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

Mohamed Omar, Fouad Khelifi, Muhammad Tahir

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

26 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationProceedings of 2016 International Conference on Control, Decision and Information Technologies (CoDIT)
PublisherIEEE
Pages227-232
ISBN (Print)9781509021888
Publication statusE-pub ahead of print - Oct 2016
EventInternational Conference on Control, Decision and Information Technologies (CoDIT2016) - Saint Julian's, Malta
Duration: 20 Oct 2016 → …

Conference

ConferenceInternational Conference on Control, Decision and Information Technologies (CoDIT2016)
Period20/10/16 → …

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