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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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Intensity Based Optic Disk Detection for Automatic Diabetic Retinopathy

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Diabetic Retinopathy Diagnosis Through Computer-Aided Fundus Image Analysis: A Review

TL;DR: A comprehensive review of the state-of-the-art methods on landmark structures detection and segmentation, retinal lesions segmentation and diabetic retinopathy screening methods with retinal fundus images is presented in this paper.
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Multi-Scale Regularized Deep Network for Retinal Vessel Segmentation

TL;DR: A novel domain enriched deep network that consists of a representation network which learns geometric features that are tailored to retinal images, followed by a task network that utilizes the features obtained from the representation layer to perform pixel-level segmentation.
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Tissue-Engineered Models for Glaucoma Research.

TL;DR: Current tissue-engineered models of the intraocular space that aim to advance the state of glaucoma research are described and the need for novel organ-on-a-chip models of glAUcoma that functionally integrate currently available 3D model of the retina and the trabecular outflow pathway is pointed out.
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Red-lesion extraction in retinal fundus images by directional intensity changes' analysis.

TL;DR: In this paper, a novel red-lesion extraction method is proposed, which first determines the boundary pixels of blood vessel and red lesions and then determines the distinguishing features of boundary pixels to discriminate them from other boundary pixels.
References
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Journal ArticleDOI

Distinctive Image Features from Scale-Invariant Keypoints

TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.

Distinctive Image Features from Scale-Invariant Keypoints

TL;DR: The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images that can then be used to reliably match objects in diering images.
Journal ArticleDOI

Optical coherence tomography

TL;DR: OCT as discussed by the authors uses low-coherence interferometry to produce a two-dimensional image of optical scattering from internal tissue microstructures in a way analogous to ultrasonic pulse-echo imaging.
Journal ArticleDOI

A taxonomy and evaluation of dense two-frame stereo correspondence algorithms

TL;DR: This paper has designed a stand-alone, flexible C++ implementation that enables the evaluation of individual components and that can easily be extended to include new algorithms.
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