validation of potential imaging biomarkers.

Improving the Accuracy of Mapping Urban Vegetation Carbon Density by Combining Shadow Remove, Spectral Unmixing Analysis and Spatial Modeling

NASA Astrophysics Data System (ADS)

Qie, G.; Wang, G.; Wang, M.

2016-12-01

Mixed pixels and shadows due to buildings in urban areas impede accurate estimation and mapping of city vegetation carbon density. In most of previous studies, these factors are often ignored, which thus result in underestimation of city vegetation carbon density. In this study we presented an integrated methodology to improve the accuracy of mapping city vegetation carbon density. Firstly, we applied a linear shadow remove analysis (LSRA) on remotely sensed Landsat 8 images to reduce the shadow effects on carbon estimation. Secondly, we integrated a linear spectral unmixing analysis (LSUA) with a linear stepwise regression (LSR), a logistic model-based stepwise regression (LMSR) and k-Nearest Neighbors (kNN), and utilized and compared the integrated models on shadow-removed images to map vegetation carbon density. This methodology was examined in Shenzhen City of Southeast China. A data set from a total of 175 sample plots measured in 2013 and 2014 was used to train the models. The independent variables statistically significantly contributing to improving the fit of the models to the data and reducing the sum of squared errors were selected from a total of 608 variables derived from different image band combinations and transformations. The vegetation fraction from LSUA was then added into the models as an important independent variable. The estimates obtained were evaluated using a cross-validation method. Our results showed that higher accuracies were obtained from the integrated models compared with the ones using traditional methods which ignore the effects of mixed pixels and shadows. This study indicates that the integrated method has great potential on improving the accuracy of urban vegetation carbon density estimation. Key words: Urban vegetation carbon, shadow, spectral unmixing, spatial modeling, Landsat 8 images

Joint PET-MR respiratory motion models for clinical PET motion correction

NASA Astrophysics Data System (ADS)

Manber, Richard; Thielemans, Kris; Hutton, Brian F.; Wan, Simon; McClelland, Jamie; Barnes, Anna; Arridge, Simon; Ourselin, Sébastien; Atkinson, David

2016-09-01

Patient motion due to respiration can lead to artefacts and blurring in positron emission tomography (PET) images, in addition to quantification errors. The integration of PET with magnetic resonance (MR) imaging in PET-MR scanners provides complementary clinical information, and allows the use of high spatial resolution and high contrast MR images to monitor and correct motion-corrupted PET data. In this paper we build on previous work to form a methodology for respiratory motion correction of PET data, and show it can improve PET image quality whilst having minimal impact on clinical PET-MR protocols. We introduce a joint PET-MR motion model, using only 1 min per PET bed position of simultaneously acquired PET and MR data to provide a respiratory motion correspondence model that captures inter-cycle and intra-cycle breathing variations. In the model setup, 2D multi-slice MR provides the dynamic imaging component, and PET data, via low spatial resolution framing and principal component analysis, provides the model surrogate. We evaluate different motion models (1D and 2D linear, and 1D and 2D polynomial) by computing model-fit and model-prediction errors on dynamic MR images on a data set of 45 patients. Finally we apply the motion model methodology to 5 clinical PET-MR oncology patient datasets. Qualitative PET reconstruction improvements and artefact reduction are assessed with visual analysis, and quantitative improvements are calculated using standardised uptake value (SUVpeak and SUVmax) changes in avid lesions. We demonstrate the capability of a joint PET-MR motion model to predict respiratory motion by showing significantly improved image quality of PET data acquired before the motion model data. The method can be used to incorporate motion into the reconstruction of any length of PET acquisition, with only 1 min of extra scan time, and with no external hardware required.

Efficiency analysis of color image filtering

NASA Astrophysics Data System (ADS)

Fevralev, Dmitriy V.; Ponomarenko, Nikolay N.; Lukin, Vladimir V.; Abramov, Sergey K.; Egiazarian, Karen O.; Astola, Jaakko T.

2011-12-01

This article addresses under which conditions filtering can visibly improve the image quality. The key points are the following. First, we analyze filtering efficiency for 25 test images, from the color image database TID2008. This database allows assessing filter efficiency for images corrupted by different noise types for several levels of noise variance. Second, the limit of filtering efficiency is determined for independent and identically distributed (i.i.d.) additive noise and compared to the output mean square error of state-of-the-art filters. Third, component-wise and vector denoising is studied, where the latter approach is demonstrated to be more efficient. Fourth, using of modern visual quality metrics, we determine that for which levels of i.i.d. and spatially correlated noise the noise in original images or residual noise and distortions because of filtering in output images are practically invisible. We also demonstrate that it is possible to roughly estimate whether or not the visual quality can clearly be improved by filtering.

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.

Optical design and system characterization of an imaging microscope at 121.6 nm

NASA Astrophysics Data System (ADS)

Gao, Weichuan; Finan, Emily; Kim, Geon-Hee; Kim, Youngsik; Milster, Thomas D.

2018-03-01

We present the optical design and system characterization of an imaging microscope prototype at 121.6 nm. System engineering processes are demonstrated through the construction of a Schwarzschild microscope objective, including tolerance analysis, fabrication, alignment, and testing. Further improvements on the as-built system with a correction phase plate are proposed and analyzed. Finally, the microscope assembly and the imaging properties of the prototype are demonstrated.

Detection of neuron membranes in electron microscopy images using a serial neural network architecture.

PubMed

Jurrus, Elizabeth; Paiva, Antonio R C; Watanabe, Shigeki; Anderson, James R; Jones, Bryan W; Whitaker, Ross T; Jorgensen, Erik M; Marc, Robert E; Tasdizen, Tolga

2010-12-01

Study of nervous systems via the connectome, the map of connectivities of all neurons in that system, is a challenging problem in neuroscience. Towards this goal, neurobiologists are acquiring large electron microscopy datasets. However, the shear volume of these datasets renders manual analysis infeasible. Hence, automated image analysis methods are required for reconstructing the connectome from these very large image collections. Segmentation of neurons in these images, an essential step of the reconstruction pipeline, is challenging because of noise, anisotropic shapes and brightness, and the presence of confounding structures. The method described in this paper uses a series of artificial neural networks (ANNs) in a framework combined with a feature vector that is composed of image intensities sampled over a stencil neighborhood. Several ANNs are applied in series allowing each ANN to use the classification context provided by the previous network to improve detection accuracy. We develop the method of serial ANNs and show that the learned context does improve detection over traditional ANNs. We also demonstrate advantages over previous membrane detection methods. The results are a significant step towards an automated system for the reconstruction of the connectome. Copyright 2010 Elsevier B.V. All rights reserved.

3D-Printed Tissue-Mimicking Phantoms for Medical Imaging and Computational Validation Applications

PubMed Central

Shahmirzadi, Danial; Li, Ronny X.; Doyle, Barry J.; Konofagou, Elisa E.; McGloughlin, Tim M.

2014-01-01

Abstract Abdominal aortic aneurysm (AAA) is a permanent, irreversible dilation of the distal region of the aorta. Recent efforts have focused on improved AAA screening and biomechanics-based failure prediction. Idealized and patient-specific AAA phantoms are often employed to validate numerical models and imaging modalities. To produce such phantoms, the investment casting process is frequently used, reconstructing the 3D vessel geometry from computed tomography patient scans. In this study the alternative use of 3D printing to produce phantoms is investigated. The mechanical properties of flexible 3D-printed materials are benchmarked against proven elastomers. We demonstrate the utility of this process with particular application to the emerging imaging modality of ultrasound-based pulse wave imaging, a noninvasive diagnostic methodology being developed to obtain regional vascular wall stiffness properties, differentiating normal and pathologic tissue in vivo. Phantom wall displacements under pulsatile loading conditions were observed, showing good correlation to fluid–structure interaction simulations and regions of peak wall stress predicted by finite element analysis. 3D-printed phantoms show a strong potential to improve medical imaging and computational analysis, potentially helping bridge the gap between experimental and clinical diagnostic tools. PMID:28804733

Correction of Atmospheric Haze in RESOURCESAT-1 LISS-4 MX Data for Urban Analysis: AN Improved Dark Object Subtraction Approach

NASA Astrophysics Data System (ADS)

Mustak, S.

2013-09-01

The correction of atmospheric effects is very essential because visible bands of shorter wavelength are highly affected by atmospheric scattering especially of Rayleigh scattering. The objectives of the paper is to find out the haze values present in the all spectral bands and to correct the haze values for urban analysis. In this paper, Improved Dark Object Subtraction method of P. Chavez (1988) is applied for the correction of atmospheric haze in the Resoucesat-1 LISS-4 multispectral satellite image. Dark object Subtraction is a very simple image-based method of atmospheric haze which assumes that there are at least a few pixels within an image which should be black (% reflectance) and such black reflectance termed as dark object which are clear water body and shadows whose DN values zero (0) or Close to zero in the image. Simple Dark Object Subtraction method is a first order atmospheric correction but Improved Dark Object Subtraction method which tends to correct the Haze in terms of atmospheric scattering and path radiance based on the power law of relative scattering effect of atmosphere. The haze values extracted using Simple Dark Object Subtraction method for Green band (Band2), Red band (Band3) and NIR band (band4) are 40, 34 and 18 but the haze values extracted using Improved Dark Object Subtraction method are 40, 18.02 and 11.80 for aforesaid bands. Here it is concluded that the haze values extracted by Improved Dark Object Subtraction method provides more realistic results than Simple Dark Object Subtraction method.

Effects of a proposed quality improvement process in the proportion of the reported ultrasound findings unsupported by stored images.

PubMed

Schenone, Mauro; Ziebarth, Sarah; Duncan, Jose; Stokes, Lea; Hernandez, Angela

2018-02-05

To investigate the proportion of documented ultrasound findings that were unsupported by stored ultrasound images in the obstetric ultrasound unit, before and after the implementation of a quality improvement process consisting of a checklist and feedback. A quality improvement process was created involving utilization of a checklist and feedback from physician to sonographer. The feedback was based on findings of the physician's review of the report and images using a check list. To assess the impact of this process, two groups were compared. Group 1 consisted of 58 ultrasound reports created prior to initiation of the process. Group 2 included 65 ultrasound reports created after process implementation. Each chart was reviewed by a physician and a sonographer. Findings considered unsupported by stored images by both reviewers were used for analysis, and the proportion of unsupported findings was compared between the two groups. Results are expressed as mean ± standard error. A p value of < .05 was used to determine statistical significance. Univariate analysis of baseline characteristics and potential confounders showed no statistically significant difference between the groups. The mean proportion of unsupported findings in Group 1 was 5.1 ± 0.87, with Group 2 having a significantly lower proportion (2.6 ± 0.62) (p value = .018). Results suggest a significant decrease in the proportion of unsupported findings in ultrasound reports after quality improvement process implementation. Thus, we present a simple yet effective quality improvement process to reduce unsupported ultrasound findings.

Polished sample preparing and backscattered electron imaging and of fly ash-cement paste

NASA Astrophysics Data System (ADS)

Feng, Shuxia; Li, Yanqi

2018-03-01

In recent decades, the technology of backscattered electron imaging and image analysis was applied in more and more study of mixed cement paste because of its special advantages. Test accuracy of this technology is affected by polished sample preparation and image acquisition. In our work, effects of two factors in polished sample preparing and backscattered electron imaging were investigated. The results showed that increasing smoothing pressure could improve the flatness of polished surface and then help to eliminate interference of morphology on grey level distribution of backscattered electron images; increasing accelerating voltage was beneficial to increase gray difference among different phases in backscattered electron images.

Diffraction enhance x-ray imaging for quantitative phase contrast studies

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

Agrawal, A. K.; Singh, B., E-mail: balwants@rrcat.gov.in; Kashyap, Y. S.

2016-05-23

Conventional X-ray imaging based on absorption contrast permits limited visibility of feature having small density and thickness variations. For imaging of weakly absorbing material or materials possessing similar densities, a novel phase contrast imaging techniques called diffraction enhanced imaging has been designed and developed at imaging beamline Indus-2 RRCAT Indore. The technique provides improved visibility of the interfaces and show high contrast in the image forsmall density or thickness gradients in the bulk. This paper presents basic principle, instrumentation and analysis methods for this technique. Initial results of quantitative phase retrieval carried out on various samples have also been presented.

Backscattering analysis of high frequency ultrasonic imaging for ultrasound-guided breast biopsy

NASA Astrophysics Data System (ADS)

Cummins, Thomas; Akiyama, Takahiro; Lee, Changyang; Martin, Sue E.; Shung, K. Kirk

2017-03-01

A new ultrasound-guided breast biopsy technique is proposed. The technique utilizes conventional ultrasound guidance coupled with a high frequency embedded ultrasound array located within the biopsy needle to improve the accuracy in breast cancer diagnosis.1 The array within the needle is intended to be used to detect micro- calcifications indicative of early breast cancers such as ductal carcinoma in situ (DCIS). Backscattering analysis has the potential to characterize tissues to improve localization of lesions. This paper describes initial results of the application of backscattering analysis of breast biopsy tissue specimens and shows the usefulness of high frequency ultrasound for the new biopsy related technique. Ultrasound echoes of ex-vivo breast biopsy tissue specimens were acquired by using a single-element transducer with a bandwidth from 41 MHz to 88 MHz utilizing a UBM methodology, and the backscattering coefficients were calculated. These values as well as B-mode image data were mapped in 2D and matched with each pathology image for the identification of tissue type for the comparison to the pathology images corresponding to each plane. Microcalcifications were significantly distinguished from normal tissue. Adenocarcinoma was also successfully differentiated from adipose tissue. These results indicate that backscattering analysis is able to quantitatively distinguish tissues into normal and abnormal, which should help radiologists locate abnormal areas during the proposed ultrasound-guided breast biopsy with high frequency ultrasound.

Predictive validity of granulation tissue color measured by digital image analysis for deep pressure ulcer healing: a multicenter prospective cohort study.

PubMed

Iizaka, Shinji; Kaitani, Toshiko; Sugama, Junko; Nakagami, Gojiro; Naito, Ayumi; Koyanagi, Hiroe; Konya, Chizuko; Sanada, Hiromi

2013-01-01

This multicenter prospective cohort study examined the predictive validity of granulation tissue color evaluated by digital image analysis for deep pressure ulcer healing. Ninety-one patients with deep pressure ulcers were followed for 3 weeks. From a wound photograph taken at baseline, an image representing the granulation red index (GRI) was processed in which a redder color represented higher values. We calculated the average GRI over granulation tissue and the proportion of pixels exceeding the threshold intensity of 80 for the granulation tissue surface (%GRI80) and wound surface (%wound red index 80). In the receiver operating characteristics curve analysis, most GRI parameters had adequate discriminative values for both improvement of the DESIGN-R total score and wound closure. Ulcers were categorized by the obtained cutoff points of the average GRI (≤80, >80), %GRI80 (≤55, >55-80, >80%), and %wound red index 80 (≤25, >25-50, >50%). In the linear mixed model, higher classes for all GRI parameters showed significantly greater relative improvement in overall wound severity during the 3 weeks after adjustment for patient characteristics and wound locations. Assessment of granulation tissue color by digital image analysis will be useful as an objective monitoring tool for granulation tissue quality or surrogate outcomes of pressure ulcer healing. © 2012 by the Wound Healing Society.

Image segmentation for uranium isotopic analysis by SIMS: Combined adaptive thresholding and marker controlled watershed approach

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

Willingham, David G.; Naes, Benjamin E.; Heasler, Patrick G.

A novel approach to particle identification and particle isotope ratio determination has been developed for nuclear safeguard applications. This particle search approach combines an adaptive thresholding algorithm and marker-controlled watershed segmentation (MCWS) transform, which improves the secondary ion mass spectrometry (SIMS) isotopic analysis of uranium containing particle populations for nuclear safeguards applications. The Niblack assisted MCWS approach (a.k.a. SEEKER) developed for this work has improved the identification of isotopically unique uranium particles under conditions that have historically presented significant challenges for SIMS image data processing techniques. Particles obtained from five NIST uranium certified reference materials (CRM U129A, U015, U150, U500more » and U850) were successfully identified in regions of SIMS image data 1) where a high variability in image intensity existed, 2) where particles were touching or were in close proximity to one another and/or 3) where the magnitude of ion signal for a given region was count limited. Analysis of the isotopic distributions of uranium containing particles identified by SEEKER showed four distinct, accurately identified 235U enrichment distributions, corresponding to the NIST certified 235U/238U isotope ratios for CRM U129A/U015 (not statistically differentiated), U150, U500 and U850. Additionally, comparison of the minor uranium isotope (234U, 235U and 236U) atom percent values verified that, even in the absence of high precision isotope ratio measurements, SEEKER could be used to segment isotopically unique uranium particles from SIMS image data. Although demonstrated specifically for SIMS analysis of uranium containing particles for nuclear safeguards, SEEKER has application in addressing a broad set of image processing challenges.« less

Body image change and improved eating self-regulation in a weight management intervention in women

PubMed Central

2011-01-01

Background Successful weight management involves the regulation of eating behavior. However, the specific mechanisms underlying its successful regulation remain unclear. This study examined one potential mechanism by testing a model in which improved body image mediated the effects of obesity treatment on eating self-regulation. Further, this study explored the role of different body image components. Methods Participants were 239 overweight women (age: 37.6 ± 7.1 yr; BMI: 31.5 ± 4.1 kg/m2) engaged in a 12-month behavioral weight management program, which included a body image module. Self-reported measures were used to assess evaluative and investment body image, and eating behavior. Measurements occurred at baseline and at 12 months. Baseline-residualized scores were calculated to report change in the dependent variables. The model was tested using partial least squares analysis. Results The model explained 18-44% of the variance in the dependent variables. Treatment significantly improved both body image components, particularly by decreasing its investment component (f2 = .32 vs. f2 = .22). Eating behavior was positively predicted by investment body image change (p < .001) and to a lesser extent by evaluative body image (p < .05). Treatment had significant effects on 12-month eating behavior change, which were fully mediated by investment and partially mediated by evaluative body image (effect ratios: .68 and .22, respectively). Conclusions Results suggest that improving body image, particularly by reducing its salience in one's personal life, might play a role in enhancing eating self-regulation during weight control. Accordingly, future weight loss interventions could benefit from proactively addressing body image-related issues as part of their protocols. PMID:21767360

Body image change and improved eating self-regulation in a weight management intervention in women.

PubMed

Carraça, Eliana V; Silva, Marlene N; Markland, David; Vieira, Paulo N; Minderico, Cláudia S; Sardinha, Luís B; Teixeira, Pedro J

2011-07-18

Successful weight management involves the regulation of eating behavior. However, the specific mechanisms underlying its successful regulation remain unclear. This study examined one potential mechanism by testing a model in which improved body image mediated the effects of obesity treatment on eating self-regulation. Further, this study explored the role of different body image components. Participants were 239 overweight women (age: 37.6 ± 7.1 yr; BMI: 31.5 ± 4.1 kg/m²) engaged in a 12-month behavioral weight management program, which included a body image module. Self-reported measures were used to assess evaluative and investment body image, and eating behavior. Measurements occurred at baseline and at 12 months. Baseline-residualized scores were calculated to report change in the dependent variables. The model was tested using partial least squares analysis. The model explained 18-44% of the variance in the dependent variables. Treatment significantly improved both body image components, particularly by decreasing its investment component (f² = .32 vs. f² = .22). Eating behavior was positively predicted by investment body image change (p < .001) and to a lesser extent by evaluative body image (p < .05). Treatment had significant effects on 12-month eating behavior change, which were fully mediated by investment and partially mediated by evaluative body image (effect ratios: .68 and .22, respectively). Results suggest that improving body image, particularly by reducing its salience in one's personal life, might play a role in enhancing eating self-regulation during weight control. Accordingly, future weight loss interventions could benefit from proactively addressing body image-related issues as part of their protocols.

Point Analysis in Java applied to histological images of the perforant pathway: a user's account.

PubMed

Scorcioni, Ruggero; Wright, Susan N; Patrick Card, J; Ascoli, Giorgio A; Barrionuevo, Germán

2008-01-01

The freeware Java tool Point Analysis in Java (PAJ), created to perform 3D point analysis, was tested in an independent laboratory setting. The input data consisted of images of the hippocampal perforant pathway from serial immunocytochemical localizations of the rat brain in multiple views at different resolutions. The low magnification set (x2 objective) comprised the entire perforant pathway, while the high magnification set (x100 objective) allowed the identification of individual fibers. A preliminary stereological study revealed a striking linear relationship between the fiber count at high magnification and the optical density at low magnification. PAJ enabled fast analysis for down-sampled data sets and a friendly interface with automated plot drawings. Noted strengths included the multi-platform support as well as the free availability of the source code, conducive to a broad user base and maximum flexibility for ad hoc requirements. PAJ has great potential to extend its usability by (a) improving its graphical user interface, (b) increasing its input size limit, (c) improving response time for large data sets, and (d) potentially being integrated with other Java graphical tools such as ImageJ.

Longitudinal brain white matter alterations in minimal hepatic encephalopathy before and after liver transplantation.

PubMed

Lin, Wei-Che; Chou, Kun-Hsien; Chen, Chao-Long; Chen, Hsiu-Ling; Lu, Cheng-Hsien; Li, Shau-Hsuan; Huang, Chu-Chung; Lin, Ching-Po; Cheng, Yu-Fan

2014-01-01

Cerebral edema is the common pathogenic mechanism for cognitive impairment in minimal hepatic encephalopathy. Whether complete reversibility of brain edema, cognitive deficits, and their associated imaging can be achieved after liver transplantation remains an open question. To characterize white matter integrity before and after liver transplantation in patients with minimal hepatic encephalopathy, multiple diffusivity indices acquired via diffusion tensor imaging was applied. Twenty-eight patients and thirty age- and sex-matched healthy volunteers were included. Multiple diffusivity indices were obtained from diffusion tensor images, including mean diffusivity, fractional anisotropy, axial diffusivity and radial diffusivity. The assessment was repeated 6-12 month after transplantation. Differences in white matter integrity between groups, as well as longitudinal changes, were evaluated using tract-based spatial statistical analysis. Correlation analyses were performed to identify first scan before transplantation and interval changes among the neuropsychiatric tests, clinical laboratory tests, and diffusion tensor imaging indices. After transplantation, decreased water diffusivity without fractional anisotropy change indicating reversible cerebral edema was found in the left anterior cingulate, claustrum, postcentral gyrus, and right corpus callosum. However, a progressive decrease in fractional anisotropy and an increase in radial diffusivity suggesting demyelination were noted in temporal lobe. Improved pre-transplantation albumin levels and interval changes were associated with better recoveries of diffusion tensor imaging indices. Improvements in interval diffusion tensor imaging indices in the right postcentral gyrus were correlated with visuospatial function score correction. In conclusion, longitudinal voxel-wise analysis of multiple diffusion tensor imaging indices demonstrated different white matter changes in minimal hepatic encephalopathy patients. Transplantation improved extracellular cerebral edema and the results of associated cognition tests. However, white matter demyelination may advance in temporal lobe.

Facilitated Diagnosis of Pneumothoraces in Newborn Mice Using X-ray Dark-Field Radiography.

PubMed

Hellbach, Katharina; Yaroshenko, Andre; Willer, Konstantin; Pritzke, Tina; Baumann, Alena; Hesse, Nina; Auweter, Sigrid; Reiser, Maximilian F; Eickelberg, Oliver; Pfeiffer, Franz; Hilgendorff, Anne; Meinel, Felix G

2016-10-01

The aim of this study was to evaluate the diagnostic value of x-ray dark-field imaging in projection radiography-based depiction of pneumothoraces in the neonatal murine lung, a potentially life-threatening medical condition that requires a timely and correct diagnosis. By the use of a unique preclinical model, 7-day-old C57Bl/6N mice received mechanical ventilation for 2 or 8 hours with oxygen-rich gas (FIO2 = 0.4; n = 24). Unventilated mice either spontaneously breathed oxygen-rich gas (FIO2 = 0.4) for 2 or 8 hours or room air (n = 22). At the end of the experiment, lungs were inflated with a standardized volume of air after a lethal dose of pentobarbital was administered to the pups. All lungs were imaged with a prototype grating-based small-animal scanner to acquire x-ray transmission and dark-field radiographs. Image contrast between the air-filled pleural space and lung tissue was quantified for both transmission and dark-field radiograms. After the independent expert's assessment, 2 blinded readers evaluated all dark-field and transmission images for the presence or absence of pneumothoraces. Contrast ratios, diagnostic accuracy, as well as reader's confidence and interreader agreement were recorded for both imaging modalities. Evaluation of both x-ray transmission and dark-field radiographs by independent experts revealed the development of a total of 10 pneumothoraces in 8 mice. Here, the contrast ratio between the air-filled pleural space of the pneumothoraces and the lung tissue was significantly higher in the dark field (8.4 ± 3.5) when compared with the transmission images (5.1 ± 2.8; P < 0.05). Accordingly, the readers' diagnostic confidence for the diagnosis of pneumothoraces was significantly higher for dark-field compared with transmission images (P = 0.001). Interreader agreement improved from moderate for the analysis of transmission images alone (κ = 0.41) to very good when analyzing dark-field images alone (κ = 0.90) or in combination with transmission images (κ = 0.88). Diagnostic accuracy significantly improved for the analysis of dark-field images alone (P = 0.04) or in combination with transmission images (P = 0.02), compared with the analysis of transmission radiographs only. The significant improvement in contrast ratios between lung parenchyma and free air in the dark-field images allows the facilitated detection of pneumothoraces in the newborn mouse. These preclinical experiments indicate the potential of the technique for future clinical applications.

Image analysis and machine learning in digital pathology: Challenges and opportunities.

PubMed

Madabhushi, Anant; Lee, George

2016-10-01

With the rise in whole slide scanner technology, large numbers of tissue slides are being scanned and represented and archived digitally. While digital pathology has substantial implications for telepathology, second opinions, and education there are also huge research opportunities in image computing with this new source of "big data". It is well known that there is fundamental prognostic data embedded in pathology images. The ability to mine "sub-visual" image features from digital pathology slide images, features that may not be visually discernible by a pathologist, offers the opportunity for better quantitative modeling of disease appearance and hence possibly improved prediction of disease aggressiveness and patient outcome. However the compelling opportunities in precision medicine offered by big digital pathology data come with their own set of computational challenges. Image analysis and computer assisted detection and diagnosis tools previously developed in the context of radiographic images are woefully inadequate to deal with the data density in high resolution digitized whole slide images. Additionally there has been recent substantial interest in combining and fusing radiologic imaging and proteomics and genomics based measurements with features extracted from digital pathology images for better prognostic prediction of disease aggressiveness and patient outcome. Again there is a paucity of powerful tools for combining disease specific features that manifest across multiple different length scales. The purpose of this review is to discuss developments in computational image analysis tools for predictive modeling of digital pathology images from a detection, segmentation, feature extraction, and tissue classification perspective. We discuss the emergence of new handcrafted feature approaches for improved predictive modeling of tissue appearance and also review the emergence of deep learning schemes for both object detection and tissue classification. We also briefly review some of the state of the art in fusion of radiology and pathology images and also combining digital pathology derived image measurements with molecular "omics" features for better predictive modeling. The review ends with a brief discussion of some of the technical and computational challenges to be overcome and reflects on future opportunities for the quantitation of histopathology. Copyright © 2016 Elsevier B.V. All rights reserved.

