Textural and Intensity Feature Based Retinal Vessels Classification for the Identification of Hypertensive Retinopathy

Abstract

Hypertensive retinopathy is a retinal disease which results as a consequence of high blood pressure. Its early detection is necessary in reducing the likelihood of permanent visual damage. The percentage of people suffering from Hypertension is high, so it is required to develop a system which automatically detects the presence of this disease. High blood pressure damages retinal vessels and due to which arteries width is reduced. This damage can be analyzed by extracting the blood vessels, classifying the segmented vessels into veins and arteries and finally computing their Arteriovenous Ratio, which is an important measure to establish whether a person is suffering from Hypertensive Retinopathy or not. This research presents a technique for automatic classification of blood vessels of retina using different classifiers and the performance of each classifier is compared on same feature set. A novel combination of features is used for classification of vessels, which is an essential step for calculation of Arteriovenous Ratio and subsequently the detection of Hypertensive Retinopathy. MATLAB has been used for this research. The results that are achieved using the proposed feature set show's 89% accuracy.