scispace - formally typeset
Open AccessJournal ArticleDOI

Retinal Imaging and Image Analysis

Reads0
Chats0
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

Content maybe subject to copyright    Report

Citations
More filters
Journal ArticleDOI

Multi-Scale Segmentation and Surface Fitting for Measuring 3-D Macular Holes

TL;DR: This work introduces a multi-scale 3-D level set segmentation approach based on a state-of-the-art level set method, and introduces novel curvature-based cutting and3-D measurement procedures.
Journal ArticleDOI

Multi-scale channel importance sorting and spatial attention mechanism for retinal vessels segmentation

TL;DR: A multi-scale channel importance sorting and important spatial information positioning encoder–decoder for segmentation in Retinal Vessels can greatly decrease false positive rate of the blood vessels at the ends and enhance the sharpness of retinal vessels.
Journal ArticleDOI

Automated 3D Segmentation of Intraretinal Surfaces in SD-OCT Volumes in Normal and Diabetic Mice.

TL;DR: The presented method, initially developed for human OCT, has been adapted for mice, with the potential to be adapted for other animals as well and is expected to be useful in the quantitative study of intraretinal layers in mice.
Journal ArticleDOI

Antiangiogenic and Neurogenic Activities of Sleeping Beauty-Mediated PEDF-Transfected RPE Cells In Vitro and In Vivo.

TL;DR: It is shown that transplantation of pigment epithelial cells overexpressing PEDF can restore a permissive subretinal environment for RPE and photoreceptor maintenance, while inhibiting choroidal blood vessel growth.
Journal ArticleDOI

GC-NET for classification of glaucoma in the retinal fundus image

TL;DR: A deep learning-based glaucoma classification network (GC-NET) for classifying a retinal image as glaucatous or non-glaucomatous and results showed that GC-NET achieves accuracy of 97.51%, sensitivity of 98.78% and specificity of 96.20% which outperforms state of the art.
References
More filters
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.
Related Papers (5)