Improving Landslide Susceptibility Modeling Using an Empirical Threshold Scheme for Excluding Landslide Deposition

NASA Astrophysics Data System (ADS)

Tsai, F.; Lai, J. S.; Chiang, S. H.

2015-12-01

Landslides are frequently triggered by typhoons and earthquakes in Taiwan, causing serious economic losses and human casualties. Remotely sensed images and geo-spatial data consisting of land-cover and environmental information have been widely used for producing landslide inventories and causative factors for slope stability analysis. Landslide susceptibility, on the other hand, can represent the spatial likelihood of landslide occurrence and is an important basis for landslide risk assessment. As multi-temporal satellite images become popular and affordable, they are commonly used to generate landslide inventories for subsequent analysis. However, it is usually difficult to distinguish different landslide sub-regions (scarp, debris flow, deposition etc.) directly from remote sensing imagery. Consequently, the extracted landslide extents using image-based visual interpretation and automatic detections may contain many depositions that may reduce the fidelity of the landslide susceptibility model. This study developed an empirical thresholding scheme based on terrain characteristics for eliminating depositions from detected landslide areas to improve landslide susceptibility modeling. In this study, Bayesian network classifier is utilized to build a landslide susceptibility model and to predict sequent rainfall-induced shallow landslides in the Shimen reservoir watershed located in northern Taiwan. Eleven causative factors are considered, including terrain slope, aspect, curvature, elevation, geology, land-use, NDVI, soil, distance to fault, river and road. Landslide areas detected using satellite images acquired before and after eight typhoons between 2004 to 2008 are collected as the main inventory for training and verification. In the analysis, previous landslide events are used as training data to predict the samples of the next event. The results are then compared with recorded landslide areas in the inventory to evaluate the accuracy. Experimental results demonstrate that the accuracies of landslide susceptibility analysis in all sequential predictions have been improved significantly after eliminating landslide depositions.

Contrast enhancement of bite mark images using the grayscale mixer in ACR in Photoshop®.

PubMed

Evans, Sam; Noorbhai, Suzanne; Lawson, Zoe; Stacey-Jones, Seren; Carabott, Romina

2013-05-01

Enhanced images may improve bite mark edge definition, assisting forensic analysis. Current contrast enhancement involves color extraction, viewing layered images by channel. A novel technique, producing a single enhanced image using the grayscale mix panel within Adobe Camera Raw®, has been developed and assessed here, allowing adjustments of multiple color channels simultaneously. Stage 1 measured RGB values in 72 versions of a color chart image; eight sliders in Photoshop® were adjusted at 25% intervals, all corresponding colors affected. Stage 2 used a bite mark image, and found only red, orange, and yellow sliders had discernable effects. Stage 3 assessed modality preference between color, grayscale, and enhanced images; on average, the 22 survey participants chose the enhanced image as better defined for nine out of 10 bite marks. The study has shown potential benefits for this new technique. However, further research is needed before use in the analysis of bite marks. © 2013 American Academy of Forensic Sciences.

Light Microscopy at Maximal Precision

NASA Astrophysics Data System (ADS)

Bierbaum, Matthew; Leahy, Brian D.; Alemi, Alexander A.; Cohen, Itai; Sethna, James P.

2017-10-01

Microscopy is the workhorse of the physical and life sciences, producing crisp images of everything from atoms to cells well beyond the capabilities of the human eye. However, the analysis of these images is frequently little more accurate than manual marking. Here, we revolutionize the analysis of microscopy images, extracting all the useful information theoretically contained in a complex microscope image. Using a generic, methodological approach, we extract the information by fitting experimental images with a detailed optical model of the microscope, a method we call parameter extraction from reconstructing images (PERI). As a proof of principle, we demonstrate this approach with a confocal image of colloidal spheres, improving measurements of particle positions and radii by 10-100 times over current methods and attaining the maximum possible accuracy. With this unprecedented accuracy, we measure nanometer-scale colloidal interactions in dense suspensions solely with light microscopy, a previously impossible feat. Our approach is generic and applicable to imaging methods from brightfield to electron microscopy, where we expect accuracies of 1 nm and 0.1 pm, respectively.

Radar image processing for rock-type discrimination

NASA Technical Reports Server (NTRS)

Blom, R. G.; Daily, M.

1982-01-01

Image processing and enhancement techniques for improving the geologic utility of digital satellite radar images are reviewed. Preprocessing techniques such as mean and variance correction on a range or azimuth line by line basis to provide uniformly illuminated swaths, median value filtering for four-look imagery to eliminate speckle, and geometric rectification using a priori elevation data. Examples are presented of application of preprocessing methods to Seasat and Landsat data, and Seasat SAR imagery was coregistered with Landsat imagery to form composite scenes. A polynomial was developed to distort the radar picture to fit the Landsat image of a 90 x 90 km sq grid, using Landsat color ratios with Seasat intensities. Subsequent linear discrimination analysis was employed to discriminate rock types from known areas. Seasat additions to the Landsat data improved rock identification by 7%.

Target recognition of ladar range images using even-order Zernike moments.

PubMed

Liu, Zheng-Jun; Li, Qi; Xia, Zhi-Wei; Wang, Qi

2012-11-01

Ladar range images have attracted considerable attention in automatic target recognition fields. In this paper, Zernike moments (ZMs) are applied to classify the target of the range image from an arbitrary azimuth angle. However, ZMs suffer from high computational costs. To improve the performance of target recognition based on small samples, even-order ZMs with serial-parallel backpropagation neural networks (BPNNs) are applied to recognize the target of the range image. It is found that the rotation invariance and classified performance of the even-order ZMs are both better than for odd-order moments and for moments compressed by principal component analysis. The experimental results demonstrate that combining the even-order ZMs with serial-parallel BPNNs can significantly improve the recognition rate for small samples.

Image encryption algorithm based on multiple mixed hash functions and cyclic shift

NASA Astrophysics Data System (ADS)

Wang, Xingyuan; Zhu, Xiaoqiang; Wu, Xiangjun; Zhang, Yingqian

2018-08-01

This paper proposes a new one-time pad scheme for chaotic image encryption that is based on the multiple mixed hash functions and the cyclic-shift function. The initial value is generated using both information of the plaintext image and the chaotic sequences, which are calculated from the SHA1 and MD5 hash algorithms. The scrambling sequences are generated by the nonlinear equations and logistic map. This paper aims to improve the deficiencies of traditional Baptista algorithms and its improved algorithms. We employ the cyclic-shift function and piece-wise linear chaotic maps (PWLCM), which give each shift number the characteristics of chaos, to diffuse the image. Experimental results and security analysis show that the new scheme has better security and can resist common attacks.

Near-infrared imaging for management of chronic maxillary sinusitis

NASA Astrophysics Data System (ADS)

You, Joon S.; Cerussi, Albert E.; Kim, James; Ison, Sean; Wong, Brian; Cui, Haotian; Bhandarkar, Naveen

2015-03-01

Efficient management of chronic sinusitis remains a great challenge for primary care physicians. Unlike ENT specialists using Computed Tomography scans, they lack an affordable and safe method to accurately screen and monitor sinus diseases in primary care settings. Lack of evidence-based sinusitis management leads to frequent under-treatments and unnecessary over-treatments (i.e. antibiotics). Previously, we reported low-cost optical imaging designs for oral illumination and facial optical imaging setup. It exploits the sensitivity of NIR transmission intensity and their unique patterns to the sinus structures and presence of fluid/mucous-buildup within the sinus cavities. Using the improved NIR system, we have obtained NIR sinus images of 45 subjects with varying degrees of sinusitis symptoms. We made diagnoses of these patients based on two types of evidence: symptoms alone or NIR images along. These diagnostic results were then compared to the gold standard diagnosis using computed tomography through sensitivity and specificity analysis. Our results indicate that diagnosis of mere presence of sinusitis that is, distinguishing between healthy individuals vs. diseased individuals did not improve much when using NIR imaging compared to the diagnosis based on symptoms alone (69% in sensitivity, 75% specificity). However, use of NIR imaging improved the differential diagnosis between mild and severe diseases significantly as the sensitivity improved from 75% for using diagnosis based on symptoms alone up to 95% for using diagnosis based on NIR images. Reported results demonstrate great promise for using NIR imaging system for management of chronic sinusitis patients in primary care settings without resorting to CT.

Deep Learning in Medical Image Analysis

PubMed Central

Shen, Dinggang; Wu, Guorong; Suk, Heung-Il

2016-01-01

The computer-assisted analysis for better interpreting images have been longstanding issues in the medical imaging field. On the image-understanding front, recent advances in machine learning, especially, in the way of deep learning, have made a big leap to help identify, classify, and quantify patterns in medical images. Specifically, exploiting hierarchical feature representations learned solely from data, instead of handcrafted features mostly designed based on domain-specific knowledge, lies at the core of the advances. In that way, deep learning is rapidly proving to be the state-of-the-art foundation, achieving enhanced performances in various medical applications. In this article, we introduce the fundamentals of deep learning methods; review their successes to image registration, anatomical/cell structures detection, tissue segmentation, computer-aided disease diagnosis or prognosis, and so on. We conclude by raising research issues and suggesting future directions for further improvements. PMID:28301734

Change Detection of High-Resolution Remote Sensing Images Based on Adaptive Fusion of Multiple Features

NASA Astrophysics Data System (ADS)

Wang, G. H.; Wang, H. B.; Fan, W. F.; Liu, Y.; Chen, C.

2018-04-01

In view of the traditional change detection algorithm mainly depends on the spectral information image spot, failed to effectively mining and fusion of multi-image feature detection advantage, the article borrows the ideas of object oriented analysis proposed a multi feature fusion of remote sensing image change detection algorithm. First by the multi-scale segmentation of image objects based; then calculate the various objects of color histogram and linear gradient histogram; utilizes the color distance and edge line feature distance between EMD statistical operator in different periods of the object, using the adaptive weighted method, the color feature distance and edge in a straight line distance of combination is constructed object heterogeneity. Finally, the curvature histogram analysis image spot change detection results. The experimental results show that the method can fully fuse the color and edge line features, thus improving the accuracy of the change detection.

Identification and characterisation of midbrain nuclei using optimised functional magnetic resonance imaging

PubMed Central

Limbrick-Oldfield, Eve H.; Brooks, Jonathan C.W.; Wise, Richard J.S.; Padormo, Francesco; Hajnal, Jo V.; Beckmann, Christian F.; Ungless, Mark A.

2012-01-01

Localising activity in the human midbrain with conventional functional MRI (fMRI) is challenging because the midbrain nuclei are small and located in an area that is prone to physiological artefacts. Here we present a replicable and automated method to improve the detection and localisation of midbrain fMRI signals. We designed a visual fMRI task that was predicted would activate the superior colliculi (SC) bilaterally. A limited number of coronal slices were scanned, orientated along the long axis of the brainstem, whilst simultaneously recording cardiac and respiratory traces. A novel anatomical registration pathway was used to optimise the localisation of the small midbrain nuclei in stereotactic space. Two additional structural scans were used to improve registration between functional and structural T1-weighted images: an echo-planar image (EPI) that matched the functional data but had whole-brain coverage, and a whole-brain T2-weighted image. This pathway was compared to conventional registration pathways, and was shown to significantly improve midbrain registration. To reduce the physiological artefacts in the functional data, we estimated and removed structured noise using a modified version of a previously described physiological noise model (PNM). Whereas a conventional analysis revealed only unilateral SC activity, the PNM analysis revealed the predicted bilateral activity. We demonstrate that these methods improve the measurement of a biologically plausible fMRI signal. Moreover they could be used to investigate the function of other midbrain nuclei. PMID:21867762

Possibility for new PolyCO imaging: stroboscopic imaging based on vibrating capillary optics

NASA Astrophysics Data System (ADS)

Liedl, A.; Dabagov, S. B.; Della Ventura, G.; Hampai, D.; Polese, C.

2015-08-01

Polycapillary lenses are well known optical devices for radiation and charged particles. These lenses consist of thousands channels through which the signal is transmitted by total external reflection phenomenon. Their application have made possible technical improvements in different fields such as imaging, fluorescence analysis, channeling studies etc. In particular, the application of this optics coupled with conventional sources such as X-ray tubes has opened a new season for potential applications of desktop instrumentations. For instance, the usage of such lenses has enhanced the spatial coherence and the brilliance over the sample allowing better resolution and contrast for imaging purposes. In addiction, improved focusing power and confocal configuration of other lenses has improved the resolution, from both the energy and the spatial points of view, in fluorescence mapping. A recent work has addressed the behavior of the transmitted radiation through a single capillary in vibrating regime. In this work a test of using a vibrating capillary for stroboscopic imaging is presented. A sample characterized by a known periodic event is studied with a synchronized vibrating capillary.

Improving MAVEN-IUVS Lyman-Alpha Apoapsis Images

NASA Astrophysics Data System (ADS)

Chaffin, M.; AlMannaei, A. S.; Jain, S.; Chaufray, J. Y.; Deighan, J.; Schneider, N. M.; Thiemann, E.; Mayyasi, M.; Clarke, J. T.; Crismani, M. M. J.; Stiepen, A.; Montmessin, F.; Epavier, F.; McClintock, B.; Stewart, I. F.; Holsclaw, G.; Jakosky, B. M.

2017-12-01

In 2013, the Mars Atmosphere and Volatile EvolutioN (MAVEN) mission was launched to study the Martian upper atmosphere and ionosphere. MAVEN orbits through a very thin cloud of hydrogen gas, known as the hydrogen corona, that has been used to explore the planet's geologic evolution by detecting the loss of hydrogen from the atmosphere. Here we present various methods of extracting properties of the hydrogen corona from observations using MAVEN's Imaging Ultraviolet Spectograph (IUVS) instrument. The analysis presented here uses the IUVS Far Ultraviolet mode apoapase data. From apoapse, IUVS is able to obtain images of the hydrogen corona by detecting the Lyman-alpha airglow using a combination of instrument scan mirror and spacecraft motion. To complete one apoapse observation, eight scan swaths are performed to collect the observations and construct a coronal image. However, these images require further processing to account for the atmospheric MUV background that hinders the quality of the data. Here, we present new techniques for correcting instrument data. For the background subtraction, a multi-linear regression (MLR) routine of the first order MUV radiance was used to improve the images. A flat field correction was also applied by fitting a polynomial to periapse radiance observations. The apoapse data was re-binned using this fit.The results are presented as images to demonstrate the improvements in the data reduction. Implementing these methods for more orbits will improve our understanding of seasonal variability and H loss. Asymmetries in the Martian hydrogen corona can also be assessed to improve current model estimates of coronal H in the Martian atmosphere.

Myocardial perfusion imaging with PET

PubMed Central

Nakazato, Ryo; Berman, Daniel S; Alexanderson, Erick; Slomka, Piotr

2013-01-01

PET-myocardial perfusion imaging (MPI) allows accurate measurement of myocardial perfusion, absolute myocardial blood flow and function at stress and rest in a single study session performed in approximately 30 min. Various PET tracers are available for MPI, and rubidium-82 or nitrogen-13-ammonia is most commonly used. In addition, a new fluorine-18-based PET-MPI tracer is currently being evaluated. Relative quantification of PET perfusion images shows very high diagnostic accuracy for detection of obstructive coronary artery disease. Dynamic myocardial blood flow analysis has demonstrated additional prognostic value beyond relative perfusion imaging. Patient radiation dose can be reduced and image quality can be improved with latest advances in PET/CT equipment. Simultaneous assessment of both anatomy and perfusion by hybrid PET/CT can result in improved diagnostic accuracy. Compared with SPECT-MPI, PET-MPI provides higher diagnostic accuracy, using lower radiation doses during a shorter examination time period for the detection of coronary artery disease. PMID:23671459

[Techniques for pixel response nonuniformity correction of CCD in interferential imaging spectrometer].

PubMed

Yao, Tao; Yin, Shi-Min; Xiangli, Bin; Lü, Qun-Bo

2010-06-01

Based on in-depth analysis of the relative radiation scaling theorem and acquired scaling data of pixel response nonuniformity correction of CCD (charge-coupled device) in spaceborne visible interferential imaging spectrometer, a pixel response nonuniformity correction method of CCD adapted to visible and infrared interferential imaging spectrometer system was studied out, and it availably resolved the engineering technical problem of nonuniformity correction in detector arrays for interferential imaging spectrometer system. The quantitative impact of CCD nonuniformity on interferogram correction and recovery spectrum accuracy was given simultaneously. Furthermore, an improved method with calibration and nonuniformity correction done after the instrument is successfully assembled was proposed. The method can save time and manpower. It can correct nonuniformity caused by other reasons in spectrometer system besides CCD itself's nonuniformity, can acquire recalibration data when working environment is changed, and can also more effectively improve the nonuniformity calibration accuracy of interferential imaging

A novel underwater dam crack detection and classification approach based on sonar images

PubMed Central

Shi, Pengfei; Fan, Xinnan; Ni, Jianjun; Khan, Zubair; Li, Min

2017-01-01

Underwater dam crack detection and classification based on sonar images is a challenging task because underwater environments are complex and because cracks are quite random and diverse in nature. Furthermore, obtainable sonar images are of low resolution. To address these problems, a novel underwater dam crack detection and classification approach based on sonar imagery is proposed. First, the sonar images are divided into image blocks. Second, a clustering analysis of a 3-D feature space is used to obtain the crack fragments. Third, the crack fragments are connected using an improved tensor voting method. Fourth, a minimum spanning tree is used to obtain the crack curve. Finally, an improved evidence theory combined with fuzzy rule reasoning is proposed to classify the cracks. Experimental results show that the proposed approach is able to detect underwater dam cracks and classify them accurately and effectively under complex underwater environments. PMID:28640925

A novel underwater dam crack detection and classification approach based on sonar images.

PubMed

Shi, Pengfei; Fan, Xinnan; Ni, Jianjun; Khan, Zubair; Li, Min

2017-01-01

Underwater dam crack detection and classification based on sonar images is a challenging task because underwater environments are complex and because cracks are quite random and diverse in nature. Furthermore, obtainable sonar images are of low resolution. To address these problems, a novel underwater dam crack detection and classification approach based on sonar imagery is proposed. First, the sonar images are divided into image blocks. Second, a clustering analysis of a 3-D feature space is used to obtain the crack fragments. Third, the crack fragments are connected using an improved tensor voting method. Fourth, a minimum spanning tree is used to obtain the crack curve. Finally, an improved evidence theory combined with fuzzy rule reasoning is proposed to classify the cracks. Experimental results show that the proposed approach is able to detect underwater dam cracks and classify them accurately and effectively under complex underwater environments.

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.

Direct magnetic field estimation based on echo planar raw data.

PubMed

Testud, Frederik; Splitthoff, Daniel Nicolas; Speck, Oliver; Hennig, Jürgen; Zaitsev, Maxim

2010-07-01

Gradient recalled echo echo planar imaging is widely used in functional magnetic resonance imaging. The fast data acquisition is, however, very sensitive to field inhomogeneities which manifest themselves as artifacts in the images. Typically used correction methods have the common deficit that the data for the correction are acquired only once at the beginning of the experiment, assuming the field inhomogeneity distribution B(0) does not change over the course of the experiment. In this paper, methods to extract the magnetic field distribution from the acquired k-space data or from the reconstructed phase image of a gradient echo planar sequence are compared and extended. A common derivation for the presented approaches provides a solid theoretical basis, enables a fair comparison and demonstrates the equivalence of the k-space and the image phase based approaches. The image phase analysis is extended here to calculate the local gradient in the readout direction and improvements are introduced to the echo shift analysis, referred to here as "k-space filtering analysis." The described methods are compared to experimentally acquired B(0) maps in phantoms and in vivo. The k-space filtering analysis presented in this work demonstrated to be the most sensitive method to detect field inhomogeneities.

Improved recognition of figures containing fluorescence microscope images in online journal articles using graphical models.

PubMed

Qian, Yuntao; Murphy, Robert F

2008-02-15

There is extensive interest in automating the collection, organization and analysis of biological data. Data in the form of images in online literature present special challenges for such efforts. The first steps in understanding the contents of a figure are decomposing it into panels and determining the type of each panel. In biological literature, panel types include many kinds of images collected by different techniques, such as photographs of gels or images from microscopes. We have previously described the SLIF system (http://slif.cbi.cmu.edu) that identifies panels containing fluorescence microscope images among figures in online journal articles as a prelude to further analysis of the subcellular patterns in such images. This system contains a pretrained classifier that uses image features to assign a type (class) to each separate panel. However, the types of panels in a figure are often correlated, so that we can consider the class of a panel to be dependent not only on its own features but also on the types of the other panels in a figure. In this article, we introduce the use of a type of probabilistic graphical model, a factor graph, to represent the structured information about the images in a figure, and permit more robust and accurate inference about their types. We obtain significant improvement over results for considering panels separately. The code and data used for the experiments described here are available from http://murphylab.web.cmu.edu/software.

An Analysis of Fundamental Waffle Mode in Early AEOS Adaptive Optics Images

NASA Astrophysics Data System (ADS)

Makidon, Russell B.; Sivaramakrishnan, Anand; Perrin, Marshall D.; Roberts, Lewis C., Jr.; Oppenheimer, Ben R.; Soummer, Rémi; Graham, James R.

2005-08-01

Adaptive optics (AO) systems have significantly improved astronomical imaging capabilities over the last decade and are revolutionizing the kinds of science possible with 4-5 m class ground-based telescopes. A thorough understanding of AO system performance at the telescope can enable new frontiers of science as observations push AO systems to their performance limits. We look at recent advances with wave-front reconstruction (WFR) on the Advanced Electro-Optical System (AEOS) 3.6 m telescope to show how progress made in improving WFR can be measured directly in improved science images. We describe how a ``waffle mode'' wave-front error (which is not sensed by a Fried geometry Shack-Hartmann wave-front sensor) affects the AO point-spread function. We model details of AEOS AO to simulate a PSF that matches the actual AO PSF in the I band and show that while the older observed AEOS PSF contained several times more waffle error than expected, improved WFR techniques noticeably improve AEOS AO performance. We estimate the impact of these improved WFRs on H-band imaging at AEOS, chosen based on the optimization of the Lyot Project near-infrared coronagraph at this bandpass. Based on observations made at the Maui Space Surveillance System, operated by Detachment 15 of the US Air Force Research Laboratory's Directed Energy Directorate.

Fourier Ptychographic Microscopy for Rapid, High-Resolution Imaging of Circulating Tumor Cells Enriched by Microfiltration.

PubMed

Williams, Anthony; Chung, Jaebum; Yang, Changhuei; Cote, Richard J

2017-01-01

Examining the hematogenous compartment for evidence of metastasis has increased significantly within the oncology research community in recent years, due to the development of technologies aimed at the enrichment of circulating tumor cells (CTCs), the subpopulation of primary tumor cells that gain access to the circulatory system and are responsible for colonization at distant sites. In contrast to other technologies, filtration-based CTC enrichment, which exploits differences in size between larger tumor cells and surrounding smaller, non-tumor blood cells, has the potential to improve CTC characterization through isolation of tumor cell populations with greater molecular heterogeneity. However, microscopic analysis of uneven filtration surfaces containing CTCs is laborious, time-consuming, and inconsistent, preventing widespread use of filtration-based enrichment technologies. Here, integrated with a microfiltration-based CTC and rare cell enrichment device we have previously described, we present a protocol for Fourier Ptychographic Microscopy (FPM), a method that, unlike many automated imaging platforms, produces high-speed, high-resolution images that can be digitally refocused, allowing users to observe objects of interest present on multiple focal planes within the same image frame. The development of a cost-effective and high-throughput CTC analysis system for filtration-based enrichment technologies could have profound clinical implications for improved CTC detection and analysis.

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.

Quantitative evaluation of in vivo vital-dye fluorescence endoscopic imaging for the detection of Barrett’s-associated neoplasia

PubMed Central

Thekkek, Nadhi; Lee, Michelle H.; Polydorides, Alexandros D.; Rosen, Daniel G.; Anandasabapathy, Sharmila; Richards-Kortum, Rebecca

2015-01-01

Abstract. Current imaging tools are associated with inconsistent sensitivity and specificity for detection of Barrett’s-associated neoplasia. Optical imaging has shown promise in improving the classification of neoplasia in vivo. The goal of this pilot study was to evaluate whether in vivo vital dye fluorescence imaging (VFI) has the potential to improve the accuracy of early-detection of Barrett’s-associated neoplasia. In vivo endoscopic VFI images were collected from 65 sites in 14 patients with confirmed Barrett’s esophagus (BE), dysplasia, or esophageal adenocarcinoma using a modular video endoscope and a high-resolution microendoscope (HRME). Qualitative image features were compared to histology; VFI and HRME images show changes in glandular structure associated with neoplastic progression. Quantitative image features in VFI images were identified for objective image classification of metaplasia and neoplasia, and a diagnostic algorithm was developed using leave-one-out cross validation. Three image features extracted from VFI images were used to classify tissue as neoplastic or not with a sensitivity of 87.8% and a specificity of 77.6% (AUC=0.878). A multimodal approach incorporating VFI and HRME imaging can delineate epithelial changes present in Barrett’s-associated neoplasia. Quantitative analysis of VFI images may provide a means for objective interpretation of BE during surveillance. PMID:25950645

Quantitative evaluation of in vivo vital-dye fluorescence endoscopic imaging for the detection of Barrett's-associated neoplasia.

PubMed

Thekkek, Nadhi; Lee, Michelle H; Polydorides, Alexandros D; Rosen, Daniel G; Anandasabapathy, Sharmila; Richards-Kortum, Rebecca

2015-05-01

Current imaging tools are associated with inconsistent sensitivity and specificity for detection of Barrett's-associated neoplasia. Optical imaging has shown promise in improving the classification of neoplasia in vivo. The goal of this pilot study was to evaluate whether in vivo vital dye fluorescence imaging (VFI) has the potential to improve the accuracy of early-detection of Barrett's-associated neoplasia. In vivo endoscopic VFI images were collected from 65 sites in 14 patients with confirmed Barrett's esophagus (BE), dysplasia, oresophageal adenocarcinoma using a modular video endoscope and a high-resolution microendoscope(HRME). Qualitative image features were compared to histology; VFI and HRME images show changes in glandular structure associated with neoplastic progression. Quantitative image features in VFI images were identified for objective image classification of metaplasia and neoplasia, and a diagnostic algorithm was developed using leave-one-out cross validation. Three image features extracted from VFI images were used to classify tissue as neoplastic or not with a sensitivity of 87.8% and a specificity of 77.6% (AUC = 0.878). A multimodal approach incorporating VFI and HRME imaging can delineate epithelial changes present in Barrett's-associated neoplasia. Quantitative analysis of VFI images may provide a means for objective interpretation of BE during surveillance.

