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Retinal area detector from Scanning Laser Ophthalmoscope (SLO) images for diagnosing retinal diseases

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journal contribution
posted on 08.02.2016, 16:47 authored by Muhammad Salman Haleem, Liangxiu Han, Jano van Hemert, Baihua Li, Alan Fleming
© 2014 IEEE. Scanning laser ophthalmoscopes (SLOs) can be used for early detection of retinal diseases. With the advent of latest screening technology, the advantage of using SLO is its wide field of view, which can image a large part of the retina for better diagnosis of the retinal diseases. On the other hand, during the imaging process, artefacts such as eyelashes and eyelids are also imaged along with the retinal area. This brings a big challenge on how to exclude these artefacts. In this paper, we propose a novel approach to automatically extract out true retinal area from an SLO image based on image processing and machine learning approaches. To reduce the complexity of image processing tasks and provide a convenient primitive image pattern, we have grouped pixels into different regions based on the regional size and compactness, called superpixels. The framework then calculates image based features reflecting textural and structural information and classifies between retinal area and artefacts. The experimental evaluation results have shown good performance with an overall accuracy of 92%.

History

School

  • Science

Department

  • Computer Science

Published in

IEEE Journal of Biomedical and Health Informatics

Volume

19

Issue

4

Pages

1472 - 1482

Citation

HALEEM, M.S. ... et al, 2015. Retinal area detector from Scanning Laser Ophthalmoscope (SLO) images for diagnosing retinal diseases. IEEE Journal of Biomedical and Health Informatics, 19 (4), pp.1472-1482

Publisher

© IEEE

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

2015

Notes

This is the accepted manuscript version of the paper. © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

ISSN

2168-2194

Language

en