Retinal Imaging and Image Analysis
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TLDR
Methods for 2-D fundus imaging and techniques for 3-D optical coherence tomography (OCT) imaging are reviewed and aspects of image acquisition, image analysis, and clinical relevance are treated together considering their mutually interlinked relationships.Abstract:
Many important eye diseases as well as systemic diseases manifest themselves in the retina. While a number of other anatomical structures contribute to the process of vision, this review focuses on retinal imaging and image analysis. Following a brief overview of the most prevalent causes of blindness in the industrialized world that includes age-related macular degeneration, diabetic retinopathy, and glaucoma, the review is devoted to retinal imaging and image analysis methods and their clinical implications. Methods for 2-D fundus imaging and techniques for 3-D optical coherence tomography (OCT) imaging are reviewed. Special attention is given to quantitative techniques for analysis of fundus photographs with a focus on clinically relevant assessment of retinal vasculature, identification of retinal lesions, assessment of optic nerve head (ONH) shape, building retinal atlases, and to automated methods for population screening for retinal diseases. A separate section is devoted to 3-D analysis of OCT images, describing methods for segmentation and analysis of retinal layers, retinal vasculature, and 2-D/3-D detection of symptomatic exudate-associated derangements, as well as to OCT-based analysis of ONH morphology and shape. Throughout the paper, aspects of image acquisition, image analysis, and clinical relevance are treated together considering their mutually interlinked relationships.read more
Citations
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Journal ArticleDOI
Trainable COSFIRE filters for vessel delineation with application to retinal images
TL;DR: A novel method for the automatic segmentation of vessel trees in retinal fundus images by summing up the responses of the two rotation-invariant B-COSFIRE filters followed by thresholding is introduced.
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Indian Diabetic Retinopathy Image Dataset (IDRiD): A Database for Diabetic Retinopathy Screening Research
Prasanna Porwal,Samiksha Pachade,Ravi Kamble,Manesh Kokare,Girish Deshmukh,Vivek Sahasrabuddhe,Fabrice Meriaudeau +6 more
TL;DR: The IDRiD (Indian Diabetic Retinopathy Image Dataset), is the first database representative of an Indian population and makes it perfect for development and evaluation of image analysis algorithms for early detection of diabetic retinopathy.
Journal ArticleDOI
Detecting the Optic Disc Boundary in Digital Fundus Images Using Morphological, Edge Detection, and Feature Extraction Techniques
TL;DR: A new template-based methodology for segmenting the OD from digital retinal images using morphological and edge detection techniques followed by the Circular Hough Transform to obtain a circular OD boundary approximation is presented.
Journal ArticleDOI
Artificial intelligence in retina.
Ursula Schmidt-Erfurth,Amir Sadeghipour,Bianca S. Gerendas,Sebastian M. Waldstein,Hrvoje Bogunovic +4 more
TL;DR: In this paper, a fully automated AI-based system has been proposed for screening of diabetic retinopathy (DR) in diabetic macular and retinal disease using a convolutional neural network.
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A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images
TL;DR: Results suggest that this method for blood vessel segmentation in fundus images based on a discriminatively trained fully connected conditional random field model is suitable for the task of segmenting elongated structures, a feature that can be exploited to contribute with other medical and biological applications.
References
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Journal ArticleDOI
Automated detection of diabetic retinopathy on digital fundus images.
Chanjira Sinthanayothin,James F. Boyce,Tom H. Williamson,H. L. Cook,E Mensah,Shiv Lal,D. Usher +6 more
TL;DR: The aim was to develop an automated screening system to analyse digital colour retinal images for important features of non‐proliferative diabetic retinopathy (NPDR).
Journal ArticleDOI
Automatic detection of diabetic retinopathy using an artificial neural network : a screening tool
TL;DR: In this article, a back propagation neural network was used to detect vessels, exudates, and haemorrhages in fundus images of 147 diabetic and 32 normal images.
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Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs
Meindert Niemeijer,Bram van Ginneken,Michael J. Cree,Atsushi Mizutani,Gwenole Quellec,Clara I. Sánchez,Bob Zhang,Roberto Hornero,Mathieu Lamard,Chisako Muramatsu,Xiangqian Wu,Guy Cazuguel,Jane You,Agustín Mayo,Qin Li,Yuji Hatanaka,Béatrice Cochener,Christian Roux,Fakhri Karray,María García,Hiroshi Fujita,Michael D. Abràmoff +21 more
TL;DR: The overall results show that microaneurysm detection is a challenging task for both the automatic methods as well as the human expert, and there is room for improvement as the best performing system does not reach the performance of thehuman expert.
Automatic Detection of Red Lesions in Digital Color
Meindert Niemeijer,Bram van Ginneken,Joes Staal,Maria S. A. Suttorp-Schulten,Michael D. Abràmoff +4 more
TL;DR: In this paper, a red lesion detection method is presented based on a hybrid approach, combining prior works by Spencer et al. (1996) and Frame et al (1998) with two important new contributions.