Fractal and Gray Level Cooccurrence Matrix Computational Analysis of Primary Osteosarcoma Magnetic Resonance Images Predicts the Chemotherapy Response.

PubMed

Djuričić, Goran J; Radulovic, Marko; Sopta, Jelena P; Nikitović, Marina; Milošević, Nebojša T

2017-01-01

The prediction of induction chemotherapy response at the time of diagnosis may improve outcomes in osteosarcoma by allowing for personalized tailoring of therapy. The aim of this study was thus to investigate the predictive potential of the so far unexploited computational analysis of osteosarcoma magnetic resonance (MR) images. Fractal and gray level cooccurrence matrix (GLCM) algorithms were employed in retrospective analysis of MR images of primary osteosarcoma localized in distal femur prior to the OsteoSa induction chemotherapy. The predicted and actual chemotherapy response outcomes were then compared by means of receiver operating characteristic (ROC) analysis and accuracy calculation. Dbin, Λ, and SCN were the standard fractal and GLCM features which significantly associated with the chemotherapy outcome, but only in one of the analyzed planes. Our newly developed normalized fractal dimension, called the space-filling ratio (SFR) exerted an independent and much better predictive value with the prediction significance accomplished in two of the three imaging planes, with accuracy of 82% and area under the ROC curve of 0.20 (95% confidence interval 0-0.41). In conclusion, SFR as the newly designed fractal coefficient provided superior predictive performance in comparison to standard image analysis features, presumably by compensating for the tumor size variation in MR images.

Astronomical data analysis software and systems I; Proceedings of the 1st Annual Conference, Tucson, AZ, Nov. 6-8, 1991

NASA Technical Reports Server (NTRS)

Worrall, Diana M. (Editor); Biemesderfer, Chris (Editor); Barnes, Jeannette (Editor)

1992-01-01

Consideration is given to a definition of a distribution format for X-ray data, the Einstein on-line system, the NASA/IPAC extragalactic database, COBE astronomical databases, Cosmic Background Explorer astronomical databases, the ADAM software environment, the Groningen Image Processing System, search for a common data model for astronomical data analysis systems, deconvolution for real and synthetic apertures, pitfalls in image reconstruction, a direct method for spectral and image restoration, and a discription of a Poisson imagery super resolution algorithm. Also discussed are multivariate statistics on HI and IRAS images, a faint object classification using neural networks, a matched filter for improving SNR of radio maps, automated aperture photometry of CCD images, interactive graphics interpreter, the ROSAT extreme ultra-violet sky survey, a quantitative study of optimal extraction, an automated analysis of spectra, applications of synthetic photometry, an algorithm for extra-solar planet system detection and data reduction facilities for the William Herschel telescope.

Dynamic physiological modeling for functional diffuse optical tomography

PubMed Central

Diamond, Solomon Gilbert; Huppert, Theodore J.; Kolehmainen, Ville; Franceschini, Maria Angela; Kaipio, Jari P.; Arridge, Simon R.; Boas, David A.

2009-01-01

Diffuse optical tomography (DOT) is a noninvasive imaging technology that is sensitive to local concentration changes in oxy- and deoxyhemoglobin. When applied to functional neuroimaging, DOT measures hemodynamics in the scalp and brain that reflect competing metabolic demands and cardiovascular dynamics. The diffuse nature of near-infrared photon migration in tissue and the multitude of physiological systems that affect hemodynamics motivate the use of anatomical and physiological models to improve estimates of the functional hemodynamic response. In this paper, we present a linear state-space model for DOT analysis that models the physiological fluctuations present in the data with either static or dynamic estimation. We demonstrate the approach by using auxiliary measurements of blood pressure variability and heart rate variability as inputs to model the background physiology in DOT data. We evaluate the improvements accorded by modeling this physiology on ten human subjects with simulated functional hemodynamic responses added to the baseline physiology. Adding physiological modeling with a static estimator significantly improved estimates of the simulated functional response, and further significant improvements were achieved with a dynamic Kalman filter estimator (paired t tests, n = 10, P < 0.05). These results suggest that physiological modeling can improve DOT analysis. The further improvement with the Kalman filter encourages continued research into dynamic linear modeling of the physiology present in DOT. Cardiovascular dynamics also affect the blood-oxygen-dependent (BOLD) signal in functional magnetic resonance imaging (fMRI). This state-space approach to DOT analysis could be extended to BOLD fMRI analysis, multimodal studies and real-time analysis. PMID:16242967

Large-scale retrieval for medical image analytics: A comprehensive review.

PubMed

Li, Zhongyu; Zhang, Xiaofan; Müller, Henning; Zhang, Shaoting

2018-01-01

Over the past decades, medical image analytics was greatly facilitated by the explosion of digital imaging techniques, where huge amounts of medical images were produced with ever-increasing quality and diversity. However, conventional methods for analyzing medical images have achieved limited success, as they are not capable to tackle the huge amount of image data. In this paper, we review state-of-the-art approaches for large-scale medical image analysis, which are mainly based on recent advances in computer vision, machine learning and information retrieval. Specifically, we first present the general pipeline of large-scale retrieval, summarize the challenges/opportunities of medical image analytics on a large-scale. Then, we provide a comprehensive review of algorithms and techniques relevant to major processes in the pipeline, including feature representation, feature indexing, searching, etc. On the basis of existing work, we introduce the evaluation protocols and multiple applications of large-scale medical image retrieval, with a variety of exploratory and diagnostic scenarios. Finally, we discuss future directions of large-scale retrieval, which can further improve the performance of medical image analysis. Copyright © 2017 Elsevier B.V. All rights reserved.

IDEAL: Images Across Domains, Experiments, Algorithms and Learning

NASA Astrophysics Data System (ADS)

Ushizima, Daniela M.; Bale, Hrishikesh A.; Bethel, E. Wes; Ercius, Peter; Helms, Brett A.; Krishnan, Harinarayan; Grinberg, Lea T.; Haranczyk, Maciej; Macdowell, Alastair A.; Odziomek, Katarzyna; Parkinson, Dilworth Y.; Perciano, Talita; Ritchie, Robert O.; Yang, Chao

2016-11-01

Research across science domains is increasingly reliant on image-centric data. Software tools are in high demand to uncover relevant, but hidden, information in digital images, such as those coming from faster next generation high-throughput imaging platforms. The challenge is to analyze the data torrent generated by the advanced instruments efficiently, and provide insights such as measurements for decision-making. In this paper, we overview work performed by an interdisciplinary team of computational and materials scientists, aimed at designing software applications and coordinating research efforts connecting (1) emerging algorithms for dealing with large and complex datasets; (2) data analysis methods with emphasis in pattern recognition and machine learning; and (3) advances in evolving computer architectures. Engineering tools around these efforts accelerate the analyses of image-based recordings, improve reusability and reproducibility, scale scientific procedures by reducing time between experiments, increase efficiency, and open opportunities for more users of the imaging facilities. This paper describes our algorithms and software tools, showing results across image scales, demonstrating how our framework plays a role in improving image understanding for quality control of existent materials and discovery of new compounds.

Multispectral Image Compression for Improvement of Colorimetric and Spectral Reproducibility by Nonlinear Spectral Transform

NASA Astrophysics Data System (ADS)

Yu, Shanshan; Murakami, Yuri; Obi, Takashi; Yamaguchi, Masahiro; Ohyama, Nagaaki

2006-09-01

The article proposes a multispectral image compression scheme using nonlinear spectral transform for better colorimetric and spectral reproducibility. In the method, we show the reduction of colorimetric error under a defined viewing illuminant and also that spectral accuracy can be improved simultaneously using a nonlinear spectral transform called Labplus, which takes into account the nonlinearity of human color vision. Moreover, we show that the addition of diagonal matrices to Labplus can further preserve the spectral accuracy and has a generalized effect of improving the colorimetric accuracy under other viewing illuminants than the defined one. Finally, we discuss the usage of the first-order Markov model to form the analysis vectors for the higher order channels in Labplus to reduce the computational complexity. We implement a multispectral image compression system that integrates Labplus with JPEG2000 for high colorimetric and spectral reproducibility. Experimental results for a 16-band multispectral image show the effectiveness of the proposed scheme.

Single-particle cryo-EM-Improved ab initio 3D reconstruction with SIMPLE/PRIME.

PubMed

Reboul, Cyril F; Eager, Michael; Elmlund, Dominika; Elmlund, Hans

2018-01-01

Cryogenic electron microscopy (cryo-EM) and single-particle analysis now enables the determination of high-resolution structures of macromolecular assemblies that have resisted X-ray crystallography and other approaches. We developed the SIMPLE open-source image-processing suite for analysing cryo-EM images of single-particles. A core component of SIMPLE is the probabilistic PRIME algorithm for identifying clusters of images in 2D and determine relative orientations of single-particle projections in 3D. Here, we extend our previous work on PRIME and introduce new stochastic optimization algorithms that improve the robustness of the approach. Our refined method for identification of homogeneous subsets of images in accurate register substantially improves the resolution of the cluster centers and of the ab initio 3D reconstructions derived from them. We now obtain maps with a resolution better than 10 Å by exclusively processing cluster centers. Excellent parallel code performance on over-the-counter laptops and CPU workstations is demonstrated. © 2017 The Protein Society.

An improved algorithm of laser spot center detection in strong noise background

NASA Astrophysics Data System (ADS)

Zhang, Le; Wang, Qianqian; Cui, Xutai; Zhao, Yu; Peng, Zhong

2018-01-01

Laser spot center detection is demanded in many applications. The common algorithms for laser spot center detection such as centroid and Hough transform method have poor anti-interference ability and low detection accuracy in the condition of strong background noise. In this paper, firstly, the median filtering was used to remove the noise while preserving the edge details of the image. Secondly, the binarization of the laser facula image was carried out to extract target image from background. Then the morphological filtering was performed to eliminate the noise points inside and outside the spot. At last, the edge of pretreated facula image was extracted and the laser spot center was obtained by using the circle fitting method. In the foundation of the circle fitting algorithm, the improved algorithm added median filtering, morphological filtering and other processing methods. This method could effectively filter background noise through theoretical analysis and experimental verification, which enhanced the anti-interference ability of laser spot center detection and also improved the detection accuracy.

Integrated Modeling Activities for the James Webb Space Telescope: Optical Jitter Analysis

NASA Technical Reports Server (NTRS)

Hyde, T. Tupper; Ha, Kong Q.; Johnston, John D.; Howard, Joseph M.; Mosier, Gary E.

2004-01-01

This is a continuation of a series of papers on the integrated modeling activities for the James Webb Space Telescope(JWST). Starting with the linear optical model discussed in part one, and using the optical sensitivities developed in part two, we now assess the optical image motion and wavefront errors from the structural dynamics. This is often referred to as "jitter: analysis. The optical model is combined with the structural model and the control models to create a linear structural/optical/control model. The largest jitter is due to spacecraft reaction wheel assembly disturbances which are harmonic in nature and will excite spacecraft and telescope structural. The structural/optic response causes image quality degradation due to image motion (centroid error) as well as dynamic wavefront error. Jitter analysis results are used to predict imaging performance, improve the structural design, and evaluate the operational impact of the disturbance sources.

Preliminary spectral and geologic analysis of Landsat-4 Thematic Mapper data, Wind River Basin area, Wyoming

NASA Technical Reports Server (NTRS)

Conel, J. E.; Lang, H. R.; Paylor, E. D.; Alley, R. E.

1985-01-01

A Landsat-4 Thematic Mapper (TM) image of the Wind River Basin area in Wyoming is currently under analysis for stratigraphic and structural mapping and for assessment of spectral and spatial characteristics using visible, near infrared, and short wavelength infrared bands. To estimate the equivalent Lambertian surface reflectance, TM radiance data were calibrated to remove atmospheric and instrumental effects. Reflectance measurements for homogeneous natural and cultural targets were acquired about one year after data acquisition. Calibration data obtained during the analysis were used to calculate new gains and offsets to improve scanner response for earth science applications. It is shown that the principal component images calculated from the TM data were the result of linear transformations of ground reflectance. In images prepared from this transform, the separation of spectral classes was independent of systematic atmospheric and instrumental factors. Several examples of the processed images are provided.

Creating the “desired mindset”: Philip Morris’s efforts to improve its corporate image among women

PubMed Central

McDaniel, Patricia A.; Malone, Ruth E.

2009-01-01

Through analysis of tobacco company documents, we explored how and why Philip Morris sought to enhance its corporate image among American women. Philip Morris regarded women as an influential group. To improve its image among women, while keeping tobacco off their organizational agendas, the company sponsored women’s groups and programs. It also sought to appeal to women it defined as “active moms” by advertising its commitment to domestic violence victims. It was more successful in securing women’s organizations as allies than active moms. Increasing tobacco’s visibility as a global women’s health issue may require addressing industry influence. PMID:19851947

Supporting Imagers' VOICE: A National Training Program in Comparative Effectiveness Research and Big Data Analytics.

PubMed

Kang, Stella K; Rawson, James V; Recht, Michael P

2017-12-05

Provided methodologic training, more imagers can contribute to the evidence basis on improved health outcomes and value in diagnostic imaging. The Value of Imaging Through Comparative Effectiveness Research Program was developed to provide hands-on, practical training in five core areas for comparative effectiveness and big biomedical data research: decision analysis, cost-effectiveness analysis, evidence synthesis, big data principles, and applications of big data analytics. The program's mixed format consists of web-based modules for asynchronous learning as well as in-person sessions for practical skills and group discussion. Seven diagnostic radiology subspecialties and cardiology are represented in the first group of program participants, showing the collective potential for greater depth of comparative effectiveness research in the imaging community. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Visual grading characteristics and ordinal regression analysis during optimisation of CT head examinations.

PubMed

Zarb, Francis; McEntee, Mark F; Rainford, Louise

2015-06-01

To evaluate visual grading characteristics (VGC) and ordinal regression analysis during head CT optimisation as a potential alternative to visual grading assessment (VGA), traditionally employed to score anatomical visualisation. Patient images (n = 66) were obtained using current and optimised imaging protocols from two CT suites: a 16-slice scanner at the national Maltese centre for trauma and a 64-slice scanner in a private centre. Local resident radiologists (n = 6) performed VGA followed by VGC and ordinal regression analysis. VGC alone indicated that optimised protocols had similar image quality as current protocols. Ordinal logistic regression analysis provided an in-depth evaluation, criterion by criterion allowing the selective implementation of the protocols. The local radiology review panel supported the implementation of optimised protocols for brain CT examinations (including trauma) in one centre, achieving radiation dose reductions ranging from 24 % to 36 %. In the second centre a 29 % reduction in radiation dose was achieved for follow-up cases. The combined use of VGC and ordinal logistic regression analysis led to clinical decisions being taken on the implementation of the optimised protocols. This improved method of image quality analysis provided the evidence to support imaging protocol optimisation, resulting in significant radiation dose savings. • There is need for scientifically based image quality evaluation during CT optimisation. • VGC and ordinal regression analysis in combination led to better informed clinical decisions. • VGC and ordinal regression analysis led to dose reductions without compromising diagnostic efficacy.

Advanced IR System For Supersonic Boundary Layer Transition Flight Experiment

NASA Technical Reports Server (NTRS)

Banks, Daniel W.

2008-01-01

Infrared thermography is a preferred method investigating transition in flight: a) Global and non-intrusive; b) Can also be used to visualize and characterize other fluid mechanic phenomena such as shock impingement, separation etc. F-15 based system was updated with new camera and digital video recorder to support high Reynolds number transition tests. Digital Recording improves image quality and analysis capability and allows for accurate quantitative (temperature) measurements and greater enhancement through image processing allows analysis of smaller scale phenomena.

Detection and Characterization of Boundary-Layer Transition in Flight at Supersonic Conditions Using Infrared Thermography

NASA Technical Reports Server (NTRS)

Banks, Daniel W.

2008-01-01

Infrared thermography is a powerful tool for investigating fluid mechanics on flight vehicles. (Can be used to visualize and characterize transition, shock impingement, separation etc.). Updated onboard F-15 based system was used to visualize supersonic boundary layer transition test article. (Tollmien-Schlichting and cross-flow dominant flow fields). Digital Recording improves image quality and analysis capability. (Allows accurate quantitative (temperature) measurements, Greater enhancement through image processing allows analysis of smaller scale phenomena).

Plus Disease in Retinopathy of Prematurity: Improving Diagnosis by Ranking Disease Severity and Using Quantitative Image Analysis.

PubMed

Kalpathy-Cramer, Jayashree; Campbell, J Peter; Erdogmus, Deniz; Tian, Peng; Kedarisetti, Dharanish; Moleta, Chace; Reynolds, James D; Hutcheson, Kelly; Shapiro, Michael J; Repka, Michael X; Ferrone, Philip; Drenser, Kimberly; Horowitz, Jason; Sonmez, Kemal; Swan, Ryan; Ostmo, Susan; Jonas, Karyn E; Chan, R V Paul; Chiang, Michael F

2016-11-01

To determine expert agreement on relative retinopathy of prematurity (ROP) disease severity and whether computer-based image analysis can model relative disease severity, and to propose consideration of a more continuous severity score for ROP. We developed 2 databases of clinical images of varying disease severity (100 images and 34 images) as part of the Imaging and Informatics in ROP (i-ROP) cohort study and recruited expert physician, nonexpert physician, and nonphysician graders to classify and perform pairwise comparisons on both databases. Six participating expert ROP clinician-scientists, each with a minimum of 10 years of clinical ROP experience and 5 ROP publications, and 5 image graders (3 physicians and 2 nonphysician graders) who analyzed images that were obtained during routine ROP screening in neonatal intensive care units. Images in both databases were ranked by average disease classification (classification ranking), by pairwise comparison using the Elo rating method (comparison ranking), and by correlation with the i-ROP computer-based image analysis system. Interexpert agreement (weighted κ statistic) compared with the correlation coefficient (CC) between experts on pairwise comparisons and correlation between expert rankings and computer-based image analysis modeling. There was variable interexpert agreement on diagnostic classification of disease (plus, preplus, or normal) among the 6 experts (mean weighted κ, 0.27; range, 0.06-0.63), but good correlation between experts on comparison ranking of disease severity (mean CC, 0.84; range, 0.74-0.93) on the set of 34 images. Comparison ranking provided a severity ranking that was in good agreement with ranking obtained by classification ranking (CC, 0.92). Comparison ranking on the larger dataset by both expert and nonexpert graders demonstrated good correlation (mean CC, 0.97; range, 0.95-0.98). The i-ROP system was able to model this continuous severity with good correlation (CC, 0.86). Experts diagnose plus disease on a continuum, with poor absolute agreement on classification but good relative agreement on disease severity. These results suggest that the use of pairwise rankings and a continuous severity score, such as that provided by the i-ROP system, may improve agreement on disease severity in the future. Copyright © 2016 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

Influence of detector pixel size, TOF resolution and DOI on image quality in MR-compatible whole-body PET.

PubMed

Thoen, Hendrik; Keereman, Vincent; Mollet, Pieter; Van Holen, Roel; Vandenberghe, Stefaan

2013-09-21

The optimization of a whole-body PET system remains a challenging task, as the imaging performance is influenced by a complex interaction of different design parameters. However, it is not always clear which parameters have the largest impact on image quality and are most eligible for optimization. To determine this, we need to be able to assess their influence on image quality. We performed Monte-Carlo simulations of a whole-body PET scanner to predict the influence on image quality of three detector parameters: the TOF resolution, the transverse pixel size and depth-of-interaction (DOI)-correction. The inner diameter of the PET scanner was 65 cm, small enough to allow physical integration into a simultaneous PET-MR system. Point sources were used to evaluate the influence of transverse pixel size and DOI-correction on spatial resolution as function of radial distance. To evaluate the influence on contrast recovery and pixel noise a cylindrical phantom of 35 cm diameter was used, representing a large patient. The phantom contained multiple hot lesions with 5 mm diameter. These lesions were placed at radial distances of 50, 100 and 150 mm from the center of the field-of-view, to be able to study the effects at different radial positions. The non-prewhitening (NPW) observer was used for objective analysis of the detectability of the hot lesions in the cylindrical phantom. Based on this analysis the NPW-SNR was used to quantify the relative improvements in image quality due to changes of the variable detector parameters. The image quality of a whole-body PET scanner can be improved significantly by reducing the transverse pixel size from 4 to 2.6 mm and improving the TOF resolution from 600 to 400 ps and further from 400 to 200 ps. Compared to pixel size, the TOF resolution has the larger potential to increase image quality for the simulated phantom. The introduction of two layer DOI-correction only leads to a modest improvement for the spheres at radial distance of 150 mm from the center of the transaxial FOV.

Ethnicity identification from face images

NASA Astrophysics Data System (ADS)

Lu, Xiaoguang; Jain, Anil K.

2004-08-01

Human facial images provide the demographic information, such as ethnicity and gender. Conversely, ethnicity and gender also play an important role in face-related applications. Image-based ethnicity identification problem is addressed in a machine learning framework. The Linear Discriminant Analysis (LDA) based scheme is presented for the two-class (Asian vs. non-Asian) ethnicity classification task. Multiscale analysis is applied to the input facial images. An ensemble framework, which integrates the LDA analysis for the input face images at different scales, is proposed to further improve the classification performance. The product rule is used as the combination strategy in the ensemble. Experimental results based on a face database containing 263 subjects (2,630 face images, with equal balance between the two classes) are promising, indicating that LDA and the proposed ensemble framework have sufficient discriminative power for the ethnicity classification problem. The normalized ethnicity classification scores can be helpful in the facial identity recognition. Useful as a "soft" biometric, face matching scores can be updated based on the output of ethnicity classification module. In other words, ethnicity classifier does not have to be perfect to be useful in practice.

Image analysis and machine learning for detecting malaria.

PubMed

Poostchi, Mahdieh; Silamut, Kamolrat; Maude, Richard J; Jaeger, Stefan; Thoma, George

2018-04-01

Malaria remains a major burden on global health, with roughly 200 million cases worldwide and more than 400,000 deaths per year. Besides biomedical research and political efforts, modern information technology is playing a key role in many attempts at fighting the disease. One of the barriers toward a successful mortality reduction has been inadequate malaria diagnosis in particular. To improve diagnosis, image analysis software and machine learning methods have been used to quantify parasitemia in microscopic blood slides. This article gives an overview of these techniques and discusses the current developments in image analysis and machine learning for microscopic malaria diagnosis. We organize the different approaches published in the literature according to the techniques used for imaging, image preprocessing, parasite detection and cell segmentation, feature computation, and automatic cell classification. Readers will find the different techniques listed in tables, with the relevant articles cited next to them, for both thin and thick blood smear images. We also discussed the latest developments in sections devoted to deep learning and smartphone technology for future malaria diagnosis. Published by Elsevier Inc.

Contrast in Terahertz Images of Archival Documents—Part II: Influence of Topographic Features

NASA Astrophysics Data System (ADS)

Bardon, Tiphaine; May, Robert K.; Taday, Philip F.; Strlič, Matija

2017-04-01

We investigate the potential of terahertz time-domain imaging in reflection mode to reveal archival information in documents in a non-invasive way. In particular, this study explores the parameters and signal processing tools that can be used to produce well-contrasted terahertz images of topographic features commonly found in archival documents, such as indentations left by a writing tool, as well as sieve lines. While the amplitude of the waveforms at a specific time delay can provide the most contrasted and legible images of topographic features on flat paper or parchment sheets, this parameter may not be suitable for documents that have a highly irregular surface, such as water- or fire-damaged documents. For analysis of such documents, cross-correlation of the time-domain signals can instead yield images with good contrast. Analysis of the frequency-domain representation of terahertz waveforms can also provide well-contrasted images of topographic features, with improved spatial resolution when utilising high-frequency content. Finally, we point out some of the limitations of these means of analysis for extracting information relating to topographic features of interest from documents.

Poster — Thur Eve — 15: Improvements in the stability of the tomotherapy imaging beam

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

Belec, J

2014-08-15

Use of helical TomoTherapy based MVCT imaging for adaptive planning requires the image values (HU) to remain stable over the course of treatment. In the past, the image value stability was suboptimal, which required frequent change to the image value to density calibration curve to avoid dose errors on the order of 2–4%. The stability of the image values at our center was recently improved by stabilizing the dose rate of the machine (dose control servo) and performing daily MVCT calibration corrections. In this work, we quantify the stability of the image values over treatment time by comparing patient treatmentmore » image density derived using MVCT and KVCT. The analysis includes 1) MVCT - KVCT density difference histogram, 2) MVCT vs KVCT density spectrum, 3) multiple average profile density comparison and 4) density difference in homogeneous locations. Over two months, the imaging beam stability was compromised several times due to a combination of target wobbling, spectral calibration, target change and magnetron issues. The stability of the image values were analyzed over the same period. Results show that the impact on the patient dose calculation is 0.7% +− 0.6%.« less

Fluorescent screens and image processing for the APS linac test stand

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

Berg, W.; Ko, K.

A fluorescent screen was used to monitor relative beam position and spot size of a 56-MeV electron beam in the linac test stand. A chromium doped alumina ceramic screen inserted into the beam was monitored by a video camera. The resulting image was captured using a frame grabber and stored into memory. Reconstruction and analysis of the stored image was performed using PV-WAVE. This paper will discuss the hardware and software implementation of the fluorescent screen and imaging system. Proposed improvements for the APS linac fluorescent screens and image processing will also be discussed.

Radar image enhancement and simulation as an aid to interpretation and training

NASA Technical Reports Server (NTRS)

Frost, V. S.; Stiles, J. A.; Holtzman, J. C.; Dellwig, L. F.; Held, D. N.

1980-01-01

Greatly increased activity in the field of radar image applications in the coming years demands that techniques of radar image analysis, enhancement, and simulation be developed now. Since the statistical nature of radar imagery differs from that of photographic imagery, one finds that the required digital image processing algorithms (e.g., for improved viewing and feature extraction) differ from those currently existing. This paper addresses these problems and discusses work at the Remote Sensing Laboratory in image simulation and processing, especially for systems comparable to the formerly operational SEASAT synthetic aperture radar.

Application of an electronic image analyzer to dimensional measurements from neutron radiographs

NASA Technical Reports Server (NTRS)

Vary, A.; Bowles, K. J.

1973-01-01

Means of obtaining improved dimensional measurements from neutron radiographs of nuclear fuel elements are discussed. The use of video-electronic image analysis relative to edge definition in radiographic images is described. Based on this study, an edge definition criterion is proposed for overcoming image unsharpness effects in taking accurate diametral measurements from radiographs. An electronic density slicing method for automatic edge definition is described. Results of measurements made with video micrometry are compared with scanning microdensitometer and micrometric physical measurements. An image quality indicator for estimating photographic and geometric unsharpness is described.

New development of the image matching algorithm

NASA Astrophysics Data System (ADS)

Zhang, Xiaoqiang; Feng, Zhao

2018-04-01

To study the image matching algorithm, algorithm four elements are described, i.e., similarity measurement, feature space, search space and search strategy. Four common indexes for evaluating the image matching algorithm are described, i.e., matching accuracy, matching efficiency, robustness and universality. Meanwhile, this paper describes the principle of image matching algorithm based on the gray value, image matching algorithm based on the feature, image matching algorithm based on the frequency domain analysis, image matching algorithm based on the neural network and image matching algorithm based on the semantic recognition, and analyzes their characteristics and latest research achievements. Finally, the development trend of image matching algorithm is discussed. This study is significant for the algorithm improvement, new algorithm design and algorithm selection in practice.

