Detection and Classification of Retinal Fundus Images Exudates using Region based Multiscale LBP Texture Approach

Omar, Mohamed, Khelifi, Fouad and Tahir, Muhammad (2016) Detection and Classification of Retinal Fundus Images Exudates using Region based Multiscale LBP Texture Approach. In: Proceedings of 2016 International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, pp. 227-232. ISBN 9781509021888

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Abstract

Diabetic retinopathy (DR) is one of the most important cause of vision loss in diabetic patients. The most primary sign of DR is the presence of exudates, and detecting these in early screening is crucial in preventing vision loss. This paper proposes a system for automatic exudate detection using a combination of texture features, extracted from different local binary pattern (LBP) variants, with an artificial neural network (ANN) classifier. The publicly available database DIARETDB0 of colour fundus images was used for testing purposes and the values of sensitivity, specificity and accuracy found were 98.68%, 94.81 % and 96.73% respectively for the neural network based classification. These results have also been shown to outperform existing work.

Item Type:	Book Section
Uncontrolled Keywords:	DIARETDB0, Diabetic retinopathy (DR), exudates, fundus image, Local Binary Pattern (LBP), Radial Basis Function, K-Nearest Neighbour
Subjects:	B800 Medical Technology G900 Others in Mathematical and Computing Sciences
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Ay Okpokam
Date Deposited:	24 Nov 2016 11:39
Last Modified:	12 Oct 2019 22:28
URI:	http://nrl.northumbria.ac.uk/id/eprint/28606

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