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

Decision support system for detection of hypertensive retinopathy using arteriovenous ratio.

TL;DR: An automated system is presented that detects and grades HR disease using Arteriovenous Ratio (AVR) and presents a new dataset AVRDB for A/V classification and HR detection.
Journal ArticleDOI

Diagnostic Accuracy of a Device for the Automated Detection of Diabetic Retinopathy in a Primary Care Setting.

TL;DR: The hybrid deep learning–enhanced device had high diagnostic accuracy for the detection of both vtDR and mtmDR in a primary care setting against an independent reading center, which allows its’ safe use in aPrimary care setting.
Journal ArticleDOI

Delineation of blood vessels in pediatric retinal images using decision trees-based ensemble classification

TL;DR: A new retinal blood vessel segmentation algorithm was developed and tested and the observed accuracy, speed, robustness and simplicity suggest that the algorithm may be a suitable tool for automated retinal image analysis in large population-based studies.
Proceedings ArticleDOI

Deep neural network and random forest hybrid architecture for learning to detect retinal vessels in fundus images

TL;DR: This work presents a computational imaging framework using deep and ensemble learning based hybrid architecture for reliable detection of blood vessels in fundus color images and achieves the objective of vessel detection with max.
Proceedings ArticleDOI

Retinal vessel classification: Sorting arteries and veins

TL;DR: The proposed system automatically classify retinal vessels as arteries or veins based on colour features using a Gaussian Mixture Model, an Expectation-Maximization (GMM-EM) unsupervised classifier, and a quadrant-pairwise approach to validate the success of classification method.
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)