Automatic notch detection in retinal images

Abstract

This paper presents a new method to detect notching in the optic cup using retinal images. Optic cup notching is an important feature in differentiating normal from glaucomatous eyes. The proposed notching detection method comprises four steps: disc and vessel segmentation, vessel bend detection at key regions, feature points selection and automatic classification. The key step of vessel bend detection involves computing the local curvature of the vessels, then ranking them based on the angle of vessel bend and the local gradient in the neighborhood region. The algorithm was tested on a set of color fundus images and achieved a notching detection rate of 88.9%, a false alarm rate of 4.0%, and an overall accuracy of 95.4%.