Automatic optic disk detection and segmentation by variational active contour estimation in retinal fundus images

Abstract

Computer-aided optic disk (OD) detection and segmentation is at the heart of modern fundus image screening systems for early detection and diagnosis of glaucoma and diabetic retinopathy. Algorithms that generalize well on fundus images with diseases, as well as screening images, are of utmost importance. This paper presents a method based on OD homogenization and subsequent contour estimation to address the challenges of OD detection in cases where either the OD boundary is discontinuous or very smooth, due to the presence of disease. This is achieved by local Laplacian filtering-based inpainting of the major vascular structure to complete the OD boundary and gradient-independent active contour estimation for unconstrained OD boundary detection. Experimental evaluation of the proposed method on three benchmark datasets and quantitative comparison with the best performing state-of-the-art methods in terms of four quantitative measures demonstrate its competitive performance and reliability for OD screening.