Euclidean distance-ordered thinning for skeleton extraction
Le Zhang, Qing He, Shin-ichi Ito, Kenji Kita · 2010
The skeleton is an important feature for the representation of a shape in image analysis. In this paper, we propose a novel Euclidean distance-ordered thinning algorithm for skeleton extraction. We first give the deletion templates which can determine a given pixel to be safely deleted or not from the pattern of its 8-neighbors. Then we delete the points which satisfy the deletion templates until there is no point that can be deleted in the linked lists of ascending order. Finally, the skeleton of the object is obtained. The experiment results show that the algorithm is able to extract the connected and one-pixel wide skeleton that can correctly preserve the topology of the object. Furthermore, the extracted skeleton locates on the accurate position and it is insensitive to boundary noise.