Blood Vessel Segmentation in Pathological Retinal Image

Abstract

Retinal vessel segmentation is a fundamental aspect of the automatic retinal image analysis. The attributes of retinal blood vessels, such as width, tortuosity and branching pattern, play an important role in clinical diagnose. However, the edges of optic disk, fovea and edges of pathological areas have negative effects on vessel segmentation and few people focus on this problem. In this paper, we proposed a supervised method for retinal blood vessel segmentation. We design features based on local area shape combined with multi-scale local statistical features based on gray level and morphology features to solve the problems. Then, a support vector classifier is used for classification. Our algorithm is analyzed on two publicly available databases, called DRIVE and STATE. The accuracy of our method on both testing set is better than the 2nd human observer. The performance in pathological retinal images is satisfactory.