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Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: a review

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journal contribution
posted on 09.02.2016, 12:54 by Muhammad Salman Haleem, Liangxiu Han, Jano van Hemert, Baihua LiBaihua Li
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.

History

School

  • Science

Department

  • Computer Science

Published in

Computerized Medical Imaging and Graphics

Volume

37

Issue

7-8

Pages

581 - 596

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

Publisher

© Elsevier

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2013

ISSN

0895-6111

eISSN

1879-0771

Language

en