Spectrally optimal illuminations for diabetic retinopathy detection in retinal imaging

NASA Astrophysics Data System (ADS)

Bartczak, Piotr; Fält, Pauli; Penttinen, Niko; Ylitepsa, Pasi; Laaksonen, Lauri; Lensu, Lasse; Hauta-Kasari, Markku; Uusitalo, Hannu

2017-04-01

Retinal photography is a standard method for recording retinal diseases for subsequent analysis and diagnosis. However, the currently used white light or red-free retinal imaging does not necessarily provide the best possible visibility of different types of retinal lesions, important when developing diagnostic tools for handheld devices, such as smartphones. Using specifically designed illumination, the visibility and contrast of retinal lesions could be improved. In this study, spectrally optimal illuminations for diabetic retinopathy lesion visualization are implemented using a spectrally tunable light source based on digital micromirror device. The applicability of this method was tested in vivo by taking retinal monochrome images from the eyes of five diabetic volunteers and two non-diabetic control subjects. For comparison to existing methods, we evaluated the contrast of retinal images taken with our method and red-free illumination. The preliminary results show that the use of optimal illuminations improved the contrast of diabetic lesions in retinal images by 30-70%, compared to the traditional red-free illumination imaging.

New developments of X-ray fluorescence imaging techniques in laboratory

NASA Astrophysics Data System (ADS)

Tsuji, Kouichi; Matsuno, Tsuyoshi; Takimoto, Yuki; Yamanashi, Masaki; Kometani, Noritsugu; Sasaki, Yuji C.; Hasegawa, Takeshi; Kato, Shuichi; Yamada, Takashi; Shoji, Takashi; Kawahara, Naoki

2015-11-01

X-ray fluorescence (XRF) analysis is a well-established analytical technique with a long research history. Many applications have been reported in various fields, such as in the environmental, archeological, biological, and forensic sciences as well as in industry. This is because XRF has a unique advantage of being a nondestructive analytical tool with good precision for quantitative analysis. Recent advances in XRF analysis have been realized by the development of new x-ray optics and x-ray detectors. Advanced x-ray focusing optics enables the making of a micro x-ray beam, leading to micro-XRF analysis and XRF imaging. A confocal micro-XRF technique has been applied for the visualization of elemental distributions inside the samples. This technique was applied for liquid samples and for monitoring chemical reactions such as the metal corrosion of steel samples in the NaCl solutions. In addition, a principal component analysis was applied for reducing the background intensity in XRF spectra obtained during XRF mapping, leading to improved spatial resolution of confocal micro-XRF images. In parallel, the authors have proposed a wavelength dispersive XRF (WD-XRF) imaging spectrometer for a fast elemental imaging. A new two dimensional x-ray detector, the Pilatus detector was applied for WD-XRF imaging. Fast XRF imaging in 1 s or even less was demonstrated for Euro coins and industrial samples. In this review paper, these recent advances in laboratory-based XRF imaging, especially in a laboratory setting, will be introduced.

Analysis of neoplastic lesions in magnetic resonance imaging using self-organizing maps.

PubMed

Mei, Paulo Afonso; de Carvalho Carneiro, Cleyton; Fraser, Stephen J; Min, Li Li; Reis, Fabiano

2015-12-15

To provide an improved method for the identification and analysis of brain tumors in MRI scans using a semi-automated computational approach, that has the potential to provide a more objective, precise and quantitatively rigorous analysis, compared to human visual analysis. Self-Organizing Maps (SOM) is an unsupervised, exploratory data analysis tool, which can automatically domain an image into selfsimilar regions or clusters, based on measures of similarity. It can be used to perform image-domain of brain tissue on MR images, without prior knowledge. We used SOM to analyze T1, T2 and FLAIR acquisitions from two MRI machines in our service from 14 patients with brain tumors confirmed by biopsies--three lymphomas, six glioblastomas, one meningioma, one ganglioglioma, two oligoastrocytomas and one astrocytoma. The SOM software was used to analyze the data from the three image acquisitions from each patient and generated a self-organized map for each containing 25 clusters. Damaged tissue was separated from the normal tissue using the SOM technique. Furthermore, in some cases it allowed to separate different areas from within the tumor--like edema/peritumoral infiltration and necrosis. In lesions with less precise boundaries in FLAIR, the estimated damaged tissue area in the resulting map appears bigger. Our results showed that SOM has the potential to be a powerful MR imaging analysis technique for the assessment of brain tumors. Copyright © 2015. Published by Elsevier B.V.

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.

A multimedia electronic patient record (ePR) system to improve decision support in pre- and rehabilitation through clinical and movement analysis

NASA Astrophysics Data System (ADS)

Liu, Brent; Documet, Jorge; McNitt-Gray, Sarah; Requejo, Phil; McNitt-Gray, Jill

2011-03-01

Clinical decisions for improving motor function in patients both with disability as well as improving an athlete's performance are made through clinical and movement analysis. Currently, this analysis facilitates identifying abnormalities in a patient's motor function for a large amount of neuro-musculoskeletal pathologies. However definitively identifying the underlying cause or long-term consequences of a specific abnormality in the patient's movement pattern is difficult since this requires information from multiple sources and formats across different times and currently relies on the experience and intuition of the expert clinician. In addition, this data must be persistent for longitudinal outcomes studies. Therefore a multimedia ePR system integrating imaging informatics data could have a significant impact on decision support within this clinical workflow. We present the design and architecture of such an ePR system as well as the data types that need integration in order to develop relevant decision support tools. Specifically, we will present two data model examples: 1) A performance improvement project involving volleyball athletes and 2) Wheelchair propulsion evaluation of patients with disabilities. The end result is a new frontier area of imaging informatics research within rehabilitation engineering and biomechanics.

Redundancy Analysis of Capacitance Data of a Coplanar Electrode Array for Fast and Stable Imaging Processing

PubMed Central

Wen, Yintang; Zhang, Zhenda; Zhang, Yuyan; Sun, Dongtao

2017-01-01

A coplanar electrode array sensor is established for the imaging of composite-material adhesive-layer defect detection. The sensor is based on the capacitive edge effect, which leads to capacitance data being considerably weak and susceptible to environmental noise. The inverse problem of coplanar array electrical capacitance tomography (C-ECT) is ill-conditioning, in which a small error of capacitance data can seriously affect the quality of reconstructed images. In order to achieve a stable image reconstruction process, a redundancy analysis method for capacitance data is proposed. The proposed method is based on contribution rate and anti-interference capability. According to the redundancy analysis, the capacitance data are divided into valid and invalid data. When the image is reconstructed by valid data, the sensitivity matrix needs to be changed accordingly. In order to evaluate the effectiveness of the sensitivity map, singular value decomposition (SVD) is used. Finally, the two-dimensional (2D) and three-dimensional (3D) images are reconstructed by the Tikhonov regularization method. Through comparison of the reconstructed images of raw capacitance data, the stability of the image reconstruction process can be improved, and the quality of reconstructed images is not degraded. As a result, much invalid data are not collected, and the data acquisition time can also be reduced. PMID:29295537

An image-processing method to detect sub-optical features based on understanding noise in intensity measurements.

PubMed

Bhatia, Tripta

2018-07-01

Accurate quantitative analysis of image data requires that we distinguish between fluorescence intensity (true signal) and the noise inherent to its measurements to the extent possible. We image multilamellar membrane tubes and beads that grow from defects in the fluid lamellar phase of the lipid 1,2-dioleoyl-sn-glycero-3-phosphocholine dissolved in water and water-glycerol mixtures by using fluorescence confocal polarizing microscope. We quantify image noise and determine the noise statistics. Understanding the nature of image noise also helps in optimizing image processing to detect sub-optical features, which would otherwise remain hidden. We use an image-processing technique "optimum smoothening" to improve the signal-to-noise ratio of features of interest without smearing their structural details. A high SNR renders desired positional accuracy with which it is possible to resolve features of interest with width below optical resolution. Using optimum smoothening, the smallest and the largest core diameter detected is of width [Formula: see text] and [Formula: see text] nm, respectively, discussed in this paper. The image-processing and analysis techniques and the noise modeling discussed in this paper can be used for detailed morphological analysis of features down to sub-optical length scales that are obtained by any kind of fluorescence intensity imaging in the raster mode.

Principles of Quantitative MR Imaging with Illustrated Review of Applicable Modular Pulse Diagrams.

PubMed

Mills, Andrew F; Sakai, Osamu; Anderson, Stephan W; Jara, Hernan

2017-01-01

Continued improvements in diagnostic accuracy using magnetic resonance (MR) imaging will require development of methods for tissue analysis that complement traditional qualitative MR imaging studies. Quantitative MR imaging is based on measurement and interpretation of tissue-specific parameters independent of experimental design, compared with qualitative MR imaging, which relies on interpretation of tissue contrast that results from experimental pulse sequence parameters. Quantitative MR imaging represents a natural next step in the evolution of MR imaging practice, since quantitative MR imaging data can be acquired using currently available qualitative imaging pulse sequences without modifications to imaging equipment. The article presents a review of the basic physical concepts used in MR imaging and how quantitative MR imaging is distinct from qualitative MR imaging. Subsequently, the article reviews the hierarchical organization of major applicable pulse sequences used in this article, with the sequences organized into conventional, hybrid, and multispectral sequences capable of calculating the main tissue parameters of T1, T2, and proton density. While this new concept offers the potential for improved diagnostic accuracy and workflow, awareness of this extension to qualitative imaging is generally low. This article reviews the basic physical concepts in MR imaging, describes commonly measured tissue parameters in quantitative MR imaging, and presents the major available pulse sequences used for quantitative MR imaging, with a focus on the hierarchical organization of these sequences. © RSNA, 2017.

Computerized multiple image analysis on mammograms: performance improvement of nipple identification for registration of multiple views using texture convergence analyses

NASA Astrophysics Data System (ADS)

Zhou, Chuan; Chan, Heang-Ping; Sahiner, Berkman; Hadjiiski, Lubomir M.; Paramagul, Chintana

2004-05-01

Automated registration of multiple mammograms for CAD depends on accurate nipple identification. We developed two new image analysis techniques based on geometric and texture convergence analyses to improve the performance of our previously developed nipple identification method. A gradient-based algorithm is used to automatically track the breast boundary. The nipple search region along the boundary is then defined by geometric convergence analysis of the breast shape. Three nipple candidates are identified by detecting the changes along the gray level profiles inside and outside the boundary and the changes in the boundary direction. A texture orientation-field analysis method is developed to estimate the fourth nipple candidate based on the convergence of the tissue texture pattern towards the nipple. The final nipple location is determined from the four nipple candidates by a confidence analysis. Our training and test data sets consisted of 419 and 368 randomly selected mammograms, respectively. The nipple location identified on each image by an experienced radiologist was used as the ground truth. For 118 of the training and 70 of the test images, the radiologist could not positively identify the nipple, but provided an estimate of its location. These were referred to as invisible nipple images. In the training data set, 89.37% (269/301) of the visible nipples and 81.36% (96/118) of the invisible nipples could be detected within 1 cm of the truth. In the test data set, 92.28% (275/298) of the visible nipples and 67.14% (47/70) of the invisible nipples were identified within 1 cm of the truth. In comparison, our previous nipple identification method without using the two convergence analysis techniques detected 82.39% (248/301), 77.12% (91/118), 89.93% (268/298) and 54.29% (38/70) of the nipples within 1 cm of the truth for the visible and invisible nipples in the training and test sets, respectively. The results indicate that the nipple on mammograms can be detected accurately. This will be an important step towards automatic multiple image analysis for CAD techniques.

The Pan-STARRS PS1 Image Processing Pipeline

NASA Astrophysics Data System (ADS)

Magnier, E.

The Pan-STARRS PS1 Image Processing Pipeline (IPP) performs the image processing and data analysis tasks needed to enable the scientific use of the images obtained by the Pan-STARRS PS1 prototype telescope. The primary goals of the IPP are to process the science images from the Pan-STARRS telescopes and make the results available to other systems within Pan-STARRS. It also is responsible for combining all of the science images in a given filter into a single representation of the non-variable component of the night sky defined as the "Static Sky". To achieve these goals, the IPP also performs other analysis functions to generate the calibrations needed in the science image processing, and to occasionally use the derived data to generate improved astrometric and photometric reference catalogs. It also provides the infrastructure needed to store the incoming data and the resulting data products. The IPP inherits lessons learned, and in some cases code and prototype code, from several other astronomy image analysis systems, including Imcat (Kaiser), the Sloan Digital Sky Survey (REF), the Elixir system (Magnier & Cuillandre), and Vista (Tonry). Imcat and Vista have a large number of robust image processing functions. SDSS has demonstrated a working analysis pipeline and large-scale databasesystem for a dedicated project. The Elixir system has demonstrated an automatic image processing system and an object database system for operational usage. This talk will present an overview of the IPP architecture, functional flow, code development structure, and selected analysis algorithms. Also discussed is the HW highly parallel HW configuration necessary to support PS1 operational requirements. Finally, results are presented of the processing of images collected during PS1 early commissioning tasks utilizing the Pan-STARRS Test Camera #3.

A statistically harmonized alignment-classification in image space enables accurate and robust alignment of noisy images in single particle analysis.

PubMed

Kawata, Masaaki; Sato, Chikara

2007-06-01

In determining the three-dimensional (3D) structure of macromolecular assemblies in single particle analysis, a large representative dataset of two-dimensional (2D) average images from huge number of raw images is a key for high resolution. Because alignments prior to averaging are computationally intensive, currently available multireference alignment (MRA) software does not survey every possible alignment. This leads to misaligned images, creating blurred averages and reducing the quality of the final 3D reconstruction. We present a new method, in which multireference alignment is harmonized with classification (multireference multiple alignment: MRMA). This method enables a statistical comparison of multiple alignment peaks, reflecting the similarities between each raw image and a set of reference images. Among the selected alignment candidates for each raw image, misaligned images are statistically excluded, based on the principle that aligned raw images of similar projections have a dense distribution around the correctly aligned coordinates in image space. This newly developed method was examined for accuracy and speed using model image sets with various signal-to-noise ratios, and with electron microscope images of the Transient Receptor Potential C3 and the sodium channel. In every data set, the newly developed method outperformed conventional methods in robustness against noise and in speed, creating 2D average images of higher quality. This statistically harmonized alignment-classification combination should greatly improve the quality of single particle analysis.

Image Registration Algorithm Based on Parallax Constraint and Clustering Analysis

NASA Astrophysics Data System (ADS)

Wang, Zhe; Dong, Min; Mu, Xiaomin; Wang, Song

2018-01-01

To resolve the problem of slow computation speed and low matching accuracy in image registration, a new image registration algorithm based on parallax constraint and clustering analysis is proposed. Firstly, Harris corner detection algorithm is used to extract the feature points of two images. Secondly, use Normalized Cross Correlation (NCC) function to perform the approximate matching of feature points, and the initial feature pair is obtained. Then, according to the parallax constraint condition, the initial feature pair is preprocessed by K-means clustering algorithm, which is used to remove the feature point pairs with obvious errors in the approximate matching process. Finally, adopt Random Sample Consensus (RANSAC) algorithm to optimize the feature points to obtain the final feature point matching result, and the fast and accurate image registration is realized. The experimental results show that the image registration algorithm proposed in this paper can improve the accuracy of the image matching while ensuring the real-time performance of the algorithm.

Quantitative analysis and temperature-induced variations of moiré pattern in fiber-coupled imaging sensors.

PubMed

Karbasi, Salman; Arianpour, Ashkan; Motamedi, Nojan; Mellette, William M; Ford, Joseph E

2015-06-10

Imaging fiber bundles can map the curved image surface formed by some high-performance lenses onto flat focal plane detectors. The relative alignment between the focal plane array pixels and the quasi-periodic fiber-bundle cores can impose an undesirable space variant moiré pattern, but this effect may be greatly reduced by flat-field calibration, provided that the local responsivity is known. Here we demonstrate a stable metric for spatial analysis of the moiré pattern strength, and use it to quantify the effect of relative sensor and fiber-bundle pitch, and that of the Bayer color filter. We measure the thermal dependence of the moiré pattern, and the achievable improvement by flat-field calibration at different operating temperatures. We show that a flat-field calibration image at a desired operating temperature can be generated using linear interpolation between white images at several fixed temperatures, comparing the final image quality with an experimentally acquired image at the same temperature.

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.

Updating Landsat-derived land-cover maps using change detection and masking techniques

NASA Technical Reports Server (NTRS)

Likens, W.; Maw, K.

1982-01-01

The California Integrated Remote Sensing System's San Bernardino County Project was devised to study the utilization of a data base at a number of jurisdictional levels. The present paper discusses the implementation of change-detection and masking techniques in the updating of Landsat-derived land-cover maps. A baseline landcover classification was first created from a 1976 image, then the adjusted 1976 image was compared with a 1979 scene by the techniques of (1) multidate image classification, (2) difference image-distribution tails thresholding, (3) difference image classification, and (4) multi-dimensional chi-square analysis of a difference image. The union of the results of methods 1, 3 and 4 was used to create a mask of possible change areas between 1976 and 1979, which served to limit analysis of the update image and reduce comparison errors in unchanged areas. The techniques of spatial smoothing of change-detection products, and of combining results of difference change-detection algorithms are also shown to improve Landsat change-detection accuracies.

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

Consumer opinion on social policy approaches to promoting positive body image: Airbrushed media images and disclaimer labels.

PubMed

Paraskeva, Nicole; Lewis-Smith, Helena; Diedrichs, Phillippa C

2017-02-01

Disclaimer labels on airbrushed media images have generated political attention and advocacy as a social policy approach to promoting positive body image. Experimental research suggests that labelling is ineffective and consumers' viewpoints have been overlooked. A mixed-method study explored British consumers' ( N = 1555, aged 11-78 years) opinions on body image and social policy approaches. Thematic analysis indicated scepticism about the effectiveness of labelling images. Quantitatively, adults, although not adolescents, reported that labelling was unlikely to improve body image. Appearance diversity in media and reorienting social norms from appearance to function and health were perceived as effective strategies. Social policy and research implications are discussed.

Measurement of two-dimensional thickness of micro-patterned thin film based on image restoration in a spectroscopic imaging reflectometer.

PubMed

Kim, Min-Gab; Kim, Jin-Yong

2018-05-01

In this paper, we introduce a method to overcome the limitation of thickness measurement of a micro-patterned thin film. A spectroscopic imaging reflectometer system that consists of an acousto-optic tunable filter, a charge-coupled-device camera, and a high-magnitude objective lens was proposed, and a stack of multispectral images was generated. To secure improved accuracy and lateral resolution in the reconstruction of a two-dimensional thin film thickness, prior to the analysis of spectral reflectance profiles from each pixel of multispectral images, the image restoration based on an iterative deconvolution algorithm was applied to compensate for image degradation caused by blurring.

"Big Data" in Rheumatology: Intelligent Data Modeling Improves the Quality of Imaging Data.

PubMed

Landewé, Robert B M; van der Heijde, Désirée

2018-05-01

Analysis of imaging data in rheumatology is a challenge. Reliability of scores is an issue for several reasons. Signal-to-noise ratio of most imaging techniques is rather unfavorable (too little signal in relation to too much noise). Optimal use of all available data may help to increase credibility of imaging data, but knowledge of complicated statistical methodology and the help of skilled statisticians are required. Clinicians should appreciate the merits of sophisticated data modeling and liaise with statisticians to increase the quality of imaging results, as proper imaging studies in rheumatology imply more than a supersensitive imaging technique alone. Copyright © 2018 Elsevier Inc. All rights reserved.

Platform for Post-Processing Waveform-Based NDE

NASA Technical Reports Server (NTRS)

Roth, Don J.

2010-01-01

Signal- and image-processing methods are commonly needed to extract information from the waves, improve resolution of, and highlight defects in an image. Since some similarity exists for all waveform-based nondestructive evaluation (NDE) methods, it would seem that a common software platform containing multiple signal- and image-processing techniques to process the waveforms and images makes sense where multiple techniques, scientists, engineers, and organizations are involved. NDE Wave & Image Processor Version 2.0 software provides a single, integrated signal- and image-processing and analysis environment for total NDE data processing and analysis. It brings some of the most useful algorithms developed for NDE over the past 20 years into a commercial-grade product. The software can import signal/spectroscopic data, image data, and image series data. This software offers the user hundreds of basic and advanced signal- and image-processing capabilities including esoteric 1D and 2D wavelet-based de-noising, de-trending, and filtering. Batch processing is included for signal- and image-processing capability so that an optimized sequence of processing operations can be applied to entire folders of signals, spectra, and images. Additionally, an extensive interactive model-based curve-fitting facility has been included to allow fitting of spectroscopy data such as from Raman spectroscopy. An extensive joint-time frequency module is included for analysis of non-stationary or transient data such as that from acoustic emission, vibration, or earthquake data.

Novel image processing method study for a label-free optical biosensor

NASA Astrophysics Data System (ADS)

Yang, Chenhao; Wei, Li'an; Yang, Rusong; Feng, Ying

2015-10-01

Optical biosensor is generally divided into labeled type and label-free type, the former mainly contains fluorescence labeled method and radioactive-labeled method, while fluorescence-labeled method is more mature in the application. The mainly image processing methods of fluorescent-labeled biosensor includes smooth filtering, artificial gridding and constant thresholding. Since some fluorescent molecules may influence the biological reaction, label-free methods have been the main developing direction of optical biosensors nowadays. The using of wider field of view and larger angle of incidence light path which could effectively improve the sensitivity of the label-free biosensor also brought more difficulties in image processing, comparing with the fluorescent-labeled biosensor. Otsu's method is widely applied in machine vision, etc, which choose the threshold to minimize the intraclass variance of the thresholded black and white pixels. It's capacity-constrained with the asymmetrical distribution of images as a global threshold segmentation. In order to solve the irregularity of light intensity on the transducer, we improved the algorithm. In this paper, we present a new image processing algorithm based on a reflectance modulation biosensor platform, which mainly comprises the design of sliding normalization algorithm for image rectification and utilizing the improved otsu's method for image segmentation, in order to implement automatic recognition of target areas. Finally we used adaptive gridding method extracting the target parameters for analysis. Those methods could improve the efficiency of image processing, reduce human intervention, enhance the reliability of experiments and laid the foundation for the realization of high throughput of label-free optical biosensors.

[Effect of body image in adolescent orthodontic treatment].

PubMed

Minghui, Peng; Jing, Kang; Xiao, Deng

2017-10-01

This study was designed to probe the psychological factors adolescent orthodontic patients, the role of body image and self-esteem in the whole process of orthodontic treatment and the impact on the efficacy and satisfaction of orthodontic. Five hundred and twenty-eight patients were selected in this study. The Aesthetic Component of the Index of Orthodontic Treatment Need (IOTN-AC) , Rosenberg Self-Esteem Scale (SES), Negative Physical Self-General (NPS-G) and other body analysis scale study after orthodontic lasted 18-24 months were used to investigate the role of body image in adolescent orthodontic treatment. Esthetic evaluation of patients teeth after correction had been significantly improved, patient self-evaluation difference IOTN-AC doctor evaluation, Psychosocial Impact of Dental Aesthetics Questionnaire-tooth confidence, aesthetic concerns, psychological impact and social function were significantly improved. The improvement of the dental aesthetics component (T2 when doctors evaluate IOTN-AC) was positively correlated with the evaluation of the efficacy, and was significantly negatively correlated with the negative emotions of patients at baseline. Negative body image-dental dissatisfied-cognitive component and the affective component, the overall negative body image and negative emotions can predict patient satisfaction with treatment efficacy. Orthodontic treatment not only improves the self-aesthetic evaluation of adolescent patients, but also has a positive effect on the mental health of adolescent patients.

A patient-specific segmentation framework for longitudinal MR images of traumatic brain injury

NASA Astrophysics Data System (ADS)

Wang, Bo; Prastawa, Marcel; Irimia, Andrei; Chambers, Micah C.; Vespa, Paul M.; Van Horn, John D.; Gerig, Guido

2012-02-01

Traumatic brain injury (TBI) is a major cause of death and disability worldwide. Robust, reproducible segmentations of MR images with TBI are crucial for quantitative analysis of recovery and treatment efficacy. However, this is a significant challenge due to severe anatomy changes caused by edema (swelling), bleeding, tissue deformation, skull fracture, and other effects related to head injury. In this paper, we introduce a multi-modal image segmentation framework for longitudinal TBI images. The framework is initialized through manual input of primary lesion sites at each time point, which are then refined by a joint approach composed of Bayesian segmentation and construction of a personalized atlas. The personalized atlas construction estimates the average of the posteriors of the Bayesian segmentation at each time point and warps the average back to each time point to provide the updated priors for Bayesian segmentation. The difference between our approach and segmenting longitudinal images independently is that we use the information from all time points to improve the segmentations. Given a manual initialization, our framework automatically segments healthy structures (white matter, grey matter, cerebrospinal fluid) as well as different lesions such as hemorrhagic lesions and edema. Our framework can handle different sets of modalities at each time point, which provides flexibility in analyzing clinical scans. We show results on three subjects with acute baseline scans and chronic follow-up scans. The results demonstrate that joint analysis of all the points yields improved segmentation compared to independent analysis of the two time points.

MSiReader v1.0: Evolving Open-Source Mass Spectrometry Imaging Software for Targeted and Untargeted Analyses.

PubMed

Bokhart, Mark T; Nazari, Milad; Garrard, Kenneth P; Muddiman, David C

2018-01-01

A major update to the mass spectrometry imaging (MSI) software MSiReader is presented, offering a multitude of newly added features critical to MSI analyses. MSiReader is a free, open-source, and vendor-neutral software written in the MATLAB platform and is capable of analyzing most common MSI data formats. A standalone version of the software, which does not require a MATLAB license, is also distributed. The newly incorporated data analysis features expand the utility of MSiReader beyond simple visualization of molecular distributions. The MSiQuantification tool allows researchers to calculate absolute concentrations from quantification MSI experiments exclusively through MSiReader software, significantly reducing data analysis time. An image overlay feature allows the incorporation of complementary imaging modalities to be displayed with the MSI data. A polarity filter has also been incorporated into the data loading step, allowing the facile analysis of polarity switching experiments without the need for data parsing prior to loading the data file into MSiReader. A quality assurance feature to generate a mass measurement accuracy (MMA) heatmap for an analyte of interest has also been added to allow for the investigation of MMA across the imaging experiment. Most importantly, as new features have been added performance has not degraded, in fact it has been dramatically improved. These new tools and the improvements to the performance in MSiReader v1.0 enable the MSI community to evaluate their data in greater depth and in less time. Graphical Abstract ᅟ.

