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Automated quantification of blood vessels in human retina is thefundamental step in designing any computer-aided diagnosis systemfor ophthalmic disorders. Detection and analysis of variations inblood vessels can be used to diagnose several ocular diseases likediabetic retinopathy. Diabetic Retinopathy is a progressive vasculardisorder caused due to variations in blood vessels of retina. Thesevariations bring different abnormalities like lesions, exudates, andhemorrhages in human retina which make the vessel detectionproblematic. Therefore, automated retinal analysis is required tocater the effect of lesions while segmenting blood vessels. Theproposed framework presents two improved approaches to carry outvessel segmentation in the presence of lesions. The paper mainlyaims to extract true vessels by reducing the effect of abnormalstructures significantly. First method is a supervised approachwhich extracts true vessels by performing region based analysis ofretinal image, while second method intends to remove lesions beforeextracting blood vessels by using an inpainting technique. Bothmethods are evaluated on STARE and DRIVE and on our own database AFIO. Experimental results demonstrate the excellence of the proposed system

Removal of False Blood Vessels Using Shape Based Features and Image Inpainting