Skeleton Extraction Using SSM of the Distance Transform

This paper proposes a novel approach of skeletonization for both binary and gray-scale images based on the Skeleton Strength Map (SSM) calculated from Euclidean distance transform. The distance transform is firstly computed based on the boundary edges, and then its gradient vector field is calculated. After that, the isotropic diffusion is performed on the gradient vector field and the SSM is computed from the diffused vector field. It has two advantages that make it useful for skeletonization: 1) For pixels that are away from the center of object, the SSM value decays very fast; 2) the SSM provides a very effective way for noise smoothing. For binary images, visual skeleton can be obtained directly from the SSM by selecting local maxima and connecting them with Dijkstra’s algorithm. For gray-scale images, since a complete contour can not be obtained usually, we compute the SSM from edge images first. Even for the clutter images, we can obtain clean skeleton with the aid of high level information. Experiments show that, our method can achieve good performance for both binary images and gray-scale images.