Classification of vessels as arteries verses veins using hybrid features for diagnosis of hypertensive retinopathy

Hypertensive retinopathy is an eye disease which causes to loss of human vision in intensive scenarios. Retinal blood vessels are highly affected by this disease. Ratio between diameters of arterioles and venules is the only measure to detect the abnormalities in vessels. The proposed system identify the symptoms of abnormal vascular structure like arteriolar narrowing. AVR calculation is the main part of the proposed system. Some of the steps in this process are background preprocessing, vessels segmentation, OD segmentation, vessels classification and vessels width calculation which estimates the AVR. This proposed system basically classify the vessels into arteries and veins which is the main part of the AVR measurements. Higher accuracy in vessels classification is observed by using the SVM as classifier. Accurate classification leads to the best grading of different stages of hypertensive retinopathy in fundus images. Three dataset of fundus images are tested by this algorithm which are INSPIRE-AVR, VICAVR and a local dataset. Results obtained after testing are compared with other techniques and shows the improvements.