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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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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

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.
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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.
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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.
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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

Retinal nerve fiber layer thickness measurement comparability between time domain optical coherence tomography (OCT) and spectral domain OCT.

TL;DR: Scan location matching may bridge the gap in RNFL thickness measurements between TD-OCT circular scan data and 3-D SD-O CT scan data, providing follow-up comparability across the two generations of OCTs.
Proceedings ArticleDOI

Content Based Image Retrieval based on Wavelet Transform coefficients distribution

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Proceedings ArticleDOI

Correction of motion artifacts and scanning beam distortions in 3D ophthalmic optical coherence tomography imaging

TL;DR: Two possible solutions to minimize and compensate for artifacts caused by subject eye and head motion, and distortions caused by the geometry of the scanning optics are proposed.
Book ChapterDOI

Incorporation of regional information in optimal 3-D graph search with application for intraretinal layer segmentation of optical coherence tomography images

TL;DR: This work presents a method for the incorporation of regional image information in a 3-D graph-theoretic approach for optimal multiple surface segmentation, and applies it to the segmentation of seven intraretinal layer surfaces of 243-D macular optical coherence tomography images from 12 subjects.
Journal Article

The accuracy of digital-video retinal imaging to screen for diabetic retinopathy: an analysis of two digital-video retinal imaging systems using standard stereoscopic seven-field photography and dilated clinical examination as reference standards.

TL;DR: The 800x600 resolution DVRI system offers an accurate method of detecting diabetic retinopathy, provided there is adequate pupillary dilation and three retinal images are taken.
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