Automated Optic Disk Segmentation Via a Modified Snake Technique

Optic disk is one of the main components on retina. It is an indicator of various ophthalmic pathologies. This paper presents a novel algorithm to segment the optic disk, based on snake model. The proposed method improves and extends original snake technique in two aspects: clustering and smoothing update. The contour first deforms to the location with the minimal energy, and then self-separates into edge-point group or uncertain-point group by means of weighted k-means algorithm. The contour points are finally updated by variable updating sample numbers. These modifications directly solve the blood vessel problem, which has not been effectively solved in the earlier work on disk boundary detection up to now. The comparative results on the 100 testing images shown that the proposed method achieves better success rate (94%) when compared to those obtained by GVF-snake (12%) and modified ASM method (82%)