Design and implementation of a unique blood-vessel detection algorithm towards early diagnosis of diabetic retinopathy

Diabetic retinopathy (DR), a major complication of diabetes and the leading cause of new cases of blindness among adults, can be cured by the early and precise detection of the disease. An important aspect of DR is the micro-vascular changes that cause detectable changes in the appearance of retinal blood vessels. In this paper, we propose a new blood-vessel detection technique in retinal images, based on the regional recursive hierarchical decomposition using quadtrees and post-filtration of edges. We exploit the fact that in retinal images, the blood vessels appear as focal and/or penumbral blurred edges, which can be characterized by an estimable intensity gradient, which also serves in dismissing false alarms to a large extent. Our technique provides information on retinal blood vessel morphology that can be calibrated to normal expected blood vessel diameters and which can detect fine blood vessel anomalies that characterize the blood vessel pathology and hence aid early detection of diabetic retinopathy.