A Snake-Based Segmentation Algorithm for Objects with Boundary Concavities

Concavities in the boundary of an object pose a challenge to active contour (snake) methods. In this paper, we present a snake-based scheme for efficiently detecting contours of objects with boundary concavities. The proposed method is composed of two steps. First, the object's boundary is detected using the proposed snake model. Second, snake points are optimized by inserting new points and deleting unnecessary points to better describe the object's boundary. The proposed algorithm can successfully extract objects with boundary concavities, and is insensitive to the number of initial snake points. Experimental results have shown that our algorithm produces more accurate segmentation results than the conventional algorithm