Retinal Imaging and Image Analysis

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.

Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search

“…We applied the proposed CNN-GS method, DOCTRAP software, and the publicly available OCTExplorer software (downloaded at: https://www.iibi.uiowa.edu/content/iowareference-algorithms-human-and-murine-oct-retinal-layer-analysis-and-display) [26,60,61] to the 60 OCT volumes from the test set and compared their results with the manually corrected segmentations. OCTExplorer is a 3D OCT layer segmentation software, which utilizes correlations among nearby B-scans for segmentation [60].…”

Registration of SD-OCT en-face images with color fundus photographs based on local patch matching

“…Currently, a few approaches are used to generate the SD-OCT fundus images from SD-OCT volumetric data, and they are all based on the idea of using depth-wise averaging of each individual A-scan [10,11]. In order to make blood vessels appear more clearly in fundus projection images, we propose a novel projection method between the RPE layer and IS-OS layer segmented by the multi-scale 3D graph search method [12] to generate the fundus image, based on the depth average of A-scan values, analogous to that done in [10].…”

Automated detection of microaneurysms using scale-adapted blob analysis and semi-supervised learning

“…Early detection of microaneurysms facilitates timely treatment of DR and limits further complications in the retina. In recent years, automated computeraided detection and diagnosis (CAD) of MAs has attracted many researchers due to its low-cost and versatile nature for public screening applications [1]. However, the variations in size, shape, presence of other retina vascular structures, and illumination variation of fundus images make the design of an accurate CAD system very challenging.…”

“…In fundus imaging, the difficulty in uniformly illuminating the retina results in non-uniform illumination and shading effect [1]. The first step in analyzing such images is thus to compensate for this non-uniform illumination.…”