Computer-Based Image Analysis for Plus Disease Diagnosis in Retinopathy of Prematurity

PubMed Central

Wittenberg, Leah A.; Jonsson, Nina J.; Chan, RV Paul; Chiang, Michael F.

2014-01-01

Presence of plus disease in retinopathy of prematurity (ROP) is an important criterion for identifying treatment-requiring ROP. Plus disease is defined by a standard published photograph selected over 20 years ago by expert consensus. However, diagnosis of plus disease has been shown to be subjective and qualitative. Computer-based image analysis, using quantitative methods, has potential to improve the objectivity of plus disease diagnosis. The objective was to review the published literature involving computer-based image analysis for ROP diagnosis. The PubMed and Cochrane library databases were searched for the keywords “retinopathy of prematurity” AND “image analysis” AND/OR “plus disease.” Reference lists of retrieved articles were searched to identify additional relevant studies. All relevant English-language studies were reviewed. There are four main computer-based systems, ROPtool (AU ROC curve, plus tortuosity 0.95, plus dilation 0.87), RISA (AU ROC curve, arteriolar TI 0.71, venular diameter 0.82), Vessel Map (AU ROC curve, arteriolar dilation 0.75, venular dilation 0.96), and CAIAR (AU ROC curve, arteriole tortuosity 0.92, venular dilation 0.91), attempting to objectively analyze vessel tortuosity and dilation in plus disease in ROP. Some of them show promise for identification of plus disease using quantitative methods. This has potential to improve the diagnosis of plus disease, and may contribute to the management of ROP using both traditional binocular indirect ophthalmoscopy and image-based telemedicine approaches. PMID:21366159

Satellite image fusion based on principal component analysis and high-pass filtering.

PubMed

Metwalli, Mohamed R; Nasr, Ayman H; Allah, Osama S Farag; El-Rabaie, S; Abd El-Samie, Fathi E

2010-06-01

This paper presents an integrated method for the fusion of satellite images. Several commercial earth observation satellites carry dual-resolution sensors, which provide high spatial resolution or simply high-resolution (HR) panchromatic (pan) images and low-resolution (LR) multi-spectral (MS) images. Image fusion methods are therefore required to integrate a high-spectral-resolution MS image with a high-spatial-resolution pan image to produce a pan-sharpened image with high spectral and spatial resolutions. Some image fusion methods such as the intensity, hue, and saturation (IHS) method, the principal component analysis (PCA) method, and the Brovey transform (BT) method provide HR MS images, but with low spectral quality. Another family of image fusion methods, such as the high-pass-filtering (HPF) method, operates on the basis of the injection of high frequency components from the HR pan image into the MS image. This family of methods provides less spectral distortion. In this paper, we propose the integration of the PCA method and the HPF method to provide a pan-sharpened MS image with superior spatial resolution and less spectral distortion. The experimental results show that the proposed fusion method retains the spectral characteristics of the MS image and, at the same time, improves the spatial resolution of the pan-sharpened image.

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

PubMed

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

2017-01-01

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

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

PubMed Central

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

2017-01-01

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

Lensfree super-resolution holographic microscopy using wetting films on a chip

NASA Astrophysics Data System (ADS)

Mudanyali, Onur; Bishara, Waheb; Ozcan, Aydogan

2011-08-01

We investigate the use of wetting films to significantly improve the imaging performance of lensfree pixel super-resolution on-chip microscopy, achieving < 1 μm spatial resolution over a large imaging area of ~24 mm2. Formation of an ultra-thin wetting film over the specimen effectively creates a micro-lens effect over each object, which significantly improves the signal-to-noise-ratio and therefore the resolution of our lensfree images. We validate the performance of this approach through lensfree on-chip imaging of various objects having fine morphological features (with dimensions of e.g., ≤0.5 μm) such as Escherichia coli (E. coli), human sperm, Giardia lamblia trophozoites, polystyrene micro beads as well as red blood cells. These results are especially important for the development of highly sensitive field-portable microscopic analysis tools for resource limited settings.

Phase unwrapping using region-based markov random field model.

PubMed

Dong, Ying; Ji, Jim

2010-01-01

Phase unwrapping is a classical problem in Magnetic Resonance Imaging (MRI), Interferometric Synthetic Aperture Radar and Sonar (InSAR/InSAS), fringe pattern analysis, and spectroscopy. Although many methods have been proposed to address this problem, robust and effective phase unwrapping remains a challenge. This paper presents a novel phase unwrapping method using a region-based Markov Random Field (MRF) model. Specifically, the phase image is segmented into regions within which the phase is not wrapped. Then, the phase image is unwrapped between different regions using an improved Highest Confidence First (HCF) algorithm to optimize the MRF model. The proposed method has desirable theoretical properties as well as an efficient implementation. Simulations and experimental results on MRI images show that the proposed method provides similar or improved phase unwrapping than Phase Unwrapping MAx-flow/min-cut (PUMA) method and ZpM method.

Improved computer-aided detection of small polyps in CT colonography using interpolation for curvature estimationa

PubMed Central

Liu, Jiamin; Kabadi, Suraj; Van Uitert, Robert; Petrick, Nicholas; Deriche, Rachid; Summers, Ronald M.

2011-01-01

Purpose: Surface curvatures are important geometric features for the computer-aided analysis and detection of polyps in CT colonography (CTC). However, the general kernel approach for curvature computation can yield erroneous results for small polyps and for polyps that lie on haustral folds. Those erroneous curvatures will reduce the performance of polyp detection. This paper presents an analysis of interpolation’s effect on curvature estimation for thin structures and its application on computer-aided detection of small polyps in CTC. Methods: The authors demonstrated that a simple technique, image interpolation, can improve the accuracy of curvature estimation for thin structures and thus significantly improve the sensitivity of small polyp detection in CTC. Results: Our experiments showed that the merits of interpolating included more accurate curvature values for simulated data, and isolation of polyps near folds for clinical data. After testing on a large clinical data set, it was observed that sensitivities with linear, quadratic B-spline and cubic B-spline interpolations significantly improved the sensitivity for small polyp detection. Conclusions: The image interpolation can improve the accuracy of curvature estimation for thin structures and thus improve the computer-aided detection of small polyps in CTC. PMID:21859029

Evaluation of an accelerated 3D SPACE sequence with compressed sensing and free-stop scan mode for imaging of the knee.

PubMed

Henninger, B; Raithel, E; Kranewitter, C; Steurer, M; Jaschke, W; Kremser, C

2018-05-01

To prospectively evaluate a prototypical 3D turbo-spin-echo proton-density-weighted sequence with compressed sensing and free-stop scan mode for preventing motion artefacts (3D-PD-CS-SPACE free-stop) for knee imaging in a clinical setting. 80 patients underwent 3T magnetic resonance imaging (MRI) of the knee with our 2D routine protocol and with 3D-PD-CS-SPACE free-stop. In case of a scan-stop caused by motion (images are calculated nevertheless) the sequence was repeated without free-stop mode. All scans were evaluated by 2 radiologists concerning image quality of the 3D-PD-CS-SPACE (with and without free-stop). Important knee structures were further assessed in a lesion based analysis and compared to our reference 2D-PD-fs sequences. Image quality of the 3D-PD-CS-SPACE free-stop was found optimal in 47/80, slightly compromised in 21/80, moderately in 10/80 and severely in 2/80. In 29/80, the free-stop scan mode stopped the 3D-PD-CS-SPACE due to subject motion with a slight increase of image quality at longer effective acquisition times. Compared to the 3D-PD-CS-SPACE with free-stop, the image quality of the acquired 3D-PD-CS-SPACE without free-stop was found equal in 6/29, slightly improved in 13/29, improved with equal contours in 8/29, and improved with sharper contours in 2/29. The lesion based analysis showed a high agreement between the results from the 3D-PD-CS-SPACE free-stop and our 2D-PD-fs routine protocol (overall agreement 96.25%-100%, Cohen's Kappa 0.883-1, p < 0.001). 3D-PD-CS-SPACE free-stop is a reliable alternative for standard 2D-PD-fs protocols with acceptable acquisition times. Copyright © 2018 Elsevier B.V. All rights reserved.

Localizer: fast, accurate, open-source, and modular software package for superresolution microscopy

PubMed Central

Duwé, Sam; Neely, Robert K.; Zhang, Jin

2012-01-01

Abstract. We present Localizer, a freely available and open source software package that implements the computational data processing inherent to several types of superresolution fluorescence imaging, such as localization (PALM/STORM/GSDIM) and fluctuation imaging (SOFI/pcSOFI). Localizer delivers high accuracy and performance and comes with a fully featured and easy-to-use graphical user interface but is also designed to be integrated in higher-level analysis environments. Due to its modular design, Localizer can be readily extended with new algorithms as they become available, while maintaining the same interface and performance. We provide front-ends for running Localizer from Igor Pro, Matlab, or as a stand-alone program. We show that Localizer performs favorably when compared with two existing superresolution packages, and to our knowledge is the only freely available implementation of SOFI/pcSOFI microscopy. By dramatically improving the analysis performance and ensuring the easy addition of current and future enhancements, Localizer strongly improves the usability of superresolution imaging in a variety of biomedical studies. PMID:23208219

Improvement of web-based data acquisition and management system for GOSAT validation lidar data analysis

NASA Astrophysics Data System (ADS)

Okumura, Hiroshi; Takubo, Shoichiro; Kawasaki, Takeru; Abdullah, Indra Nugraha; Uchino, Osamu; Morino, Isamu; Yokota, Tatsuya; Nagai, Tomohiro; Sakai, Tetsu; Maki, Takashi; Arai, Kohei

2013-01-01

A web-base data acquisition and management system for GOSAT (Greenhouse gases Observation SATellite) validation lidar data-analysis has been developed. The system consists of data acquisition sub-system (DAS) and data management sub-system (DMS). DAS written in Perl language acquires AMeDAS (Automated Meteorological Data Acquisition System) ground-level local meteorological data, GPS Radiosonde upper-air meteorological data, ground-level oxidant data, skyradiometer data, skyview camera images, meteorological satellite IR image data and GOSAT validation lidar data. DMS written in PHP language demonstrates satellite-pass date and all acquired data. In this article, we briefly describe some improvement for higher performance and higher data usability. GPS Radiosonde upper-air meteorological data and U.S. standard atmospheric model in DAS automatically calculate molecule number density profiles. Predicted ozone density prole images above Saga city are also calculated by using Meteorological Research Institute (MRI) chemistry-climate model version 2 for comparison to actual ozone DIAL data.

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.

Improvement of white matter and functional connectivity abnormalities by repetitive transcranial magnetic stimulation in crossed aphasia in dextral.

PubMed

Lu, Haitao; Wu, Haiyan; Cheng, Hewei; Wei, Dongjie; Wang, Xiaoyan; Fan, Yong; Zhang, Hao; Zhang, Tong

2014-01-01

As a special aphasia, the occurrence of crossed aphasia in dextral (CAD) is unusual. This study aims to improve the language ability by applying 1 Hz repetitive transcranial magnetic stimulation (rTMS). We studied multiple modality imaging of structural connectivity (diffusion tensor imaging), functional connectivity (resting fMRI), PET, and neurolinguistic analysis on a patient with CAD. Furthermore, we applied rTMS of 1 Hz for 40 times and observed the language function improvement. The results indicated that a significantly reduced structural and function connectivity was found in DTI and fMRI data compared with the control. The PET imaging showed hypo-metabolism in right hemisphere and left cerebellum. In conclusion, one of the mechanisms of CAD is that right hemisphere is the language dominance. Stimulating left Wernicke area could improve auditory comprehension, stimulating left Broca's area could enhance expression, and the results outlasted 6 months by 1 Hz rTMS balancing the excitability inter-hemisphere in CAD.

Computer‐assisted surgical planning and intraoperative guidance in fetal surgery: a systematic review†

PubMed Central

Deprest, Jan; Vercauteren, Tom; Ourselin, Sebastien; David, Anna L.

2015-01-01

Abstract Fetal surgery has become a clinical reality, with interventions for twin‐to‐twin transfusion syndrome (TTTS) and spina bifida demonstrated to improve outcome. Fetal imaging is evolving, with the use of 3D ultrasound and fetal MRI becoming more common in clinical practise. Medical imaging analysis is also changing, with technology being developed to assist surgeons by creating 3D virtual models that improve understanding of complex anatomy, and prove powerful tools in surgical planning and intraoperative guidance. We introduce the concept of computer‐assisted surgical planning, and present the results of a systematic review of image reconstruction for fetal surgical planning that identified six articles using such technology. Indications from other specialities suggest a benefit of surgical planning and guidance to improve outcomes. There is therefore an urgent need to develop fetal‐specific technology in order to improve fetal surgical outcome. © 2015 The Authors. Prenatal Diagnosis published by John Wiley & Sons Ltd. PMID:26235960

Ghost image in enhanced self-heterodyne synthetic aperture imaging ladar

NASA Astrophysics Data System (ADS)

Zhang, Guo; Sun, Jianfeng; Zhou, Yu; Lu, Zhiyong; Li, Guangyuan; Xu, Mengmeng; Zhang, Bo; Lao, Chenzhe; He, Hongyu

2018-03-01

The enhanced self-heterodyne synthetic aperture imaging ladar (SAIL) self-heterodynes two polarization-orthogonal echo signals to eliminate the phase disturbance caused by atmospheric turbulence and mechanical trembling, uses heterodyne receiver instead of self-heterodyne receiver to improve signal-to-noise ratio. The principle and structure of the enhanced self-heterodyne SAIL are presented. The imaging process of enhanced self-heterodyne SAIL for distributed target is also analyzed. In enhanced self-heterodyne SAIL, the phases of two orthogonal-polarization beams are modulated by four cylindrical lenses in transmitter to improve resolutions in orthogonal direction and travel direction, which will generate ghost image. The generation process of ghost image in enhanced self-heterodyne SAIL is mathematically detailed, and a method of eliminating ghost image is also presented, which is significant for far-distance imaging. A number of experiments of enhanced self-heterodyne SAIL for distributed target are presented, these experimental results verify the theoretical analysis of enhanced self-heterodyne SAIL. The enhanced self-heterodyne SAIL has the capability to eliminate the influence from the atmospheric turbulence and mechanical trembling, has high advantage in detecting weak signals, and has promising application for far-distance ladar imaging.

A novel computer-assisted image analysis of [123I]β-CIT SPECT images improves the diagnostic accuracy of parkinsonian disorders.

PubMed

Goebel, Georg; Seppi, Klaus; Donnemiller, Eveline; Warwitz, Boris; Wenning, Gregor K; Virgolini, Irene; Poewe, Werner; Scherfler, Christoph

2011-04-01

The purpose of this study was to develop an observer-independent algorithm for the correct classification of dopamine transporter SPECT images as Parkinson's disease (PD), multiple system atrophy parkinson variant (MSA-P), progressive supranuclear palsy (PSP) or normal. A total of 60 subjects with clinically probable PD (n = 15), MSA-P (n = 15) and PSP (n = 15), and 15 age-matched healthy volunteers, were studied with the dopamine transporter ligand [(123)I]β-CIT. Parametric images of the specific-to-nondisplaceable equilibrium partition coefficient (BP(ND)) were generated. Following a voxel-wise ANOVA, cut-off values were calculated from the voxel values of the resulting six post-hoc t-test maps. The percentages of the volume of an individual BP(ND) image remaining below and above the cut-off values were determined. The higher percentage of image volume from all six cut-off matrices was used to classify an individual's image. For validation, the algorithm was compared to a conventional region of interest analysis. The predictive diagnostic accuracy of the algorithm in the correct assignment of a [(123)I]β-CIT SPECT image was 83.3% and increased to 93.3% on merging the MSA-P and PSP groups. In contrast the multinomial logistic regression of mean region of interest values of the caudate, putamen and midbrain revealed a diagnostic accuracy of 71.7%. In contrast to a rater-driven approach, this novel method was superior in classifying [(123)I]β-CIT-SPECT images as one of four diagnostic entities. In combination with the investigator-driven visual assessment of SPECT images, this clinical decision support tool would help to improve the diagnostic yield of [(123)I]β-CIT SPECT in patients presenting with parkinsonism at their initial visit.

Supervised detection of exoplanets in high-contrast imaging sequences

NASA Astrophysics Data System (ADS)

Gomez Gonzalez, C. A.; Absil, O.; Van Droogenbroeck, M.

2018-06-01

Context. Post-processing algorithms play a key role in pushing the detection limits of high-contrast imaging (HCI) instruments. State-of-the-art image processing approaches for HCI enable the production of science-ready images relying on unsupervised learning techniques, such as low-rank approximations, for generating a model point spread function (PSF) and subtracting the residual starlight and speckle noise. Aims: In order to maximize the detection rate of HCI instruments and survey campaigns, advanced algorithms with higher sensitivities to faint companions are needed, especially for the speckle-dominated innermost region of the images. Methods: We propose a reformulation of the exoplanet detection task (for ADI sequences) that builds on well-established machine learning techniques to take HCI post-processing from an unsupervised to a supervised learning context. In this new framework, we present algorithmic solutions using two different discriminative models: SODIRF (random forests) and SODINN (neural networks). We test these algorithms on real ADI datasets from VLT/NACO and VLT/SPHERE HCI instruments. We then assess their performances by injecting fake companions and using receiver operating characteristic analysis. This is done in comparison with state-of-the-art ADI algorithms, such as ADI principal component analysis (ADI-PCA). Results: This study shows the improved sensitivity versus specificity trade-off of the proposed supervised detection approach. At the diffraction limit, SODINN improves the true positive rate by a factor ranging from 2 to 10 (depending on the dataset and angular separation) with respect to ADI-PCA when working at the same false-positive level. Conclusions: The proposed supervised detection framework outperforms state-of-the-art techniques in the task of discriminating planet signal from speckles. In addition, it offers the possibility of re-processing existing HCI databases to maximize their scientific return and potentially improve the demographics of directly imaged exoplanets.

Extraction of composite visual objects from audiovisual materials

NASA Astrophysics Data System (ADS)

Durand, Gwenael; Thienot, Cedric; Faudemay, Pascal

1999-08-01

An effective analysis of Visual Objects appearing in still images and video frames is required in order to offer fine grain access to multimedia and audiovisual contents. In previous papers, we showed how our method for segmenting still images into visual objects could improve content-based image retrieval and video analysis methods. Visual Objects are used in particular for extracting semantic knowledge about the contents. However, low-level segmentation methods for still images are not likely to extract a complex object as a whole but instead as a set of several sub-objects. For example, a person would be segmented into three visual objects: a face, hair, and a body. In this paper, we introduce the concept of Composite Visual Object. Such an object is hierarchically composed of sub-objects called Component Objects.

An optical flow-based method for velocity field of fluid flow estimation

NASA Astrophysics Data System (ADS)

Głomb, Grzegorz; Świrniak, Grzegorz; Mroczka, Janusz

2017-06-01

The aim of this paper is to present a method for estimating flow-velocity vector fields using the Lucas-Kanade algorithm. The optical flow measurements are based on the Particle Image Velocimetry (PIV) technique, which is commonly used in fluid mechanics laboratories in both research institutes and industry. Common approaches for an optical characterization of velocity fields base on computation of partial derivatives of the image intensity using finite differences. Nevertheless, the accuracy of velocity field computations is low due to the fact that an exact estimation of spatial derivatives is very difficult in presence of rapid intensity changes in the PIV images, caused by particles having small diameters. The method discussed in this paper solves this problem by interpolating the PIV images using Gaussian radial basis functions. This provides a significant improvement in the accuracy of the velocity estimation but, more importantly, allows for the evaluation of the derivatives in intermediate points between pixels. Numerical analysis proves that the method is able to estimate even a separate vector for each particle with a 5× 5 px2 window, whereas a classical correlation-based method needs at least 4 particle images. With the use of a specialized multi-step hybrid approach to data analysis the method improves the estimation of the particle displacement far above 1 px.

A noise power spectrum study of a new model-based iterative reconstruction system: Veo 3.0.

PubMed

Li, Guang; Liu, Xinming; Dodge, Cristina T; Jensen, Corey T; Rong, X John

2016-09-08

The purpose of this study was to evaluate performance of the third generation of model-based iterative reconstruction (MBIR) system, Veo 3.0, based on noise power spectrum (NPS) analysis with various clinical presets over a wide range of clinically applicable dose levels. A CatPhan 600 surrounded by an oval, fat-equivalent ring to mimic patient size/shape was scanned 10 times at each of six dose levels on a GE HD 750 scanner. NPS analysis was performed on images reconstructed with various Veo 3.0 preset combinations for comparisons of those images reconstructed using Veo 2.0, filtered back projection (FBP) and adaptive statistical iterative reconstruc-tion (ASiR). The new Target Thickness setting resulted in higher noise in thicker axial images. The new Texture Enhancement function achieved a more isotropic noise behavior with less image artifacts. Veo 3.0 provides additional reconstruction options designed to allow the user choice of balance between spatial resolution and image noise, relative to Veo 2.0. Veo 3.0 provides more user selectable options and in general improved isotropic noise behavior in comparison to Veo 2.0. The overall noise reduction performance of both versions of MBIR was improved in comparison to FBP and ASiR, especially at low-dose levels. © 2016 The Authors.

A survey of infrared and visual image fusion methods

NASA Astrophysics Data System (ADS)

Jin, Xin; Jiang, Qian; Yao, Shaowen; Zhou, Dongming; Nie, Rencan; Hai, Jinjin; He, Kangjian

2017-09-01

Infrared (IR) and visual (VI) image fusion is designed to fuse multiple source images into a comprehensive image to boost imaging quality and reduce redundancy information, which is widely used in various imaging equipment to improve the visual ability of human and robot. The accurate, reliable and complementary descriptions of the scene in fused images make these techniques be widely used in various fields. In recent years, a large number of fusion methods for IR and VI images have been proposed due to the ever-growing demands and the progress of image representation methods; however, there has not been published an integrated survey paper about this field in last several years. Therefore, we make a survey to report the algorithmic developments of IR and VI image fusion. In this paper, we first characterize the IR and VI image fusion based applications to represent an overview of the research status. Then we present a synthesize survey of the state of the art. Thirdly, the frequently-used image fusion quality measures are introduced. Fourthly, we perform some experiments of typical methods and make corresponding analysis. At last, we summarize the corresponding tendencies and challenges in IR and VI image fusion. This survey concludes that although various IR and VI image fusion methods have been proposed, there still exist further improvements or potential research directions in different applications of IR and VI image fusion.

Heterogeneous Optimization Framework: Reproducible Preprocessing of Multi-Spectral Clinical MRI for Neuro-Oncology Imaging Research.

PubMed

Milchenko, Mikhail; Snyder, Abraham Z; LaMontagne, Pamela; Shimony, Joshua S; Benzinger, Tammie L; Fouke, Sarah Jost; Marcus, Daniel S

2016-07-01

Neuroimaging research often relies on clinically acquired magnetic resonance imaging (MRI) datasets that can originate from multiple institutions. Such datasets are characterized by high heterogeneity of modalities and variability of sequence parameters. This heterogeneity complicates the automation of image processing tasks such as spatial co-registration and physiological or functional image analysis. Given this heterogeneity, conventional processing workflows developed for research purposes are not optimal for clinical data. In this work, we describe an approach called Heterogeneous Optimization Framework (HOF) for developing image analysis pipelines that can handle the high degree of clinical data non-uniformity. HOF provides a set of guidelines for configuration, algorithm development, deployment, interpretation of results and quality control for such pipelines. At each step, we illustrate the HOF approach using the implementation of an automated pipeline for Multimodal Glioma Analysis (MGA) as an example. The MGA pipeline computes tissue diffusion characteristics of diffusion tensor imaging (DTI) acquisitions, hemodynamic characteristics using a perfusion model of susceptibility contrast (DSC) MRI, and spatial cross-modal co-registration of available anatomical, physiological and derived patient images. Developing MGA within HOF enabled the processing of neuro-oncology MR imaging studies to be fully automated. MGA has been successfully used to analyze over 160 clinical tumor studies to date within several research projects. Introduction of the MGA pipeline improved image processing throughput and, most importantly, effectively produced co-registered datasets that were suitable for advanced analysis despite high heterogeneity in acquisition protocols.

A forensic science perspective on the role of images in crime investigation and reconstruction.

PubMed

Milliet, Quentin; Delémont, Olivier; Margot, Pierre

2014-12-01

This article presents a global vision of images in forensic science. The proliferation of perspectives on the use of images throughout criminal investigations and the increasing demand for research on this topic seem to demand a forensic science-based analysis. In this study, the definitions of and concepts related to material traces are revisited and applied to images, and a structured approach is used to persuade the scientific community to extend and improve the use of images as traces in criminal investigations. Current research efforts focus on technical issues and evidence assessment. This article provides a sound foundation for rationalising and explaining the processes involved in the production of clues from trace images. For example, the mechanisms through which these visual traces become clues of presence or action are described. An extensive literature review of forensic image analysis emphasises the existing guidelines and knowledge available for answering investigative questions (who, what, where, when and how). However, complementary developments are still necessary to demystify many aspects of image analysis in forensic science, including how to review and select images or use them to reconstruct an event or assist intelligence efforts. The hypothetico-deductive reasoning pathway used to discover unknown elements of an event or crime can also help scientists understand the underlying processes involved in their decision making. An analysis of a single image in an investigative or probative context is used to demonstrate the highly informative potential of images as traces and/or clues. Research efforts should be directed toward formalising the extraction and combination of clues from images. An appropriate methodology is key to expanding the use of images in forensic science. Copyright © 2014 Forensic Science Society. Published by Elsevier Ireland Ltd. All rights reserved.

Quantitative analysis of image quality for acceptance and commissioning of an MRI simulator with a semiautomatic method.

PubMed

Chen, Xinyuan; Dai, Jianrong

2018-05-01

Magnetic Resonance Imaging (MRI) simulation differs from diagnostic MRI in purpose, technical requirements, and implementation. We propose a semiautomatic method for image acceptance and commissioning for the scanner, the radiofrequency (RF) coils, and pulse sequences for an MRI simulator. The ACR MRI accreditation large phantom was used for image quality analysis with seven parameters. Standard ACR sequences with a split head coil were adopted to examine the scanner's basic performance. The performance of simulation RF coils were measured and compared using the standard sequence with different clinical diagnostic coils. We used simulation sequences with simulation coils to test the quality of image and advanced performance of the scanner. Codes and procedures were developed for semiautomatic image quality analysis. When using standard ACR sequences with a split head coil, image quality passed all ACR recommended criteria. The image intensity uniformity with a simulation RF coil decreased about 34% compared with the eight-channel diagnostic head coil, while the other six image quality parameters were acceptable. Those two image quality parameters could be improved to more than 85% by built-in intensity calibration methods. In the simulation sequences test, the contrast resolution was sensitive to the FOV and matrix settings. The geometric distortion of simulation sequences such as T1-weighted and T2-weighted images was well-controlled in the isocenter and 10 cm off-center within a range of ±1% (2 mm). We developed a semiautomatic image quality analysis method for quantitative evaluation of images and commissioning of an MRI simulator. The baseline performances of simulation RF coils and pulse sequences have been established for routine QA. © 2018 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Image Sharing Technologies and Reduction of Imaging Utilization: A Systematic Review and Meta-analysis.

