Optic disk feature extraction via modified deformable model technique for glaucoma analysis

A deformable-model based approach is presented in this paper for robust detection of optic disk and cup boundaries. Earlier work on disk boundary detection up to now could not effectively solve the problem of vessel occlusion. The method proposed here improves and extends the original snake, which is essentially a deforming-only technique, in two aspects: knowledge-based clustering and smoothing update. The contour deforms to the location with minimum energy, and then self-clusters into two groups, i.e., edge-point group and uncertain-point group, which are finally updated by the combination of both local and global information. The modifications enable the proposed algorithm to become more accurate and robust to blood vessel occlusions, noises, ill-defined edges and fuzzy contour shapes. The comparative results on the 100 testing images show that the proposed method achieves better success rate (94%) when compared to those obtained by GVF-snake (12%) and modified ASM (82%). The proposed method is extended to detect the cup boundary and then extract the disk parameters for clinical application, which is a relatively new task in fundus image processing. The resulted cup-to-disk (C/D) ratio shows good consistency and compatibility when compared with the results from Heidelberg Retina Tomograph (HRT) under clinical validation.