We present a technique for segmentation of optic cup based on the structural features found in blood vessels surrounding the optic cup region. The advantage of using such features is that they are robust to variations in the properties of the fundus image such as brightness, contrast, etc. The main features used in the technique are vessel bends (also called as landmark points or kinks), which are identified by applying the Harris corner detection algorithm on the optic disc region, followed by a Voronoi image decomposition. Pratt's circle fitting algorithm is employed on the extracted landmark points to segment the optic cup region. The proposed technique is validated on a total of 191 images taken from publicly available fundus image datasets, namely, Drishti-GS and MESSIDOR. Performance metrics such as sensitivity, specificity, accuracy, Jaccarďs index, and Dice coefficient are computed to be 85%, 97%, 96%, 69.5%, and 81%, respectively, which indicates that the proposed technique for optic cup segmentation is competitive with the state-of-the-art methods.