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International Conference on Intelligent and Advanced Systems, ICIAS

作者： Apurv Joshi Atharva Kadethankar Vedant Patwardhan Prasanna Porwal Samiksha Pachade Manesh Kokare Fabrice Meriaudeau Center of Excellence in Signal and Image Processing Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India Department of Electrical & Electronic Engineering University Teknologi PETRONAS Malaysia

Glaucoma is considered as world's second biggest health emergency of this century. Glaucoma is an irremediable chronic ocular disease. In Glaucoma, Optic Nerve Head (ONH) gets damaged due to loss of ganglion cells. This damage of ONH causes cupping of nerve head and optic cup size increases with respect to the size of optic disc. These changes in ONH can be detected using various image processing methods which can be applied on retinal images acquired using Optical Coherence Tomograph, Retinal fundus camera and Heidelberg Retina Tomograph. This paper proposes an approach to detect Glaucoma automatically from retinal fundus images. A level set based approach for segmentation of optic disc (OD) is used and further analysis of segmented OD is done by textural information. In this approach, image features are obtained by the use of Local Binary Pattern (LBP) and these image features were classified using Support Vector Machine(SVM) classifier.

关键词： Level set Optical imaging image segmentation Feature extraction Retina Integrated optics Adaptive optics

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Automatic Segmentation of Optic Disc by Gradient Minimization Based Approach

Automatic Segmentation of Optic Disc by Gradient Minimizatio...

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International Conference on Intelligent and Advanced Systems, ICIAS

作者： Prasanna Porwal Samiksha Pachade Atharva Kadethankar Apurv Joshi Vedant Patwardhan Ravi Kamble Manesh Kokare Fabrice Meriaudeau Center of Excellence in Signal and Image Processing Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India Department of Electrical & Electronic Engineering University Teknologi PETRONAS Malaysia

Diabetic Retinopathy (DR) and Glaucoma are the eye diseases that cause progressive damage to patient's vision over time. Optic disc detection and segmentation is of significant importance for automating the screening of both diseases. The method proposed for the precise and fast segmentation of the optic disc mainly based obtaining optic disc center location using intensity profile analysis and further segmentation of chosen region of interest using L 0 gradient minimization based technique. The optimum threshold was chosen for OD segmentation using Otsu's method. Performance of the technique is evaluated using Dice and Jaccard metrics that are obtained as 80.23% and 71.17% 78.46% and 82.63% respectively on IDRiD and the MESSIDOR dataset. The proposed method is found to be very fast and efficient.

关键词： Optical imaging image segmentation Adaptive optics Minimization Retina Optical distortion Retinopathy

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Metrics Reloaded: Recommendations for image analysis validation

arXiv

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arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

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Automatic Field of View Extraction with Variable Enhancement of Color Fundus images 14

Automatic Field of View Extraction with Variable Enhancement...

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14th IEEE India Council International Conference, INDICON 2017

作者： Kulkarni, Sushma Kamble, Ravi Kokare, Manesh Dept. of Electronics and Telecom. Engg. Center of Excellence in Signal and Image Processing SGGS Institute of Engineering and Technolgy Nanded431606 India

ISBN: (纸本)9781538643181

Most retinal images suffers from large size with inadequate contrast and brightness. Automated field of view (FOV) extraction and image enhancement significantly increases the performance of image analysis for diabetic retinopathy (DR) detection. This paper presents a simple and automated FOV extraction technique using only one horizontal and vertical scan lines. The FOV image further enhanced using the guided filter with variable enhancement factor. The proposed method has been applied on five publicly available databases. The accuracy of FOV extraction is obtained 100% for Diaretdb0 and ONHSD databases, 99.91% for messidor, 99.32% for E-optha and 98.87% for Diaretdb1 database. The experimental results shows that, proposed method gives the good enhancement on five different databases compared with the existing state-of-the-art methods. Overall, the proposed method can be used as a effective preprocessing tool for accurate detection of lesions in retinal image analysis. © 2017 IEEE.

关键词： Database systems

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Common Limitations of image processing Metrics: A Picture Story

arXiv

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arXiv 2021年

作者： Reinke, Annika Tizabi, Minu D. Sudre, Carole H. Eisenmann, Matthias Rädsch, Tim Baumgartner, Michael Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Bankhead, Peter Benis, Arriel Blaschko, Matthew Buettner, Florian Cardoso, M. Jorge Chen, Jianxu Cheplygina, Veronika Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Engelhardt, Sandy Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hamprecht, Fred Hashimoto, Daniel A. Heckmann-Nötzel, Doreen Hirsch, Peter Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kavur, A. Emre Kenngott, Hannes Kleesiek, Jens Kleppe, Andreas Koehler, Sven Kofler, Florian Kopp-Schneider, Annette Kooi, Thijs Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moher, David Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Noyan, M. Alican Petersen, Jens Polat, Gorkem Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Rieke, Nicola Riegler, Michael A. Rivaz, Hassan Saez-Rodriguez, Julio Sánchez, Clara I. Schroeter, Julien Saha, Anindo Selver, M. Alper Sharan, Lalith Shetty, Shravya Smeden, Maarten V.A.N. Stieltjes, Bram Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben V.A.N. Varoquaux, Gaël Wiesenfarth, Manuel Yaniv, Ziv R. Jäger, Paul Maier-Hein, Lena Division of Intelligent Medical Systems and HI Helmholtz Imaging Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Intelligent Medical Systems Heidelberg Germany NCT Heidelberg DKFZ University Medical Center Heidelberg Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Division of Medical Image Computing Heidelberg Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Institute of Genetics and Cancer University of Edinburgh Edinburgh United Kingdom Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Kasteelpark Arenberg 10 - box 2441 Leuven3001 Belgium Frankfurt/Mainz DKFZ UCT Frankfurt-Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany Frankfurt Cancer Insititute Germany Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for St

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide. © 2021, CC BY.

