Optic Disc Segmentation using Vessel In-painting and Random Walk Algorithm

Abstract

Optic disc segmentation in fundus images is a fundamental step for the detection of retinal diseases like glaucoma. Glaucoma effects the parts of retina inside and around optic disc leading in manifestation of various structural abnormalities. The work proposed in this paper presents an efficient optic disc segmentation methodology using random walk algorithm. Random walk algorithm divides the image into foreground and background regions based on the initial seeds. The optic disc is segmented by using random walk with weights calculated on the color similarity and dissimilarity among neighborhood pixels. The proposed method is tested on fundus images of publicly available Drishti-GS1 database. The final performance is evaluated with respect to precision, sensitivity, specificity, F-score, jaccard, dice, and mean absolute distance measures and compared with other optic disc segmentation approaches presented in the literature.