PubMed

Vest, Joshua R; Jung, Hye-Young; Ostrovsky, Aaron; Das, Lala Tanmoy; McGinty, Geraldine B

2015-12-01

Image sharing technologies may reduce unneeded imaging by improving provider access to imaging information. A systematic review and meta-analysis were conducted to summarize the impact of image sharing technologies on patient imaging utilization. Quantitative evaluations of the effects of PACS, regional image exchange networks, interoperable electronic heath records, tools for importing physical media, and health information exchange systems on utilization were identified through a systematic review of the published and gray English-language literature (2004-2014). Outcomes, standard effect sizes (ESs), settings, technology, populations, and risk of bias were abstracted from each study. The impact of image sharing technologies was summarized with random-effects meta-analysis and meta-regression models. A total of 17 articles were included in the review, with a total of 42 different studies. Image sharing technology was associated with a significant decrease in repeat imaging (pooled effect size [ES] = -0.17; 95% confidence interval [CI] = [-0.25, -0.09]; P < .001). However, image sharing technology was associated with a significant increase in any imaging utilization (pooled ES = 0.20; 95% CI = [0.07, 0.32]; P = .002). For all outcomes combined, image sharing technology was not associated with utilization. Most studies were at risk for bias. Image sharing technology was associated with reductions in repeat and unnecessary imaging, in both the overall literature and the most-rigorous studies. Stronger evidence is needed to further explore the role of specific technologies and their potential impact on various modalities, patient populations, and settings. Copyright © 2015 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Image Sharing Technologies and Reduction of Imaging Utilization: A Systematic Review and Meta-analysis

PubMed Central

Vest, Joshua R.; Jung, Hye-Young; Ostrovsky, Aaron; Das, Lala Tanmoy; McGinty, Geraldine B.

2016-01-01

Introduction Image sharing technologies may reduce unneeded imaging by improving provider access to imaging information. A systematic review and meta-analysis were conducted to summarize the impact of image sharing technologies on patient imaging utilization. Methods Quantitative evaluations of the effects of PACS, regional image exchange networks, interoperable electronic heath records, tools for importing physical media, and health information exchange systems on utilization were identified through a systematic review of the published and gray English-language literature (2004–2014). Outcomes, standard effect sizes (ESs), settings, technology, populations, and risk of bias were abstracted from each study. The impact of image sharing technologies was summarized with random-effects meta-analysis and meta-regression models. Results A total of 17 articles were included in the review, with a total of 42 different studies. Image sharing technology was associated with a significant decrease in repeat imaging (pooled effect size [ES] = −0.17; 95% confidence interval [CI] = [−0.25, −0.09]; P < .001). However, image sharing technology was associated with a significant increase in any imaging utilization (pooled ES = 0.20; 95% CI = [0.07, 0.32]; P = .002). For all outcomes combined, image sharing technology was not associated with utilization. Most studies were at risk for bias. Conclusions Image sharing technology was associated with reductions in repeat and unnecessary imaging, in both the overall literature and the most-rigorous studies. Stronger evidence is needed to further explore the role of specific technologies and their potential impact on various modalities, patient populations, and settings. PMID:26614882

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

Bogunovic, Hrvoje; Pozo, Jose Maria; Villa-Uriol, Maria Cruz

Purpose: To evaluate the suitability of an improved version of an automatic segmentation method based on geodesic active regions (GAR) for segmenting cerebral vasculature with aneurysms from 3D x-ray reconstruction angiography (3DRA) and time of flight magnetic resonance angiography (TOF-MRA) images available in the clinical routine. Methods: Three aspects of the GAR method have been improved: execution time, robustness to variability in imaging protocols, and robustness to variability in image spatial resolutions. The improved GAR was retrospectively evaluated on images from patients containing intracranial aneurysms in the area of the Circle of Willis and imaged with two modalities: 3DRA andmore » TOF-MRA. Images were obtained from two clinical centers, each using different imaging equipment. Evaluation included qualitative and quantitative analyses of the segmentation results on 20 images from 10 patients. The gold standard was built from 660 cross-sections (33 per image) of vessels and aneurysms, manually measured by interventional neuroradiologists. GAR has also been compared to an interactive segmentation method: isointensity surface extraction (ISE). In addition, since patients had been imaged with the two modalities, we performed an intermodality agreement analysis with respect to both the manual measurements and each of the two segmentation methods. Results: Both GAR and ISE differed from the gold standard within acceptable limits compared to the imaging resolution. GAR (ISE) had an average accuracy of 0.20 (0.24) mm for 3DRA and 0.27 (0.30) mm for TOF-MRA, and had a repeatability of 0.05 (0.20) mm. Compared to ISE, GAR had a lower qualitative error in the vessel region and a lower quantitative error in the aneurysm region. The repeatability of GAR was superior to manual measurements and ISE. The intermodality agreement was similar between GAR and the manual measurements. Conclusions: The improved GAR method outperformed ISE qualitatively as well as quantitatively and is suitable for segmenting 3DRA and TOF-MRA images from clinical routine.« less

SU-E-J-218: Evaluation of CT Images Created Using a New Metal Artifact Reduction Reconstruction Algorithm for Radiation Therapy Treatment Planning

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

Niemkiewicz, J; Palmiotti, A; Miner, M

2014-06-01

Purpose: Metal in patients creates streak artifacts in CT images. When used for radiation treatment planning, these artifacts make it difficult to identify internal structures and affects radiation dose calculations, which depend on HU numbers for inhomogeneity correction. This work quantitatively evaluates a new metal artifact reduction (MAR) CT image reconstruction algorithm (GE Healthcare CT-0521-04.13-EN-US DOC1381483) when metal is present. Methods: A Gammex Model 467 Tissue Characterization phantom was used. CT images were taken of this phantom on a GE Optima580RT CT scanner with and without steel and titanium plugs using both the standard and MAR reconstruction algorithms. HU valuesmore » were compared pixel by pixel to determine if the MAR algorithm altered the HUs of normal tissues when no metal is present, and to evaluate the effect of using the MAR algorithm when metal is present. Also, CT images of patients with internal metal objects using standard and MAR reconstruction algorithms were compared. Results: Comparing the standard and MAR reconstructed images of the phantom without metal, 95.0% of pixels were within ±35 HU and 98.0% of pixels were within ±85 HU. Also, the MAR reconstruction algorithm showed significant improvement in maintaining HUs of non-metallic regions in the images taken of the phantom with metal. HU Gamma analysis (2%, 2mm) of metal vs. non-metal phantom imaging using standard reconstruction resulted in an 84.8% pass rate compared to 96.6% for the MAR reconstructed images. CT images of patients with metal show significant artifact reduction when reconstructed with the MAR algorithm. Conclusion: CT imaging using the MAR reconstruction algorithm provides improved visualization of internal anatomy and more accurate HUs when metal is present compared to the standard reconstruction algorithm. MAR reconstructed CT images provide qualitative and quantitative improvements over current reconstruction algorithms, thus improving radiation treatment planning accuracy.« less

A New Feedback-Based Method for Parameter Adaptation in Image Processing Routines.

PubMed

Khan, Arif Ul Maula; Mikut, Ralf; Reischl, Markus

2016-01-01

The parametrization of automatic image processing routines is time-consuming if a lot of image processing parameters are involved. An expert can tune parameters sequentially to get desired results. This may not be productive for applications with difficult image analysis tasks, e.g. when high noise and shading levels in an image are present or images vary in their characteristics due to different acquisition conditions. Parameters are required to be tuned simultaneously. We propose a framework to improve standard image segmentation methods by using feedback-based automatic parameter adaptation. Moreover, we compare algorithms by implementing them in a feedforward fashion and then adapting their parameters. This comparison is proposed to be evaluated by a benchmark data set that contains challenging image distortions in an increasing fashion. This promptly enables us to compare different standard image segmentation algorithms in a feedback vs. feedforward implementation by evaluating their segmentation quality and robustness. We also propose an efficient way of performing automatic image analysis when only abstract ground truth is present. Such a framework evaluates robustness of different image processing pipelines using a graded data set. This is useful for both end-users and experts.

A New Feedback-Based Method for Parameter Adaptation in Image Processing Routines

PubMed Central

Mikut, Ralf; Reischl, Markus

2016-01-01

The parametrization of automatic image processing routines is time-consuming if a lot of image processing parameters are involved. An expert can tune parameters sequentially to get desired results. This may not be productive for applications with difficult image analysis tasks, e.g. when high noise and shading levels in an image are present or images vary in their characteristics due to different acquisition conditions. Parameters are required to be tuned simultaneously. We propose a framework to improve standard image segmentation methods by using feedback-based automatic parameter adaptation. Moreover, we compare algorithms by implementing them in a feedforward fashion and then adapting their parameters. This comparison is proposed to be evaluated by a benchmark data set that contains challenging image distortions in an increasing fashion. This promptly enables us to compare different standard image segmentation algorithms in a feedback vs. feedforward implementation by evaluating their segmentation quality and robustness. We also propose an efficient way of performing automatic image analysis when only abstract ground truth is present. Such a framework evaluates robustness of different image processing pipelines using a graded data set. This is useful for both end-users and experts. PMID:27764213

SU-E-I-33: Initial Evaluation of Model-Based Iterative CT Reconstruction Using Standard Image Quality Phantoms

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

Gingold, E; Dave, J

2014-06-01

Purpose: The purpose of this study was to compare a new model-based iterative reconstruction with existing reconstruction methods (filtered backprojection and basic iterative reconstruction) using quantitative analysis of standard image quality phantom images. Methods: An ACR accreditation phantom (Gammex 464) and a CATPHAN600 phantom were scanned using 3 routine clinical acquisition protocols (adult axial brain, adult abdomen, and pediatric abdomen) on a Philips iCT system. Each scan was acquired using default conditions and 75%, 50% and 25% dose levels. Images were reconstructed using standard filtered backprojection (FBP), conventional iterative reconstruction (iDose4) and a prototype model-based iterative reconstruction (IMR). Phantom measurementsmore » included CT number accuracy, contrast to noise ratio (CNR), modulation transfer function (MTF), low contrast detectability (LCD), and noise power spectrum (NPS). Results: The choice of reconstruction method had no effect on CT number accuracy, or MTF (p<0.01). The CNR of a 6 HU contrast target was improved by 1–67% with iDose4 relative to FBP, while IMR improved CNR by 145–367% across all protocols and dose levels. Within each scan protocol, the CNR improvement from IMR vs FBP showed a general trend of greater improvement at lower dose levels. NPS magnitude was greatest for FBP and lowest for IMR. The NPS of the IMR reconstruction showed a pronounced decrease with increasing spatial frequency, consistent with the unusual noise texture seen in IMR images. Conclusion: Iterative Model Reconstruction reduces noise and improves contrast-to-noise ratio without sacrificing spatial resolution in CT phantom images. This offers the possibility of radiation dose reduction and improved low contrast detectability compared with filtered backprojection or conventional iterative reconstruction.« less

Dynamic Transmit-Receive Beamforming by Spatial Matched Filtering for Ultrasound Imaging with Plane Wave Transmission.

PubMed

Chen, Yuling; Lou, Yang; Yen, Jesse

2017-07-01

During conventional ultrasound imaging, the need for multiple transmissions for one image and the time of flight for a desired imaging depth limit the frame rate of the system. Using a single plane wave pulse during each transmission followed by parallel receive processing allows for high frame rate imaging. However, image quality is degraded because of the lack of transmit focusing. Beamforming by spatial matched filtering (SMF) is a promising method which focuses ultrasonic energy using spatial filters constructed from the transmit-receive impulse response of the system. Studies by other researchers have shown that SMF beamforming can provide dynamic transmit-receive focusing throughout the field of view. In this paper, we apply SMF beamforming to plane wave transmissions (PWTs) to achieve both dynamic transmit-receive focusing at all imaging depths and high imaging frame rate (>5000 frames per second). We demonstrated the capability of the combined method (PWT + SMF) of achieving two-way focusing mathematically through analysis based on the narrowband Rayleigh-Sommerfeld diffraction theory. Moreover, the broadband performance of PWT + SMF was quantified in terms of lateral resolution and contrast from both computer simulations and experimental data. Results were compared between SMF beamforming and conventional delay-and-sum (DAS) beamforming in both simulations and experiments. At an imaging depth of 40 mm, simulation results showed a 29% lateral resolution improvement and a 160% contrast improvement with PWT + SMF. These improvements were 17% and 48% for experimental data with noise.

Introduction to the GEOBIA 2010 special issue: From pixels to geographic objects in remote sensing image analysis

NASA Astrophysics Data System (ADS)

Addink, Elisabeth A.; Van Coillie, Frieke M. B.; De Jong, Steven M.

2012-04-01

Traditional image analysis methods are mostly pixel-based and use the spectral differences of landscape elements at the Earth surface to classify these elements or to extract element properties from the Earth Observation image. Geographic object-based image analysis (GEOBIA) has received considerable attention over the past 15 years for analyzing and interpreting remote sensing imagery. In contrast to traditional image analysis, GEOBIA works more like the human eye-brain combination does. The latter uses the object's color (spectral information), size, texture, shape and occurrence to other image objects to interpret and analyze what we see. GEOBIA starts by segmenting the image grouping together pixels into objects and next uses a wide range of object properties to classify the objects or to extract object's properties from the image. Significant advances and improvements in image analysis and interpretation are made thanks to GEOBIA. In June 2010 the third conference on GEOBIA took place at the Ghent University after successful previous meetings in Calgary (2008) and Salzburg (2006). This special issue presents a selection of the 2010 conference papers that are worked out as full research papers for JAG. The papers cover GEOBIA applications as well as innovative methods and techniques. The topics range from vegetation mapping, forest parameter estimation, tree crown identification, urban mapping, land cover change, feature selection methods and the effects of image compression on segmentation. From the original 94 conference papers, 26 full research manuscripts were submitted; nine papers were selected and are presented in this special issue. Selection was done on the basis of quality and topic of the studies. The next GEOBIA conference will take place in Rio de Janeiro from 7 to 9 May 2012 where we hope to welcome even more scientists working in the field of GEOBIA.

Hyperspectral imaging spectro radiometer improves radiometric accuracy

NASA Astrophysics Data System (ADS)

Prel, Florent; Moreau, Louis; Bouchard, Robert; Bullis, Ritchie D.; Roy, Claude; Vallières, Christian; Levesque, Luc

2013-06-01

Reliable and accurate infrared characterization is necessary to measure the specific spectral signatures of aircrafts and associated infrared counter-measures protections (i.e. flares). Infrared characterization is essential to improve counter measures efficiency, improve friend-foe identification and reduce the risk of friendly fire. Typical infrared characterization measurement setups include a variety of panchromatic cameras and spectroradiometers. Each instrument brings essential information; cameras measure the spatial distribution of targets and spectroradiometers provide the spectral distribution of the emitted energy. However, the combination of separate instruments brings out possible radiometric errors and uncertainties that can be reduced with Hyperspectral imagers. These instruments combine both spectral and spatial information into the same data. These instruments measure both the spectral and spatial distribution of the energy at the same time ensuring the temporal and spatial cohesion of collected information. This paper presents a quantitative analysis of the main contributors of radiometric uncertainties and shows how a hyperspectral imager can reduce these uncertainties.

Improving spatial perception in 5-yr.-old Spanish children.

PubMed

Jiménez, Andrés Canto; Sicilia, Antonio Oña; Vera, Juan Granda

2007-06-01

Assimilation of distance perception was studied in 70 Spanish primary school children. This assimilation involves the generation of projective images which are acquired through two mechanisms. One mechanism is spatial perception, wherein perceptual processes develop ensuring successful immersion in space and the acquisition of visual cues which a person may use to interpret images seen in the distance. The other mechanism is movement through space so that these images are produced. The present study evaluated the influence on improvements in spatial perception of using increasingly larger spaces for training sessions within a motor skills program. Visual parameters were measured in relation to the capture and tracking of moving objects or ocular motility and speed of detection or visual reaction time. Analysis showed that for the group trained in increasingly larger spaces, ocular motility and visual reaction time were significantly improved during. different phases of the program.

Representation of scientific methodology in secondary science textbooks

NASA Astrophysics Data System (ADS)

Binns, Ian C.

The purpose of this investigation was to assess the representation of scientific methodology in secondary science textbooks. More specifically, this study looked at how textbooks introduced scientific methodology and to what degree the examples from the rest of the textbook, the investigations, and the images were consistent with the text's description of scientific methodology, if at all. The sample included eight secondary science textbooks from two publishers, McGraw-Hill/Glencoe and Harcourt/Holt, Rinehart & Winston. Data consisted of all student text and teacher text that referred to scientific methodology. Second, all investigations in the textbooks were analyzed. Finally, any images that depicted scientists working were also collected and analyzed. The text analysis and activity analysis used the ethnographic content analysis approach developed by Altheide (1996). The rubrics used for the text analysis and activity analysis were initially guided by the Benchmarks (AAAS, 1993), the NSES (NRC, 1996), and the nature of science literature. Preliminary analyses helped to refine each of the rubrics and grounded them in the data. Image analysis used stereotypes identified in the DAST literature. Findings indicated that all eight textbooks presented mixed views of scientific methodology in their initial descriptions. Five textbooks placed more emphasis on the traditional view and three placed more emphasis on the broad view. Results also revealed that the initial descriptions, examples, investigations, and images all emphasized the broad view for Glencoe Biology and the traditional view for Chemistry: Matter and Change. The initial descriptions, examples, investigations, and images in the other six textbooks were not consistent. Overall, the textbook with the most appropriate depiction of scientific methodology was Glencoe Biology and the textbook with the least appropriate depiction of scientific methodology was Physics: Principles and Problems. These findings suggest that compared to earlier investigations, textbooks have begun to improve in how they represent scientific methodology. However, there is still much room for improvement. Future research needs to consider how textbooks impact teachers' and students' understandings of scientific methodology.

Analytical validation of quantitative immunohistochemical assays of tumor infiltrating lymphocyte biomarkers.

PubMed

Singh, U; Cui, Y; Dimaano, N; Mehta, S; Pruitt, S K; Yearley, J; Laterza, O F; Juco, J W; Dogdas, B

2018-06-04

Tumor infiltrating lymphocytes (TIL), especially T-cells, have both prognostic and therapeutic applications. The presence of CD8+ effector T-cells and the ratio of CD8+ cells to FOXP3+ regulatory T-cells have been used as biomarkers of disease prognosis to predict response to various immunotherapies. Blocking the interaction between inhibitory receptors on T-cells and their ligands with therapeutic antibodies including atezolizumab, nivolumab, pembrolizumab and tremelimumab increases the immune response against cancer cells and has shown significant improvement in clinical benefits and survival in several different tumor types. The improved clinical outcome is presumed to be associated with a higher tumor infiltration; therefore, it is thought that more accurate methods for measuring the amount of TIL could assist prognosis and predict treatment response. We have developed and validated quantitative immunohistochemistry (IHC) assays for CD3, CD8 and FOXP3 for immunophenotyping T-lymphocytes in tumor tissue. Various types of formalin fixed, paraffin embedded (FFPE) tumor tissues were immunolabeled with anti-CD3, anti-CD8 and anti-FOXP3 antibodies using an IHC autostainer. The tumor area of stained tissues, including the invasive margin of the tumor, was scored by a pathologist (visual scoring) and by computer-based quantitative image analysis. Two image analysis scores were obtained for the staining of each biomarker: the percent positive cells in the tumor area and positive cells/mm 2 tumor area. Comparison of visual vs. image analysis scoring methods using regression analysis showed high correlation and indicated that quantitative image analysis can be used to score the number of positive cells in IHC stained slides. To demonstrate that the IHC assays produce consistent results in normal daily testing, we evaluated the specificity, sensitivity and reproducibility of the IHC assays using both visual and image analysis scoring methods. We found that CD3, CD8 and FOXP3 IHC assays met the fit-for-purpose analytical acceptance validation criteria and that they can be used to support clinical studies.

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.

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

Asymmetry and irregularity border as discrimination factor between melanocytic lesions

NASA Astrophysics Data System (ADS)

Sbrissa, David; Pratavieira, Sebastião.; Salvio, Ana Gabriela; Kurachi, Cristina; Bagnato, Vanderlei Salvadori; Costa, Luciano Da Fontoura; Travieso, Gonzalo

2015-06-01

Image processing tools have been widely used in systems supporting medical diagnosis. The use of mobile devices for the diagnosis of melanoma can assist doctors and improve their diagnosis of a melanocytic lesion. This study proposes a method of image analysis for melanoma discrimination from other types of melanocytic lesions, such as regular and atypical nevi. The process is based on extracting features related with asymmetry and border irregularity. It were collected 104 images, from medical database of two years. The images were obtained with standard digital cameras without lighting and scale control. Metrics relating to the characteristics of shape, asymmetry and curvature of the contour were extracted from segmented images. Linear Discriminant Analysis was performed for dimensionality reduction and data visualization. Segmentation results showed good efficiency in the process, with approximately 88:5% accuracy. Validation results presents sensibility and specificity 85% and 70% for melanoma detection, respectively.

Fuzzy-based propagation of prior knowledge to improve large-scale image analysis pipelines

PubMed Central

Mikut, Ralf

2017-01-01

Many automatically analyzable scientific questions are well-posed and a variety of information about expected outcomes is available a priori. Although often neglected, this prior knowledge can be systematically exploited to make automated analysis operations sensitive to a desired phenomenon or to evaluate extracted content with respect to this prior knowledge. For instance, the performance of processing operators can be greatly enhanced by a more focused detection strategy and by direct information about the ambiguity inherent in the extracted data. We present a new concept that increases the result quality awareness of image analysis operators by estimating and distributing the degree of uncertainty involved in their output based on prior knowledge. This allows the use of simple processing operators that are suitable for analyzing large-scale spatiotemporal (3D+t) microscopy images without compromising result quality. On the foundation of fuzzy set theory, we transform available prior knowledge into a mathematical representation and extensively use it to enhance the result quality of various processing operators. These concepts are illustrated on a typical bioimage analysis pipeline comprised of seed point detection, segmentation, multiview fusion and tracking. The functionality of the proposed approach is further validated on a comprehensive simulated 3D+t benchmark data set that mimics embryonic development and on large-scale light-sheet microscopy data of a zebrafish embryo. The general concept introduced in this contribution represents a new approach to efficiently exploit prior knowledge to improve the result quality of image analysis pipelines. The generality of the concept makes it applicable to practically any field with processing strategies that are arranged as linear pipelines. The automated analysis of terabyte-scale microscopy data will especially benefit from sophisticated and efficient algorithms that enable a quantitative and fast readout. PMID:29095927

Location precision analysis of stereo thermal anti-sniper detection system

NASA Astrophysics Data System (ADS)

He, Yuqing; Lu, Ya; Zhang, Xiaoyan; Jin, Weiqi

2012-06-01

Anti-sniper detection devices are the urgent requirement in modern warfare. The precision of the anti-sniper detection system is especially important. This paper discusses the location precision analysis of the anti-sniper detection system based on the dual-thermal imaging system. It mainly discusses the following two aspects which produce the error: the digital quantitative effects of the camera; effect of estimating the coordinate of bullet trajectory according to the infrared images in the process of image matching. The formula of the error analysis is deduced according to the method of stereovision model and digital quantitative effects of the camera. From this, we can get the relationship of the detecting accuracy corresponding to the system's parameters. The analysis in this paper provides the theory basis for the error compensation algorithms which are put forward to improve the accuracy of 3D reconstruction of the bullet trajectory in the anti-sniper detection devices.

The performance improvement of automatic classification among obstructive lung diseases on the basis of the features of shape analysis, in addition to texture analysis at HRCT

NASA Astrophysics Data System (ADS)

Lee, Youngjoo; Kim, Namkug; Seo, Joon Beom; Lee, JuneGoo; Kang, Suk Ho

2007-03-01

In this paper, we proposed novel shape features to improve classification performance of differentiating obstructive lung diseases, based on HRCT (High Resolution Computerized Tomography) images. The images were selected from HRCT images, obtained from 82 subjects. For each image, two experienced radiologists selected rectangular ROIs with various sizes (16x16, 32x32, and 64x64 pixels), representing each disease or normal lung parenchyma. Besides thirteen textural features, we employed additional seven shape features; cluster shape features, and Top-hat transform features. To evaluate the contribution of shape features for differentiation of obstructive lung diseases, several experiments were conducted with two different types of classifiers and various ROI sizes. For automated classification, the Bayesian classifier and support vector machine (SVM) were implemented. To assess the performance and cross-validation of the system, 5-folding method was used. In comparison to employing only textural features, adding shape features yields significant enhancement of overall sensitivity(5.9, 5.4, 4.4% in the Bayesian and 9.0, 7.3, 5.3% in the SVM), in the order of ROI size 16x16, 32x32, 64x64 pixels, respectively (t-test, p<0.01). Moreover, this enhancement was largely due to the improvement on class-specific sensitivity of mild centrilobular emphysema and bronchiolitis obliterans which are most hard to differentiate for radiologists. According to these experimental results, adding shape features to conventional texture features is much useful to improve classification performance of obstructive lung diseases in both Bayesian and SVM classifiers.

Do pre-trained deep learning models improve computer-aided classification of digital mammograms?

NASA Astrophysics Data System (ADS)

Aboutalib, Sarah S.; Mohamed, Aly A.; Zuley, Margarita L.; Berg, Wendie A.; Luo, Yahong; Wu, Shandong

2018-02-01

Digital mammography screening is an important exam for the early detection of breast cancer and reduction in mortality. False positives leading to high recall rates, however, results in unnecessary negative consequences to patients and health care systems. In order to better aid radiologists, computer-aided tools can be utilized to improve distinction between image classifications and thus potentially reduce false recalls. The emergence of deep learning has shown promising results in the area of biomedical imaging data analysis. This study aimed to investigate deep learning and transfer learning methods that can improve digital mammography classification performance. In particular, we evaluated the effect of pre-training deep learning models with other imaging datasets in order to boost classification performance on a digital mammography dataset. Two types of datasets were used for pre-training: (1) a digitized film mammography dataset, and (2) a very large non-medical imaging dataset. By using either of these datasets to pre-train the network initially, and then fine-tuning with the digital mammography dataset, we found an increase in overall classification performance in comparison to a model without pre-training, with the very large non-medical dataset performing the best in improving the classification accuracy.

Impact of time-of-flight PET on quantification errors in MR imaging-based attenuation correction.

