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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/20260

Title: Retinal area detector from Scanning Laser Ophthalmoscope (SLO) images for diagnosing retinal diseases
Authors: Haleem, Muhammad Salman
Han, Liangxiu
van Hemert, Jano
Li, Baihua
Fleming, Alan
Keywords: Scanning Laser Ophthalmoscope
Retinal image analysis
Feature selection
Retinal artefacts extraction
Issue Date: 2015
Publisher: © IEEE
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
Abstract: © 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%.
Description: 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.
Version: Accepted for publication
DOI: 10.1109/JBHI.2014.2352271
URI: https://dspace.lboro.ac.uk/2134/20260
Publisher Link: http://dx.doi.org/10.1109/JBHI.2014.2352271
ISSN: 2168-2194
Appears in Collections:Published Articles (Computer Science)

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