Detection and classification of retinal lesions for grading of diabetic retinopathy

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dc.contributor.author M. Usman Akram
dc.contributor.author Shehzad Khalid
dc.contributor.author Anam Tariq
dc.contributor.author Shoab A Khan
dc.contributor.author Farooque Azam
dc.date.accessioned 2017-12-26T11:52:45Z
dc.date.available 2017-12-26T11:52:45Z
dc.date.issued 2014
dc.identifier.uri http://hdl.handle.net/123456789/5191
dc.description.abstract Diabetic Retinopathy (DR) is an eye abnormality in which the human retina is affected due to an increasing amount of insulin in blood. The early detection and diagnosis of DR is vital to save the vision of diabetes patients. The early signs of DR which appear on the surface of the retina are microaneurysms, haemorrhages, and exudates. In this paper, we propose a system consisting of a novel hybrid classifier for the detection of retinal lesions. The proposed system consists of preprocessing, extraction of candidate lesions, feature set formulation, and classification. In preprocessing, the system eliminates background pixels and extracts the blood vessels and optic disc from the digital retinal image. The candidate lesion detection phase extracts, using filter banks, all regions which may possibly have any type of lesion. A feature set based on different descriptors, such as shape, intensity, and statistics, is formulated for each possible candidate region: this further helps in classifying that region. This paper presents an extension of the m-Mediods based modeling approach, and combines it with a Gaussian Mixture Model in an ensemble to form a hybrid classifier to improve the accuracy of the classification. The proposed system is assessed using standard fundus image databases with the help of performance parameters, such as, sensitivity, specificity, accuracy, and the Receiver Operating Characteristics curves for statistical analysis. en_US
dc.language.iso en en_US
dc.publisher Bahria University Islamabad Campus en_US
dc.relation.ispartofseries ;http://dx.doi.org/10.1016/j.compbiomed.2013.11.014
dc.subject Department of Computer Engineering CE en_US
dc.title Detection and classification of retinal lesions for grading of diabetic retinopathy en_US
dc.type Article en_US


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