PubMed

Mehranian, Abolfazl; Zaidi, Habib

2015-04-01

Time-of-flight (TOF) PET/MR imaging is an emerging imaging technology with great capabilities offered by TOF to improve image quality and lesion detectability. We assessed, for the first time, the impact of TOF image reconstruction on PET quantification errors induced by MR imaging-based attenuation correction (MRAC) using simulation and clinical PET/CT studies. Standard 4-class attenuation maps were derived by segmentation of CT images of 27 patients undergoing PET/CT examinations into background air, lung, soft-tissue, and fat tissue classes, followed by the assignment of predefined attenuation coefficients to each class. For each patient, 4 PET images were reconstructed: non-TOF and TOF both corrected for attenuation using reference CT-based attenuation correction and the resulting 4-class MRAC maps. The relative errors between non-TOF and TOF MRAC reconstructions were compared with their reference CT-based attenuation correction reconstructions. The bias was locally and globally evaluated using volumes of interest (VOIs) defined on lesions and normal tissues and CT-derived tissue classes containing all voxels in a given tissue, respectively. The impact of TOF on reducing the errors induced by metal-susceptibility and respiratory-phase mismatch artifacts was also evaluated using clinical and simulation studies. Our results show that TOF PET can remarkably reduce attenuation correction artifacts and quantification errors in the lungs and bone tissues. Using classwise analysis, it was found that the non-TOF MRAC method results in an error of -3.4% ± 11.5% in the lungs and -21.8% ± 2.9% in bones, whereas its TOF counterpart reduced the errors to -2.9% ± 7.1% and -15.3% ± 2.3%, respectively. The VOI-based analysis revealed that the non-TOF and TOF methods resulted in an average overestimation of 7.5% and 3.9% in or near lung lesions (n = 23) and underestimation of less than 5% for soft tissue and in or near bone lesions (n = 91). Simulation results showed that as TOF resolution improves, artifacts and quantification errors are substantially reduced. TOF PET substantially reduces artifacts and improves significantly the quantitative accuracy of standard MRAC methods. Therefore, MRAC should be less of a concern on future TOF PET/MR scanners with improved timing resolution. © 2015 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

Combined Magnetic Resonance Imaging and Spectroscopic Imaging Approach to Molecular Imaging of Prostate Cancer

PubMed Central

Kurhanewicz, John; Swanson, Mark G.; Nelson, Sarah J.; Vigneron, Daniel B.

2005-01-01

Magnetic resonance spectroscopic imaging (MRSI) provides a noninvasive method of detecting small molecular markers (historically the metabolites choline and citrate) within the cytosol and extracellular spaces of the prostate, and is performed in conjunction with high-resolution anatomic imaging. Recent studies in pre-prostatectomy patients have indicated that the metabolic information provided by MRSI combined with the anatomical information provided by MRI can significantly improve the assessment of cancer location and extent within the prostate, extracapsular spread, and cancer aggressiveness. Additionally, pre- and post-therapy studies have demonstrated the potential of MRI/MRSI to provide a direct measure of the presence and spatial extent of prostate cancer after therapy, a measure of the time course of response, and information concerning the mechanism of therapeutic response. In addition to detecting metabolic biomarkers of disease behavior and therapeutic response, MRI/MRSI guidance can improve tissue selection for ex vivo analysis. High-resolution magic angle spinning (1H HR-MAS) spectroscopy provides a full chemical analysis of MRI/MRSI-targeted tissues prior to pathologic and immunohistochemical analyses of the same tissue. Preliminary 1H HR-MAS spectroscopy studies have already identified unique spectral patterns for healthy glandular and stromal tissues and prostate cancer, determined the composition of the composite in vivo choline peak, and identified the polyamine spermine as a new metabolic marker of prostate cancer. The addition of imaging sequences that provide other functional information within the same exam (dynamic contrast uptake imaging and diffusion-weighted imaging) have also demonstrated the potential to further increase the accuracy of prostate cancer detection and characterization. PMID:12353259

Corrections on energy spectrum and scatterings for fast neutron radiography at NECTAR facility

NASA Astrophysics Data System (ADS)

Liu, Shu-Quan; Bücherl, Thomas; Li, Hang; Zou, Yu-Bin; Lu, Yuan-Rong; Guo, Zhi-Yu

2013-11-01

Distortions caused by the neutron spectrum and scattered neutrons are major problems in fast neutron radiography and should be considered for improving the image quality. This paper puts emphasis on the removal of these image distortions and deviations for fast neutron radiography performed at the NECTAR facility of the research reactor FRM- II in Technische Universität München (TUM), Germany. The NECTAR energy spectrum is analyzed and established to modify the influence caused by the neutron spectrum, and the Point Scattered Function (PScF) simulated by the Monte-Carlo program MCNPX is used to evaluate scattering effects from the object and improve image quality. Good analysis results prove the sound effects of the above two corrections.

Improved deconvolution of very weak confocal signals.

PubMed

Day, Kasey J; La Rivière, Patrick J; Chandler, Talon; Bindokas, Vytas P; Ferrier, Nicola J; Glick, Benjamin S

2017-01-01

Deconvolution is typically used to sharpen fluorescence images, but when the signal-to-noise ratio is low, the primary benefit is reduced noise and a smoother appearance of the fluorescent structures. 3D time-lapse (4D) confocal image sets can be improved by deconvolution. However, when the confocal signals are very weak, the popular Huygens deconvolution software erases fluorescent structures that are clearly visible in the raw data. We find that this problem can be avoided by prefiltering the optical sections with a Gaussian blur. Analysis of real and simulated data indicates that the Gaussian blur prefilter preserves meaningful signals while enabling removal of background noise. This approach is very simple, and it allows Huygens to be used with 4D imaging conditions that minimize photodamage.

Frequency analysis of gaze points with CT colonography interpretation using eye gaze tracking system

NASA Astrophysics Data System (ADS)

Tsutsumi, Shoko; Tamashiro, Wataru; Sato, Mitsuru; Okajima, Mika; Ogura, Toshihiro; Doi, Kunio

2017-03-01

It is important to investigate eye tracking gaze points of experts, in order to assist trainees in understanding of image interpretation process. We investigated gaze points of CT colonography (CTC) interpretation process, and analyzed the difference in gaze points between experts and trainees. In this study, we attempted to understand how trainees can be improved to a level achieved by experts in viewing of CTC. We used an eye gaze point sensing system, Gazefineder (JVCKENWOOD Corporation, Tokyo, Japan), which can detect pupil point and corneal reflection point by the dark pupil eye tracking. This system can provide gaze points images and excel file data. The subjects are radiological technologists who are experienced, and inexperienced in reading CTC. We performed observer studies in reading virtual pathology images and examined observer's image interpretation process using gaze points data. Furthermore, we examined eye tracking frequency analysis by using the Fast Fourier Transform (FFT). We were able to understand the difference in gaze points between experts and trainees by use of the frequency analysis. The result of the trainee had a large amount of both high-frequency components and low-frequency components. In contrast, both components by the expert were relatively low. Regarding the amount of eye movement in every 0.02 second we found that the expert tended to interpret images slowly and calmly. On the other hand, the trainee was moving eyes quickly and also looking for wide areas. We can assess the difference in the gaze points on CTC between experts and trainees by use of the eye gaze point sensing system and based on the frequency analysis. The potential improvements in CTC interpretation for trainees can be evaluated by using gaze points data.

LittleQuickWarp: an ultrafast image warping tool.

PubMed

Qu, Lei; Peng, Hanchuan

2015-02-01

Warping images into a standard coordinate space is critical for many image computing related tasks. However, for multi-dimensional and high-resolution images, an accurate warping operation itself is often very expensive in terms of computer memory and computational time. For high-throughput image analysis studies such as brain mapping projects, it is desirable to have high performance image warping tools that are compatible with common image analysis pipelines. In this article, we present LittleQuickWarp, a swift and memory efficient tool that boosts 3D image warping performance dramatically and at the same time has high warping quality similar to the widely used thin plate spline (TPS) warping. Compared to the TPS, LittleQuickWarp can improve the warping speed 2-5 times and reduce the memory consumption 6-20 times. We have implemented LittleQuickWarp as an Open Source plug-in program on top of the Vaa3D system (http://vaa3d.org). The source code and a brief tutorial can be found in the Vaa3D plugin source code repository. Copyright © 2014 Elsevier Inc. All rights reserved.

An Efficient Computational Framework for the Analysis of Whole Slide Images: Application to Follicular Lymphoma Immunohistochemistry

PubMed Central

Samsi, Siddharth; Krishnamurthy, Ashok K.; Gurcan, Metin N.

2012-01-01

Follicular Lymphoma (FL) is one of the most common non-Hodgkin Lymphoma in the United States. Diagnosis and grading of FL is based on the review of histopathological tissue sections under a microscope and is influenced by human factors such as fatigue and reader bias. Computer-aided image analysis tools can help improve the accuracy of diagnosis and grading and act as another tool at the pathologist’s disposal. Our group has been developing algorithms for identifying follicles in immunohistochemical images. These algorithms have been tested and validated on small images extracted from whole slide images. However, the use of these algorithms for analyzing the entire whole slide image requires significant changes to the processing methodology since the images are relatively large (on the order of 100k × 100k pixels). In this paper we discuss the challenges involved in analyzing whole slide images and propose potential computational methodologies for addressing these challenges. We discuss the use of parallel computing tools on commodity clusters and compare performance of the serial and parallel implementations of our approach. PMID:22962572

Comparing features sets for content-based image retrieval in a medical-case database

NASA Astrophysics Data System (ADS)

Muller, Henning; Rosset, Antoine; Vallee, Jean-Paul; Geissbuhler, Antoine

2004-04-01

Content-based image retrieval systems (CBIRSs) have frequently been proposed for the use in medical image databases and PACS. Still, only few systems were developed and used in a real clinical environment. It rather seems that medical professionals define their needs and computer scientists develop systems based on data sets they receive with little or no interaction between the two groups. A first study on the diagnostic use of medical image retrieval also shows an improvement in diagnostics when using CBIRSs which underlines the potential importance of this technique. This article explains the use of an open source image retrieval system (GIFT - GNU Image Finding Tool) for the retrieval of medical images in the medical case database system CasImage that is used in daily, clinical routine in the university hospitals of Geneva. Although the base system of GIFT shows an unsatisfactory performance, already little changes in the feature space show to significantly improve the retrieval results. The performance of variations in feature space with respect to color (gray level) quantizations and changes in texture analysis (Gabor filters) is compared. Whereas stock photography relies mainly on colors for retrieval, medical images need a large number of gray levels for successful retrieval, especially when executing feedback queries. The results also show that a too fine granularity in the gray levels lowers the retrieval quality, especially with single-image queries. For the evaluation of the retrieval peformance, a subset of the entire case database of more than 40,000 images is taken with a total of 3752 images. Ground truth was generated by a user who defined the expected query result of a perfect system by selecting images relevant to a given query image. The results show that a smaller number of gray levels (32 - 64) leads to a better retrieval performance, especially when using relevance feedback. The use of more scales and directions for the Gabor filters in the texture analysis also leads to improved results but response time is going up equally due to the larger feature space. CBIRSs can be of great use in managing large medical image databases. They allow to find images that might otherwise be lost for research and publications. They also give students students the possibility to navigate within large image repositories. In the future, CBIR might also become more important in case-based reasoning and evidence-based medicine to support the diagnostics because first studies show good results.

Objective definition of rosette shape variation using a combined computer vision and data mining approach.

PubMed

Camargo, Anyela; Papadopoulou, Dimitra; Spyropoulou, Zoi; Vlachonasios, Konstantinos; Doonan, John H; Gay, Alan P

2014-01-01

Computer-vision based measurements of phenotypic variation have implications for crop improvement and food security because they are intrinsically objective. It should be possible therefore to use such approaches to select robust genotypes. However, plants are morphologically complex and identification of meaningful traits from automatically acquired image data is not straightforward. Bespoke algorithms can be designed to capture and/or quantitate specific features but this approach is inflexible and is not generally applicable to a wide range of traits. In this paper, we have used industry-standard computer vision techniques to extract a wide range of features from images of genetically diverse Arabidopsis rosettes growing under non-stimulated conditions, and then used statistical analysis to identify those features that provide good discrimination between ecotypes. This analysis indicates that almost all the observed shape variation can be described by 5 principal components. We describe an easily implemented pipeline including image segmentation, feature extraction and statistical analysis. This pipeline provides a cost-effective and inherently scalable method to parameterise and analyse variation in rosette shape. The acquisition of images does not require any specialised equipment and the computer routines for image processing and data analysis have been implemented using open source software. Source code for data analysis is written using the R package. The equations to calculate image descriptors have been also provided.

Comparison among Reconstruction Algorithms for Quantitative Analysis of 11C-Acetate Cardiac PET Imaging.

PubMed

Shi, Ximin; Li, Nan; Ding, Haiyan; Dang, Yonghong; Hu, Guilan; Liu, Shuai; Cui, Jie; Zhang, Yue; Li, Fang; Zhang, Hui; Huo, Li

2018-01-01

Kinetic modeling of dynamic 11 C-acetate PET imaging provides quantitative information for myocardium assessment. The quality and quantitation of PET images are known to be dependent on PET reconstruction methods. This study aims to investigate the impacts of reconstruction algorithms on the quantitative analysis of dynamic 11 C-acetate cardiac PET imaging. Suspected alcoholic cardiomyopathy patients ( N = 24) underwent 11 C-acetate dynamic PET imaging after low dose CT scan. PET images were reconstructed using four algorithms: filtered backprojection (FBP), ordered subsets expectation maximization (OSEM), OSEM with time-of-flight (TOF), and OSEM with both time-of-flight and point-spread-function (TPSF). Standardized uptake values (SUVs) at different time points were compared among images reconstructed using the four algorithms. Time-activity curves (TACs) in myocardium and blood pools of ventricles were generated from the dynamic image series. Kinetic parameters K 1 and k 2 were derived using a 1-tissue-compartment model for kinetic modeling of cardiac flow from 11 C-acetate PET images. Significant image quality improvement was found in the images reconstructed using iterative OSEM-type algorithms (OSME, TOF, and TPSF) compared with FBP. However, no statistical differences in SUVs were observed among the four reconstruction methods at the selected time points. Kinetic parameters K 1 and k 2 also exhibited no statistical difference among the four reconstruction algorithms in terms of mean value and standard deviation. However, for the correlation analysis, OSEM reconstruction presented relatively higher residual in correlation with FBP reconstruction compared with TOF and TPSF reconstruction, and TOF and TPSF reconstruction were highly correlated with each other. All the tested reconstruction algorithms performed similarly for quantitative analysis of 11 C-acetate cardiac PET imaging. TOF and TPSF yielded highly consistent kinetic parameter results with superior image quality compared with FBP. OSEM was relatively less reliable. Both TOF and TPSF were recommended for cardiac 11 C-acetate kinetic analysis.

Lucky Imaging in Astronomy

NASA Astrophysics Data System (ADS)

Brandner, Wolfgang; Hormuth, Felix

Lucky Imaging improves the angular resolution of astronomical observations hampered by atmospheric turbulence ("seeing"). Unlike adaptive optics, Lucky Imaging is a passive observing technique with individual integration times comparable to the atmospheric coherence time. Thanks to the advent of essentially noise free "Electron multiplying CCD" detectors, Lucky Imaging saw a renewed interest in the past decade. It is now routinely used at a number of 2-5-m class telescopes, such as ESO's NTT. We review the history of Lucky Imaging, present the technical implementation, describe the data analysis philosophy, and show some recent results obtained with this technique. We also discuss the advantages and limitations of Lucky Imaging compared to other passive and active high angular resolution observing techniques.

Sub-band denoising and spline curve fitting method for hemodynamic measurement in perfusion MRI

NASA Astrophysics Data System (ADS)

Lin, Hong-Dun; Huang, Hsiao-Ling; Hsu, Yuan-Yu; Chen, Chi-Chen; Chen, Ing-Yi; Wu, Liang-Chi; Liu, Ren-Shyan; Lin, Kang-Ping

2003-05-01

In clinical research, non-invasive MR perfusion imaging is capable of investigating brain perfusion phenomenon via various hemodynamic measurements, such as cerebral blood volume (CBV), cerebral blood flow (CBF), and mean trasnit time (MTT). These hemodynamic parameters are useful in diagnosing brain disorders such as stroke, infarction and periinfarct ischemia by further semi-quantitative analysis. However, the accuracy of quantitative analysis is usually affected by poor signal-to-noise ratio image quality. In this paper, we propose a hemodynamic measurement method based upon sub-band denoising and spline curve fitting processes to improve image quality for better hemodynamic quantitative analysis results. Ten sets of perfusion MRI data and corresponding PET images were used to validate the performance. For quantitative comparison, we evaluate gray/white matter CBF ratio. As a result, the hemodynamic semi-quantitative analysis result of mean gray to white matter CBF ratio is 2.10 +/- 0.34. The evaluated ratio of brain tissues in perfusion MRI is comparable to PET technique is less than 1-% difference in average. Furthermore, the method features excellent noise reduction and boundary preserving in image processing, and short hemodynamic measurement time.

Advances in two photon scanning and scanless microscopy technologies for functional neural circuit imaging.

PubMed

Schultz, Simon R; Copeland, Caroline S; Foust, Amanda J; Quicke, Peter; Schuck, Renaud

2017-01-01

Recent years have seen substantial developments in technology for imaging neural circuits, raising the prospect of large scale imaging studies of neural populations involved in information processing, with the potential to lead to step changes in our understanding of brain function and dysfunction. In this article we will review some key recent advances: improved fluorophores for single cell resolution functional neuroimaging using a two photon microscope; improved approaches to the problem of scanning active circuits; and the prospect of scanless microscopes which overcome some of the bandwidth limitations of current imaging techniques. These advances in technology for experimental neuroscience have in themselves led to technical challenges, such as the need for the development of novel signal processing and data analysis tools in order to make the most of the new experimental tools. We review recent work in some active topics, such as region of interest segmentation algorithms capable of demixing overlapping signals, and new highly accurate algorithms for calcium transient detection. These advances motivate the development of new data analysis tools capable of dealing with spatial or spatiotemporal patterns of neural activity, that scale well with pattern size.

Advances in two photon scanning and scanless microscopy technologies for functional neural circuit imaging

PubMed Central

Schultz, Simon R.; Copeland, Caroline S.; Foust, Amanda J.; Quicke, Peter; Schuck, Renaud

2017-01-01

Recent years have seen substantial developments in technology for imaging neural circuits, raising the prospect of large scale imaging studies of neural populations involved in information processing, with the potential to lead to step changes in our understanding of brain function and dysfunction. In this article we will review some key recent advances: improved fluorophores for single cell resolution functional neuroimaging using a two photon microscope; improved approaches to the problem of scanning active circuits; and the prospect of scanless microscopes which overcome some of the bandwidth limitations of current imaging techniques. These advances in technology for experimental neuroscience have in themselves led to technical challenges, such as the need for the development of novel signal processing and data analysis tools in order to make the most of the new experimental tools. We review recent work in some active topics, such as region of interest segmentation algorithms capable of demixing overlapping signals, and new highly accurate algorithms for calcium transient detection. These advances motivate the development of new data analysis tools capable of dealing with spatial or spatiotemporal patterns of neural activity, that scale well with pattern size. PMID:28757657

Image quality affected by diffraction of aperture structure arrangement in transparent active-matrix organic light-emitting diode displays.

PubMed

Tsai, Yu-Hsiang; Huang, Mao-Hsiu; Jeng, Wei-de; Huang, Ting-Wei; Lo, Kuo-Lung; Ou-Yang, Mang

2015-10-01

Transparent display is one of the main technologies in next-generation displays, especially for augmented reality applications. An aperture structure is attached on each display pixel to partition them into transparent and black regions. However, diffraction blurs caused by the aperture structure typically degrade the transparent image when the light from a background object passes through finite aperture window. In this paper, the diffraction effect of an active-matrix organic light-emitting diode display (AMOLED) is studied. Several aperture structures have been proposed and implemented. Based on theoretical analysis and simulation, the appropriate aperture structure will effectively reduce the blur. The analysis data are also consistent with the experimental results. Compared with the various transparent aperture structure on AMOLED, diffraction width (zero energy position of diffraction pattern) of the optimize aperture structure can be reduced 63% and 31% in the x and y directions in CASE 3. Associated with a lenticular lens on the aperture structure, the improvement could reach to 77% and 54% of diffraction width in the x and y directions. Modulation transfer function and practical images are provided to evaluate the improvement of image blurs.

Hyperspectral image analysis for rapid and accurate discrimination of bacterial infections: A benchmark study.

PubMed

Arrigoni, Simone; Turra, Giovanni; Signoroni, Alberto

2017-09-01

With the rapid diffusion of Full Laboratory Automation systems, Clinical Microbiology is currently experiencing a new digital revolution. The ability to capture and process large amounts of visual data from microbiological specimen processing enables the definition of completely new objectives. These include the direct identification of pathogens growing on culturing plates, with expected improvements in rapid definition of the right treatment for patients affected by bacterial infections. In this framework, the synergies between light spectroscopy and image analysis, offered by hyperspectral imaging, are of prominent interest. This leads us to assess the feasibility of a reliable and rapid discrimination of pathogens through the classification of their spectral signatures extracted from hyperspectral image acquisitions of bacteria colonies growing on blood agar plates. We designed and implemented the whole data acquisition and processing pipeline and performed a comprehensive comparison among 40 combinations of different data preprocessing and classification techniques. High discrimination performance has been achieved also thanks to improved colony segmentation and spectral signature extraction. Experimental results reveal the high accuracy and suitability of the proposed approach, driving the selection of most suitable and scalable classification pipelines and stimulating clinical validations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Prostate cancer localization with endorectal MR imaging and MR spectroscopic imaging: effect of clinical data on reader accuracy.

PubMed

Dhingsa, Rajpal; Qayyum, Aliya; Coakley, Fergus V; Lu, Ying; Jones, Kirk D; Swanson, Mark G; Carroll, Peter R; Hricak, Hedvig; Kurhanewicz, John

2004-01-01

To determine the effect of digital rectal examination findings, sextant biopsy results, and prostate-specific antigen (PSA) levels on reader accuracy in the localization of prostate cancer with endorectal magnetic resonance (MR) imaging and MR spectroscopic imaging. This was a retrospective study of 37 patients (mean age, 57 years) with biopsy-proved prostate cancer. Transverse T1-weighted, transverse high-spatial-resolution, and coronal T2-weighted MR images and MR spectroscopic images were obtained. Two independent readers, unaware of clinical data, recorded the size and location of suspicious peripheral zone tumor nodules on a standardized diagram of the prostate. Readers also recorded their degree of diagnostic confidence for each nodule on a five-point scale. Both readers repeated this interpretation with knowledge of rectal examination findings, sextant biopsy results, and PSA level. Step-section histopathologic findings were the reference standard. Logistic regression analysis with generalized estimating equations was used to correlate tumor detection with clinical data, and alternative free-response receiver operating characteristic (AFROC) curve analysis was used to examine the overall effect of clinical data on all positive results. Fifty-one peripheral zone tumor nodules were identified at histopathologic evaluation. Logistic regression analysis showed awareness of clinical data significantly improved tumor detection rate (P <.02) from 15 to 19 nodules for reader 1 and from 13 to 19 nodules for reader 2 (27%-37% overall) by using both size and location criteria. AFROC analysis showed no significant change in overall reader performance because there was an associated increase in the number of false-positive findings with awareness of clinical data, from 11 to 21 for reader 1 and from 16 to 25 for reader 2. Awareness of clinical data significantly improves reader detection of prostate cancer nodules with endorectal MR imaging and MR spectroscopic imaging, but there is no overall change in reader accuracy, because of an associated increase in false-positive findings. A stricter definition of a true-positive result is associated with reduced sensitivity for prostate cancer nodule detection. Copyright RSNA, 2004

Excitation-resolved cone-beam x-ray luminescence tomography.

PubMed

Liu, Xin; Liao, Qimei; Wang, Hongkai; Yan, Zhuangzhi

2015-07-01

Cone-beam x-ray luminescence computed tomography (CB-XLCT), as an emerging imaging technique, plays an important role in in vivo small animal imaging studies. However, CB-XLCT suffers from low-spatial resolution due to the ill-posed nature of reconstruction. We improve the imaging performance of CB-XLCT by using a multiband excitation-resolved imaging scheme combined with principal component analysis. To evaluate the performance of the proposed method, the physical phantom experiment is performed with a custom-made XLCT/XCT imaging system. The experimental results validate the feasibility of the method, where two adjacent nanophosphors (with an edge-to-edge distance of 2.4 mm) can be located.

Robust boundary detection of left ventricles on ultrasound images using ASM-level set method.

PubMed

Zhang, Yaonan; Gao, Yuan; Li, Hong; Teng, Yueyang; Kang, Yan

2015-01-01

Level set method has been widely used in medical image analysis, but it has difficulties when being used in the segmentation of left ventricular (LV) boundaries on echocardiography images because the boundaries are not very distinguish, and the signal-to-noise ratio of echocardiography images is not very high. In this paper, we introduce the Active Shape Model (ASM) into the traditional level set method to enforce shape constraints. It improves the accuracy of boundary detection and makes the evolution more efficient. The experiments conducted on the real cardiac ultrasound image sequences show a positive and promising result.

The New Maia Detector System: Methods For High Definition Trace Element Imaging Of Natural Material

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

Ryan, C. G.; School of Physics, University of Melbourne, Parkville VIC; CODES Centre of Excellence, University of Tasmania, Hobart TAS

2010-04-06

Motivated by the need for megapixel high definition trace element imaging to capture intricate detail in natural material, together with faster acquisition and improved counting statistics in elemental imaging, a large energy-dispersive detector array called Maia has been developed by CSIRO and BNL for SXRF imaging on the XFM beamline at the Australian Synchrotron. A 96 detector prototype demonstrated the capacity of the system for real-time deconvolution of complex spectral data using an embedded implementation of the Dynamic Analysis method and acquiring highly detailed images up to 77 M pixels spanning large areas of complex mineral sample sections.

The New Maia Detector System: Methods For High Definition Trace Element Imaging Of Natural Material

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

Ryan, C.G.; Siddons, D.P.; Kirkham, R.

2010-05-25

Motivated by the need for megapixel high definition trace element imaging to capture intricate detail in natural material, together with faster acquisition and improved counting statistics in elemental imaging, a large energy-dispersive detector array called Maia has been developed by CSIRO and BNL for SXRF imaging on the XFM beamline at the Australian Synchrotron. A 96 detector prototype demonstrated the capacity of the system for real-time deconvolution of complex spectral data using an embedded implementation of the Dynamic Analysis method and acquiring highly detailed images up to 77 M pixels spanning large areas of complex mineral sample sections.

Looking back to inform the future: The role of cognition in forest disturbance characterization from remote sensing imagery

NASA Astrophysics Data System (ADS)

Bianchetti, Raechel Anne

Remotely sensed images have become a ubiquitous part of our daily lives. From novice users, aiding in search and rescue missions using tools such as TomNod, to trained analysts, synthesizing disparate data to address complex problems like climate change, imagery has become central to geospatial problem solving. Expert image analysts are continually faced with rapidly developing sensor technologies and software systems. In response to these cognitively demanding environments, expert analysts develop specialized knowledge and analytic skills to address increasingly complex problems. This study identifies the knowledge, skills, and analytic goals of expert image analysts tasked with identification of land cover and land use change. Analysts participating in this research are currently working as part of a national level analysis of land use change, and are well versed with the use of TimeSync, forest science, and image analysis. The results of this study benefit current analysts as it improves their awareness of their mental processes used during the image interpretation process. The study also can be generalized to understand the types of knowledge and visual cues that analysts use when reasoning with imagery for purposes beyond land use change studies. Here a Cognitive Task Analysis framework is used to organize evidence from qualitative knowledge elicitation methods for characterizing the cognitive aspects of the TimeSync image analysis process. Using a combination of content analysis, diagramming, semi-structured interviews, and observation, the study highlights the perceptual and cognitive elements of expert remote sensing interpretation. Results show that image analysts perform several standard cognitive processes, but flexibly employ these processes in response to various contextual cues. Expert image analysts' ability to think flexibly during their analysis process was directly related to their amount of image analysis experience. Additionally, results show that the basic Image Interpretation Elements continue to be important despite technological augmentation of the interpretation process. These results are used to derive a set of design guidelines for developing geovisual analytic tools and training to support image analysis.