关键词： Medical imaging

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Edgy salient local binary patterns in inter-plane relationship for image retrieval in Diabetic Retinopathy

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Procedia Computer Science 2017年 115卷 440-447页

作者： Gajanan M. Galshetwar Laxman M. Waghmare Anil B. Gonde Subrahmanyam Murala Center of Excellence in Signal and Image Processing (COESIP) Department of ECE SGGSIET Nanded Maharashtra 431606 India Computer Vision and Pattern Recognition Laboratory Department of Electrical Engineering IIT Ropar Rupnagar 140001 India

In this paper, a novel approach for content based image retrieval (CBIR) in diabetic retinopathy (DR) is proposed. The concept of salient point selection and inter-plane relationship technique is used. Salient points are selected from edgy image and later using inter-planer relationship, Local Binary Patterns (LBPs) are calculated using the salient point as a center pixel. Our approach enhanced the results as we used color features in combination with LBP features. Experimentation is carried out on MESSIDOR database of 1200 retinal images, proposed approach has average precision of 57.82% as compared to the earlier approach whose average precision is 53.70%.

关键词： Content-Based image retrieval (CBIR) Diabetic Retinopathy (DR) Edgy Salient points Local Binary Patterns (LBPs)

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A novel method to remove bright artifacts for enhancing lesion detection in retinal images

A novel method to remove bright artifacts for enhancing lesi...

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International Conference on signal and Information processing (IConSIP)

作者： Prasanna Porwal Manesh Kokare Center of Excellence in Signal and Image Processing Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded M.S India

ISBN: (纸本)9781509015238

The advancement of computerized telemedicine framework for diabetic retinopathy screening relies on efficient detection of retinal lesions. Good contrast and artifact-less images are the requirement of automated methods for detection of abnormalities. In this paper, we propose a novel method for detection of bright artifacts such as nerve fiber layer reflections, optical reflections and bright border regions using quantile measure. We have tested our method on 2308 images which shows satisfactory mask generation performance. Removal of these outliers have advantage for minimizing false positives in the detection of abnormalities by computerized systems or manual evaluation. Our experiment show encouraging results in catching these outliers and it can be used for automatic detection and removal of bright artifacts.

关键词： Retina Optical distortion Optical imaging Lesions Optical fibers Diabetes

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Automatic retinal image registration using fully connected vascular tree

Automatic retinal image registration using fully connected v...

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International Conference on signal and Information processing (IConSIP)

作者： Janabai Parekar Prasanna Porwal Manesh Kokare Center of Excellence in Signal and Image Processing Shri Guru Gobind Singhji Institute of Engineering and Technology Vishnupuri Nanded M.S. India

ISBN: (纸本)9781509015238

Retinal image registration plays very important role in medical imaging, as it combines the information of different field of view images and temporal images. This paper presents the novel method for retinal image registration based on bifurcation structure matching. In proposed method, segmentation is based on the multi-scale ridge detection, and the preprocessing steps used to avoid false detection of vessels. Also, Dijkstra's algorithm is used to get a fully connected vascular tree. A novel technique is proposed for removing the false correspondence. This method is invariant to translation, rotation and scaling since feature vector contains normalized lengths and angles. The registered image is visually good as well as experimental performance shows very good results.

关键词： Retina Bifurcation image segmentation image registration Biomedical imaging Detectors image edge detection

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Ambiguity function for distributed MIMO radar systems

Ambiguity function for distributed MIMO radar systems

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IEEE National Conference on Radar

作者： Christos V Ilioudis Carmine Clemente Ian Proudler John Soraghan Center for Excellence in Signal and Image Processing University of Strathclyde Glasgow UK School of Electronic Electrical and Systems Engineering Loughborough University Leicestershire UK

ISBN: (纸本)9781509008643

In this paper a multi-static ambiguity function (AF) based on the Kullback directed divergence (KDD) and a distributed multiple-input and multiple-output radar system (DMRS) framework is introduced. Additionally a mathematical analysis is used to derive the AF in terms of signal-to-noise ratios (SNRs) and matched filter outputs. This method manages to extract an upper bound and properly define an AF bounded from 0 to 1. Moreover, this method leads in avoidance of large matrices inversions allowing less complex and more accurate computations. Finally the performance of the proposed method in localisation problems is assessed by comparing the proposed AF with the squared summation of the matched filter outputs at each receiver at different SNR scenarios.

关键词： Receivers Transmitters signal to noise ratio MIMO radar Impedance matching Radar antennas

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Edge Detection in SAR images using phase stretch transform 2

Edge Detection in SAR images using phase stretch transform

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2nd IET International Conference on Intelligent signal processing 2015, ISP 2015

作者： VIlioudis, Christos Clemente, Carmine Asghari, Mohammad H Jalali, Bahram Soraghan, John J. Center for Excellence in Signal and Image Processing University of Strathclyde Glasgow United Kingdom Department of Electrical Engineering University of California Los Angeles Los AngelesCA90095 United States

ISBN: (纸本)9781785611360

In this work a novel approach to edge detection on Synthetic Aperture Radar (SAR) images is introduced. The proposed method uses an optics inspired transform which emulates the diffraction of an image through a medium with nonlinear dispersive properties. The experimental results show that the output of the introduced Phase Stretch Transform (PST) in conjunction with further morphological operations can be effectively used for image edge detection.

关键词： Edge detection

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