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

Abstract

Automated quantification of blood vessels in human retina is the fundamental step in designing any computer-aided diagnosis system for ophthalmic disorders. Detection and analysis of variations in blood vessels can be used to diagnose several ocular diseases like diabetic retinopathy. Diabetic Retinopathy is a progressive vascular disorder caused due to variations in blood vessels of retina. These variations bring different abnormalities like lesions, exudates, and hemorrhages in human retina which make the vessel detection problematic. Therefore, automated retinal analysis is required to cater the effect of lesions while segmenting blood vessels. The proposed framework presents two improved approaches to carry out vessel segmentation in the presence of lesions. The paper mainly aims to extract true vessels by reducing the effect of abnormal structures significantly. First method is a supervised approach which extracts true vessels by performing region based analysis of retinal image, while second method intends to remove lesions before extracting blood vessels by using an inpainting technique. Both methods are evaluated on STARE and DRIVE and on our own database AFIO. Experimental results demonstrate the excellence of the proposed system.