Loughborough University
Leicestershire, UK
LE11 3TU
+44 (0)1509 263171
Loughborough University

Loughborough University Institutional Repository

Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/20268

Title: Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: a review
Authors: Haleem, Muhammad Salman
Han, Liangxiu
van Hemert, Jano
Li, Baihua
Keywords: Automatic feature detection
Retinal image analysis
Fundus image
Retinal diseases analysis
Feature extraction
Issue Date: 2013
Publisher: © Elsevier
Citation: HALEEM, M.S. ... et al, 2013. Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: a review. Computerized Medical Imaging and Graphics, 37 (7-8), pp.581-596
Abstract: Glaucoma is a group of eye diseases that have common traits such as, high eye pressure, damage to the Optic Nerve Head and gradual vision loss. It affects peripheral vision and eventually leads to blindness if left untreated. The current common methods of pre-diagnosis of Glaucoma include measurement of Intra-Ocular Pressure (IOP) using Tonometer, Pachymetry, Gonioscopy; which are performed manually by the clinicians. These tests are usually followed by Optic Nerve Head (ONH) Appearance examination for the confirmed diagnosis of Glaucoma. The diagnoses require regular monitoring, which is costly and time consuming. The accuracy and reliability of diagnosis is limited by the domain knowledge of different ophthalmologists. Therefore automatic diagnosis of Glaucoma attracts a lot of attention.This paper surveys the state-of-the-art of automatic extraction of anatomical features from retinal images to assist early diagnosis of the Glaucoma. We have conducted critical evaluation of the existing automatic extraction methods based on features including Optic Cup to Disc Ratio (CDR), Retinal Nerve Fibre Layer (RNFL), Peripapillary Atrophy (PPA), Neuroretinal Rim Notching, Vasculature Shift, etc., which adds value on efficient feature extraction related to Glaucoma diagnosis. © 2013 Elsevier Ltd.
Version: Accepted for publication
DOI: 10.1016/j.compmedimag.2013.09.005
URI: https://dspace.lboro.ac.uk/2134/20268
Publisher Link: http://dx.doi.org/10.1016/j.compmedimag.2013.09.005
ISSN: 0895-6111
Appears in Collections:Published Articles (Computer Science)

Files associated with this item:

File Description SizeFormat
Muh+CMIG-Review2014-accepted.pdfAccepted version1.88 MBAdobe PDFView/Open


SFX Query

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.