Neovascularization detection on retinal images

Abstract

Proliferative Diabetic Retinopathy (PDR) is characterized by the growth of new abnormal, thin blood vessels called neovascu-larzation that spread along the retinal surface. An automated computer aided diagnosis system needs to identify neovasculars for PDR screening. Retinal images are often noisy and poorly illuminated. The thin vessels mostly appear to be disconnected and are inseparable from the background. This paper proposes a new method for neovascularization detection on retinal images. Blood vessels are extracted as thick, medium and thin types using multilevel thresholding on matched filter response. The total mutual information between the vessel density and the tortuosity of the thin vessel class is maximized to obtain the optimal thresholds to classify the normal and the abnormal vessels. Simulation results demonstrate that the proposed method outperforms the existing ones for neovascularization detection with an average accuracy of 97.54%.