Raman Imaging in Cell Membranes, Lipid-Rich Organelles, and Lipid Bilayers.

PubMed

Syed, Aleem; Smith, Emily A

2017-06-12

Raman-based optical imaging is a promising analytical tool for noninvasive, label-free chemical imaging of lipid bilayers and cellular membranes. Imaging using spontaneous Raman scattering suffers from a low intensity that hinders its use in some cellular applications. However, developments in coherent Raman imaging, surface-enhanced Raman imaging, and tip-enhanced Raman imaging have enabled video-rate imaging, excellent detection limits, and nanometer spatial resolution, respectively. After a brief introduction to these commonly used Raman imaging techniques for cell membrane studies, this review discusses selected applications of these modalities for chemical imaging of membrane proteins and lipids. Finally, recent developments in chemical tags for Raman imaging and their applications in the analysis of selected cell membrane components are summarized. Ongoing developments toward improving the temporal and spatial resolution of Raman imaging and small-molecule tags with strong Raman scattering cross sections continue to expand the utility of Raman imaging for diverse cell membrane studies.

Advances in combined endoscopic fluorescence confocal microscopy and optical coherence tomography

NASA Astrophysics Data System (ADS)

Risi, Matthew D.

Confocal microendoscopy provides real-time high resolution cellular level images via a minimally invasive procedure. Results from an ongoing clinical study to detect ovarian cancer with a novel confocal fluorescent microendoscope are presented. As an imaging modality, confocal fluorescence microendoscopy typically requires exogenous fluorophores, has a relatively limited penetration depth (100 μm), and often employs specialized aperture configurations to achieve real-time imaging in vivo. Two primary research directions designed to overcome these limitations and improve diagnostic capability are presented. Ideal confocal imaging performance is obtained with a scanning point illumination and confocal aperture, but this approach is often unsuitable for real-time, in vivo biomedical imaging. By scanning a slit aperture in one direction, image acquisition speeds are greatly increased, but at the cost of a reduction in image quality. The design, implementation, and experimental verification of a custom multi-point-scanning modification to a slit-scanning multi-spectral confocal microendoscope is presented. This new design improves the axial resolution while maintaining real-time imaging rates. In addition, the multi-point aperture geometry greatly reduces the effects of tissue scatter on imaging performance. Optical coherence tomography (OCT) has seen wide acceptance and FDA approval as a technique for ophthalmic retinal imaging, and has been adapted for endoscopic use. As a minimally invasive imaging technique, it provides morphological characteristics of tissues at a cellular level without requiring the use of exogenous fluorophores. OCT is capable of imaging deeper into biological tissue (˜1-2 mm) than confocal fluorescence microscopy. A theoretical analysis of the use of a fiber-bundle in spectral-domain OCT systems is presented. The fiber-bundle enables a flexible endoscopic design and provides fast, parallelized acquisition of the optical coherence tomography data. However, the multi-mode characteristic of the fibers in the fiber-bundle affects the depth sensitivity of the imaging system. A description of light interference in a multi-mode fiber is presented along with numerical simulations and experimental studies to illustrate the theoretical analysis.

Morphometric information to reduce the semantic gap in the characterization of microscopic images of thyroid nodules.

PubMed

Macedo, Alessandra A; Pessotti, Hugo C; Almansa, Luciana F; Felipe, Joaquim C; Kimura, Edna T

2016-07-01

The analyses of several systems for medical-imaging processing typically support the extraction of image attributes, but do not comprise some information that characterizes images. For example, morphometry can be applied to find new information about the visual content of an image. The extension of information may result in knowledge. Subsequently, results of mappings can be applied to recognize exam patterns, thus improving the accuracy of image retrieval and allowing a better interpretation of exam results. Although successfully applied in breast lesion images, the morphometric approach is still poorly explored in thyroid lesions due to the high subjectivity thyroid examinations. This paper presents a theoretical-practical study, considering Computer Aided Diagnosis (CAD) and Morphometry, to reduce the semantic discontinuity between medical image features and human interpretation of image content. The proposed method aggregates the content of microscopic images characterized by morphometric information and other image attributes extracted by traditional object extraction algorithms. This method carries out segmentation, feature extraction, image labeling and classification. Morphometric analysis was included as an object extraction method in order to verify the improvement of its accuracy for automatic classification of microscopic images. To validate this proposal and verify the utility of morphometric information to characterize thyroid images, a CAD system was created to classify real thyroid image-exams into Papillary Cancer, Goiter and Non-Cancer. Results showed that morphometric information can improve the accuracy and precision of image retrieval and the interpretation of results in computer-aided diagnosis. For example, in the scenario where all the extractors are combined with the morphometric information, the CAD system had its best performance (70% of precision in Papillary cases). Results signalized a positive use of morphometric information from images to reduce semantic discontinuity between human interpretation and image characterization. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Alzheimer disease in a mouse model: MR imaging-guided focused ultrasound targeted to the hippocampus opens the blood-brain barrier and improves pathologic abnormalities and behavior.

PubMed

Burgess, Alison; Dubey, Sonam; Yeung, Sharon; Hough, Olivia; Eterman, Naomi; Aubert, Isabelle; Hynynen, Kullervo

2014-12-01

To validate whether repeated magnetic resonance (MR) imaging-guided focused ultrasound treatments targeted to the hippocampus, a brain structure relevant for Alzheimer disease ( AD Alzheimer disease ), could modulate pathologic abnormalities, plasticity, and behavior in a mouse model. All animal procedures were approved by the Animal Care Committee and are in accordance with the Canadian Council on Animal Care. Seven-month-old transgenic (TgCRND8) (Tg) mice and their nontransgenic (non-Tg) littermates were entered in the study. Mice were treated weekly with MR imaging-guided focused ultrasound in the bilateral hippocampus (1.68 MHz, 10-msec bursts, 1-Hz burst repetition frequency, 120-second total duration). After 1 month, spatial memory was tested in the Y maze with the novel arm prior to sacrifice and immunohistochemical analysis. The data were compared by using unpaired t tests and analysis of variance with Tukey post hoc analysis. Untreated Tg mice spent 61% less time than untreated non-Tg mice exploring the novel arm of the Y maze because of spatial memory impairments (P < .05). Following MR imaging-guided focused ultrasound, Tg mice spent 99% more time exploring the novel arm, performing as well as their non-Tg littermates. Changes in behavior were correlated with a reduction of the number and size of amyloid plaques in the MR imaging-guided focused ultrasound-treated animals (P < .01). Further, after MR imaging-guided focused ultrasound treatment, there was a 250% increase in the number of newborn neurons in the hippocampus (P < .01). The newborn neurons had longer dendrites and more arborization after MR imaging-guided focused ultrasound, as well (P < .01). Repeated MR imaging-guided focused ultrasound treatments led to spatial memory improvement in a Tg mouse model of AD Alzheimer disease . The behavior changes may be mediated by decreased amyloid pathologic abnormalities and increased neuronal plasticity. © RSNA, 2014.

Parallel processing considerations for image recognition tasks

NASA Astrophysics Data System (ADS)

Simske, Steven J.

2011-01-01

Many image recognition tasks are well-suited to parallel processing. The most obvious example is that many imaging tasks require the analysis of multiple images. From this standpoint, then, parallel processing need be no more complicated than assigning individual images to individual processors. However, there are three less trivial categories of parallel processing that will be considered in this paper: parallel processing (1) by task; (2) by image region; and (3) by meta-algorithm. Parallel processing by task allows the assignment of multiple workflows-as diverse as optical character recognition [OCR], document classification and barcode reading-to parallel pipelines. This can substantially decrease time to completion for the document tasks. For this approach, each parallel pipeline is generally performing a different task. Parallel processing by image region allows a larger imaging task to be sub-divided into a set of parallel pipelines, each performing the same task but on a different data set. This type of image analysis is readily addressed by a map-reduce approach. Examples include document skew detection and multiple face detection and tracking. Finally, parallel processing by meta-algorithm allows different algorithms to be deployed on the same image simultaneously. This approach may result in improved accuracy.

Stable image acquisition for mobile image processing applications

NASA Astrophysics Data System (ADS)

Henning, Kai-Fabian; Fritze, Alexander; Gillich, Eugen; Mönks, Uwe; Lohweg, Volker

2015-02-01

Today, mobile devices (smartphones, tablets, etc.) are widespread and of high importance for their users. Their performance as well as versatility increases over time. This leads to the opportunity to use such devices for more specific tasks like image processing in an industrial context. For the analysis of images requirements like image quality (blur, illumination, etc.) as well as a defined relative position of the object to be inspected are crucial. Since mobile devices are handheld and used in constantly changing environments the challenge is to fulfill these requirements. We present an approach to overcome the obstacles and stabilize the image capturing process such that image analysis becomes significantly improved on mobile devices. Therefore, image processing methods are combined with sensor fusion concepts. The approach consists of three main parts. First, pose estimation methods are used to guide a user moving the device to a defined position. Second, the sensors data and the pose information are combined for relative motion estimation. Finally, the image capturing process is automated. It is triggered depending on the alignment of the device and the object as well as the image quality that can be achieved under consideration of motion and environmental effects.

AN IMPROVED DISTANCE AND MASS ESTIMATE FOR SGR A* FROM A MULTISTAR ORBIT ANALYSIS

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

Boehle, A.; Ghez, A. M.; Meyer, L.

2016-10-10

We present new, more precise measurements of the mass and distance of our Galaxy’s central supermassive black hole, Sgr A*. These results stem from a new analysis that more than doubles the time baseline for astrometry of faint stars orbiting Sgr A*, combining 2 decades of speckle imaging and adaptive optics data. Specifically, we improve our analysis of the speckle images by using information about a star’s orbit from the deep adaptive optics data (2005–2013) to inform the search for the star in the speckle years (1995–2005). When this new analysis technique is combined with the first complete re-reduction ofmore » Keck Galactic Center speckle images using speckle holography, we are able to track the short-period star S0-38 ( K -band magnitude = 17, orbital period = 19 yr) through the speckle years. We use the kinematic measurements from speckle holography and adaptive optics to estimate the orbits of S0-38 and S0-2 and thereby improve our constraints of the mass ( M {sub bh}) and distance ( R {sub o} ) of Sgr A*: M {sub bh} = (4.02 ± 0.16 ± 0.04) × 10{sup 6} M {sub ⊙} and 7.86 ± 0.14 ± 0.04 kpc. The uncertainties in M {sub bh} and R {sub o} as determined by the combined orbital fit of S0-2 and S0-38 are improved by a factor of 2 and 2.5, respectively, compared to an orbital fit of S0-2 alone and a factor of ∼2.5 compared to previous results from stellar orbits. This analysis also limits the extended dark mass within 0.01 pc to less than 0.13 × 10{sup 6} M {sub ⊙} at 99.7% confidence, a factor of 3 lower compared to prior work.« less

Visual self-images of scientists and science in Greece.

PubMed

Christidou, Vasilia; Kouvatas, Apostolos

2013-01-01

A popular and well-established image of scientists and science dominates in the public field, signifying a contradictory and multifaceted combination of stereotypes. This paper investigates crucial aspects of the visual self-image of Greek scientists and science as exposed in photographic material retrieved from relevant institutions' websites. In total 971 photos were analysed along dimensions corresponding to the image of scientists and science. Analysis demonstrates ambivalence in Greek scientists' self-images between traditional stereotypic characteristics and an intention to overcome them. Differences between the self-images of physics, chemistry and biology are determined, as well as between the "masculine" and "feminine" face of science. Implications concerning improvements in science and scientists' self-images and further research are presented.

High-throughput Analysis of Large Microscopy Image Datasets on CPU-GPU Cluster Platforms

PubMed Central

Teodoro, George; Pan, Tony; Kurc, Tahsin M.; Kong, Jun; Cooper, Lee A. D.; Podhorszki, Norbert; Klasky, Scott; Saltz, Joel H.

2014-01-01

Analysis of large pathology image datasets offers significant opportunities for the investigation of disease morphology, but the resource requirements of analysis pipelines limit the scale of such studies. Motivated by a brain cancer study, we propose and evaluate a parallel image analysis application pipeline for high throughput computation of large datasets of high resolution pathology tissue images on distributed CPU-GPU platforms. To achieve efficient execution on these hybrid systems, we have built runtime support that allows us to express the cancer image analysis application as a hierarchical data processing pipeline. The application is implemented as a coarse-grain pipeline of stages, where each stage may be further partitioned into another pipeline of fine-grain operations. The fine-grain operations are efficiently managed and scheduled for computation on CPUs and GPUs using performance aware scheduling techniques along with several optimizations, including architecture aware process placement, data locality conscious task assignment, data prefetching, and asynchronous data copy. These optimizations are employed to maximize the utilization of the aggregate computing power of CPUs and GPUs and minimize data copy overheads. Our experimental evaluation shows that the cooperative use of CPUs and GPUs achieves significant improvements on top of GPU-only versions (up to 1.6×) and that the execution of the application as a set of fine-grain operations provides more opportunities for runtime optimizations and attains better performance than coarser-grain, monolithic implementations used in other works. An implementation of the cancer image analysis pipeline using the runtime support was able to process an image dataset consisting of 36,848 4Kx4K-pixel image tiles (about 1.8TB uncompressed) in less than 4 minutes (150 tiles/second) on 100 nodes of a state-of-the-art hybrid cluster system. PMID:25419546

Note: An automated image analysis method for high-throughput classification of surface-bound bacterial cell motions.

PubMed

Shen, Simon; Syal, Karan; Tao, Nongjian; Wang, Shaopeng

2015-12-01

We present a Single-Cell Motion Characterization System (SiCMoCS) to automatically extract bacterial cell morphological features from microscope images and use those features to automatically classify cell motion for rod shaped motile bacterial cells. In some imaging based studies, bacteria cells need to be attached to the surface for time-lapse observation of cellular processes such as cell membrane-protein interactions and membrane elasticity. These studies often generate large volumes of images. Extracting accurate bacterial cell morphology features from these images is critical for quantitative assessment. Using SiCMoCS, we demonstrated simultaneous and automated motion tracking and classification of hundreds of individual cells in an image sequence of several hundred frames. This is a significant improvement from traditional manual and semi-automated approaches to segmenting bacterial cells based on empirical thresholds, and a first attempt to automatically classify bacterial motion types for motile rod shaped bacterial cells, which enables rapid and quantitative analysis of various types of bacterial motion.

Image edge detection based tool condition monitoring with morphological component analysis.

PubMed

Yu, Xiaolong; Lin, Xin; Dai, Yiquan; Zhu, Kunpeng

2017-07-01

The measurement and monitoring of tool condition are keys to the product precision in the automated manufacturing. To meet the need, this study proposes a novel tool wear monitoring approach based on the monitored image edge detection. Image edge detection has been a fundamental tool to obtain features of images. This approach extracts the tool edge with morphological component analysis. Through the decomposition of original tool wear image, the approach reduces the influence of texture and noise for edge measurement. Based on the target image sparse representation and edge detection, the approach could accurately extract the tool wear edge with continuous and complete contour, and is convenient in charactering tool conditions. Compared to the celebrated algorithms developed in the literature, this approach improves the integrity and connectivity of edges, and the results have shown that it achieves better geometry accuracy and lower error rate in the estimation of tool conditions. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Investigation of image enhancement techniques for the development of a self-contained airborne radar navigation system

NASA Technical Reports Server (NTRS)

Phatak, A. V.; Karmali, M. S.

1983-01-01

This study was devoted to an investigation of the feasibility of applying advanced image processing techniques to enhance radar image characteristics that are pertinent to the pilot's navigation and guidance task. Millimeter (95 GHz) wave radar images for the overwater (i.e., offshore oil rigs) and overland (Heliport) scenario were used as a data base. The purpose of the study was to determine the applicability of image enhancement and scene analysis algorithms to detect and improve target characteristics (i.e., manmade objects such as buildings, parking lots, cars, roads, helicopters, towers, landing pads, etc.) that would be helpful to the pilot in determining his own position/orientation with respect to the outside world and assist him in the navigation task. Results of this study show that significant improvements in the raw radar image may be obtained using two dimensional image processing algorithms. In the overwater case, it is possible to remove the ocean clutter by thresholding the image data, and furthermore to extract the target boundary as well as the tower and catwalk locations using noise cleaning (e.g., median filter) and edge detection (e.g., Sobel operator) algorithms.

An Improved Variational Method for Hyperspectral Image Pansharpening with the Constraint of Spectral Difference Minimization

NASA Astrophysics Data System (ADS)

Huang, Z.; Chen, Q.; Shen, Y.; Chen, Q.; Liu, X.

2017-09-01

Variational pansharpening can enhance the spatial resolution of a hyperspectral (HS) image using a high-resolution panchromatic (PAN) image. However, this technology may lead to spectral distortion that obviously affect the accuracy of data analysis. In this article, we propose an improved variational method for HS image pansharpening with the constraint of spectral difference minimization. We extend the energy function of the classic variational pansharpening method by adding a new spectral fidelity term. This fidelity term is designed following the definition of spectral angle mapper, which means that for every pixel, the spectral difference value of any two bands in the HS image is in equal proportion to that of the two corresponding bands in the pansharpened image. Gradient descent method is adopted to find the optimal solution of the modified energy function, and the pansharpened image can be reconstructed. Experimental results demonstrate that the constraint of spectral difference minimization is able to preserve the original spectral information well in HS images, and reduce the spectral distortion effectively. Compared to original variational method, our method performs better in both visual and quantitative evaluation, and achieves a good trade-off between spatial and spectral information.

Iterative Structural and Functional Synergistic Resolution Recovery (iSFS-RR) Applied to PET-MR Images in Epilepsy

NASA Astrophysics Data System (ADS)

Silva-Rodríguez, J.; Cortés, J.; Rodríguez-Osorio, X.; López-Urdaneta, J.; Pardo-Montero, J.; Aguiar, P.; Tsoumpas, C.

2016-10-01

Structural Functional Synergistic Resolution Recovery (SFS-RR) is a technique that uses supplementary structural information from MR or CT to improve the spatial resolution of PET or SPECT images. This wavelet-based method may have a potential impact on the clinical decision-making of brain focal disorders such as refractory epilepsy, since it can produce images with better quantitative accuracy and enhanced detectability. In this work, a method for the iterative application of SFS-RR (iSFS-RR) was firstly developed and optimized in terms of convergence and input voxel size, and the corrected images were used for the diagnosis of 18 patients with refractory epilepsy. To this end, PET/MR images were clinically evaluated through visual inspection, atlas-based asymmetry indices (AIs) and SPM (Statistical Parametric Mapping) analysis, using uncorrected images and images corrected with SFS-RR and iSFS-RR. Our results showed that the sensitivity can be increased from 78% for uncorrected images, to 84% for SFS-RR and 94% for the proposed iSFS-RR. Thus, the proposed methodology has demonstrated the potential to improve the management of refractory epilepsy patients in the clinical routine.

Analysis of sharpness increase by image noise

NASA Astrophysics Data System (ADS)

Kurihara, Takehito; Aoki, Naokazu; Kobayashi, Hiroyuki

2009-02-01

Motivated by the reported increase in sharpness by image noise, we investigated how noise affects sharpness perception. We first used natural images of tree bark with different amounts of noise to see whether noise enhances sharpness. Although the result showed sharpness decreased as noise amount increased, some observers seemed to perceive more sharpness with increasing noise, while the others did not. We next used 1D and 2D uni-frequency patterns as stimuli in an attempt to reduce such variability in the judgment. The result showed, for higher frequency stimuli, sharpness decreased as the noise amount increased, while sharpness of the lower frequency stimuli increased at a certain noise level. From this result, we thought image noise might reduce sharpness at edges, but be able to improve sharpness of lower frequency component or texture in image. To prove this prediction, we experimented again with the natural image used in the first experiment. Stimuli were made by applying noise separately to edge or to texture part of the image. The result showed noise, when added to edge region, only decreased sharpness, whereas when added to texture, could improve sharpness. We think it is the interaction between noise and texture that sharpens image.

Update on imaging techniques in oculoplastics

PubMed Central

Cetinkaya, Altug

2012-01-01

Imaging is a beneficial aid to the oculoplastic surgeon especially in orbital and lacrimal disorders when the pathology is not visible from outside. It is a powerful tool that may be benefited in not only diagnosis but also management and follow-up. The most common imaging modalities required are CT and MRI, with CT being more frequently ordered by oculoplastic surgeons. Improvements in technology enabled the acquisition times to shorten incredibly. Radiologists can now obtain images with superb resolution, and isolate the site and tissue of interest from other structures with special techniques. Better contrast agents and 3D imaging capabilities make complicated cases easier to identify. Color Doppler imaging is becoming more popular both for research and clinical purposes. Magnetic resonance angiography (MRA) added so much to the vascular system imaging recently. Although angiography is still the gold standard, new software and techniques rendered MRA as valuable as angiography in most circumstances. Stereotactic navigation, although in use for a long time, recently became the focus of interest for the oculoplastic surgeon especially in orbital decompressions. Improvements in radiology and nuclear medicine techniques of lacrimal drainage system imaging provided more detailed analysis of the system. PMID:23961020

Contribution of non-negative matrix factorization to the classification of remote sensing images

NASA Astrophysics Data System (ADS)

Karoui, M. S.; Deville, Y.; Hosseini, S.; Ouamri, A.; Ducrot, D.

2008-10-01

Remote sensing has become an unavoidable tool for better managing our environment, generally by realizing maps of land cover using classification techniques. The classification process requires some pre-processing, especially for data size reduction. The most usual technique is Principal Component Analysis. Another approach consists in regarding each pixel of the multispectral image as a mixture of pure elements contained in the observed area. Using Blind Source Separation (BSS) methods, one can hope to unmix each pixel and to perform the recognition of the classes constituting the observed scene. Our contribution consists in using Non-negative Matrix Factorization (NMF) combined with sparse coding as a solution to BSS, in order to generate new images (which are at least partly separated images) using HRV SPOT images from Oran area, Algeria). These images are then used as inputs of a supervised classifier integrating textural information. The results of classifications of these "separated" images show a clear improvement (correct pixel classification rate improved by more than 20%) compared to classification of initial (i.e. non separated) images. These results show the contribution of NMF as an attractive pre-processing for classification of multispectral remote sensing imagery.

Algorithm sensitivity analysis and parameter tuning for tissue image segmentation pipelines

PubMed Central

Kurç, Tahsin M.; Taveira, Luís F. R.; Melo, Alba C. M. A.; Gao, Yi; Kong, Jun; Saltz, Joel H.

2017-01-01

Abstract Motivation: Sensitivity analysis and parameter tuning are important processes in large-scale image analysis. They are very costly because the image analysis workflows are required to be executed several times to systematically correlate output variations with parameter changes or to tune parameters. An integrated solution with minimum user interaction that uses effective methodologies and high performance computing is required to scale these studies to large imaging datasets and expensive analysis workflows. Results: The experiments with two segmentation workflows show that the proposed approach can (i) quickly identify and prune parameters that are non-influential; (ii) search a small fraction (about 100 points) of the parameter search space with billions to trillions of points and improve the quality of segmentation results (Dice and Jaccard metrics) by as much as 1.42× compared to the results from the default parameters; (iii) attain good scalability on a high performance cluster with several effective optimizations. Conclusions: Our work demonstrates the feasibility of performing sensitivity analyses, parameter studies and auto-tuning with large datasets. The proposed framework can enable the quantification of error estimations and output variations in image segmentation pipelines. Availability and Implementation: Source code: https://github.com/SBU-BMI/region-templates/. Contact: teodoro@unb.br Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28062445

Algorithm sensitivity analysis and parameter tuning for tissue image segmentation pipelines.

PubMed

Teodoro, George; Kurç, Tahsin M; Taveira, Luís F R; Melo, Alba C M A; Gao, Yi; Kong, Jun; Saltz, Joel H

2017-04-01

Sensitivity analysis and parameter tuning are important processes in large-scale image analysis. They are very costly because the image analysis workflows are required to be executed several times to systematically correlate output variations with parameter changes or to tune parameters. An integrated solution with minimum user interaction that uses effective methodologies and high performance computing is required to scale these studies to large imaging datasets and expensive analysis workflows. The experiments with two segmentation workflows show that the proposed approach can (i) quickly identify and prune parameters that are non-influential; (ii) search a small fraction (about 100 points) of the parameter search space with billions to trillions of points and improve the quality of segmentation results (Dice and Jaccard metrics) by as much as 1.42× compared to the results from the default parameters; (iii) attain good scalability on a high performance cluster with several effective optimizations. Our work demonstrates the feasibility of performing sensitivity analyses, parameter studies and auto-tuning with large datasets. The proposed framework can enable the quantification of error estimations and output variations in image segmentation pipelines. Source code: https://github.com/SBU-BMI/region-templates/ . teodoro@unb.br. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Digital Transplantation Pathology: Combining Whole Slide Imaging, Multiplex Staining, and Automated Image Analysis

PubMed Central

Isse, Kumiko; Lesniak, Andrew; Grama, Kedar; Roysam, Badrinath; Minervini, Martha I.; Demetris, Anthony J

2013-01-01

Conventional histopathology is the gold standard for allograft monitoring, but its value proposition is increasingly questioned. “-Omics” analysis of tissues, peripheral blood and fluids and targeted serologic studies provide mechanistic insights into allograft injury not currently provided by conventional histology. Microscopic biopsy analysis, however, provides valuable and unique information: a) spatial-temporal relationships; b) rare events/cells; c) complex structural context; and d) integration into a “systems” model. Nevertheless, except for immunostaining, no transformative advancements have “modernized” routine microscopy in over 100 years. Pathologists now team with hardware and software engineers to exploit remarkable developments in digital imaging, nanoparticle multiplex staining, and computational image analysis software to bridge the traditional histology - global “–omic” analyses gap. Included are side-by-side comparisons, objective biopsy finding quantification, multiplexing, automated image analysis, and electronic data and resource sharing. Current utilization for teaching, quality assurance, conferencing, consultations, research and clinical trials is evolving toward implementation for low-volume, high-complexity clinical services like transplantation pathology. Cost, complexities of implementation, fluid/evolving standards, and unsettled medical/legal and regulatory issues remain as challenges. Regardless, challenges will be overcome and these technologies will enable transplant pathologists to increase information extraction from tissue specimens and contribute to cross-platform biomarker discovery for improved outcomes. PMID:22053785