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Open AccessJournal ArticleDOI

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.

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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.
Journal ArticleDOI

Indian Diabetic Retinopathy Image Dataset (IDRiD): A Database for Diabetic Retinopathy Screening Research

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.

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.
Journal ArticleDOI

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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Proceedings ArticleDOI

Accurate Boundary Localization using Dynamic Programming on Snakes

TL;DR: A deterministic iterative statistical data fusion approach, in which the visual boundaries of the object are extracted, ignoring any prior, employing a hidden Markov model and Viterbi search, and then applying importance sampling to the boundary points, on which the shape prior is asserted.
Book ChapterDOI

A global-to-local matching strategy for registering retinal fundus images

TL;DR: A multi-resolution rigid-model-based global matching algorithm is employed to register tree structures of blood vessels extracted from retinal fundus images to improve alignment of the vessels and to eliminate the existence of ‘ghost vessels' for accurate registration.
Book ChapterDOI

Automatic Change Detection of Retinal Images

TL;DR: The proposed approach for the detection of temporal changes within the registered images is based on the application of an unsupervised algorithm, in order to cope with the lack of training information about the statistic of the changed areas in fundus images.
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