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

PixelBNN: Augmenting the PixelCNN with batch normalization and the presentation of a fast architecture for retinal vessel segmentation

TL;DR: PixelBNN as discussed by the authors is a state-of-the-art method for segmentation of fundus morphologies, which is trained, tested and cross tested on the DRIVE, STARE and CHASE\_DB1 retinal vessel segmentation datasets.
Journal ArticleDOI

A Review on Glaucoma Disease Detection Using Computerized Techniques

TL;DR: A comprehensive overview of various existing techniques that use machine learning to detect and diagnose glaucoma based on fundus images is provided in this article, where readers can understand the challenges of image processing and machine learning stand-point and will be able to identify gaps in current research.
Journal ArticleDOI

Left-invariant evolutions of wavelet transforms on the similitude group

TL;DR: In this paper, a continuous wavelet transform on the similitude group, SIM(2), is proposed to map the space of images onto a reproducing kernel space, allowing to robustly relate Euclidean invariant operators on images to left-invariant operator on multiple-scale orientation scores.
Journal ArticleDOI

Lung segmentation refinement based on optimal surface finding utilizing a hybrid desktop/virtual reality user interface

TL;DR: A hybrid desktop/virtual reality user interface was developed for efficient interaction with the segmentations utilizing state-of-the-art stereoscopic visualization technology and advanced interaction techniques and is generally applicable to segmentation problems beyond lung segmentation in CT scans as long as the underlying segmentation utilizes the OSF framework.
Journal ArticleDOI

Morphological operations with iterative rotation of structuring elements for segmentation of retinal vessel structures

TL;DR: A hysteresis thresholding, guided by some morphological operations has been employed to obtain the binary image excluding other unwanted areas of the blood vessel from its background, and a maximum accuracy and an average accuracy of 95.65 and 94.31% respectively have been achieved.
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)