为提高骨架提取的准确性和连通性，提出了一种利用模板和邻域信息的静脉骨架提取新算法，该算法首先对二值图像进行平滑，并通过自适应方法计算静脉纹路上所有像素点邻域之和，以快速区分出边缘点和中轴点，然后遍历图像找出所有符合中轴点模板的像素点，并删除其中的孤立中轴点之后,得到一些间断的中轴线段，最后从这些中轴线段的端点开始采用最大邻域点跟踪方法提取出静脉骨架。实验结果表明，该算法提取的静脉骨架与中轴线重合且平滑稳定，且具有尺度不变性，角度不变性和良好的抗噪性能，是一种有效的骨架提取算法。

In order to improve connexity and accuracy in skeletonization, a new skeletonization method using template and neighborhood information is proposed. Firstly, we smooth the bilevel image and calculate the neighborhood sum of every point in vein line to differentiate fringe points and medial axis points, then find some medial axis points through the template matching and obtain some discontinuous axle wires by deleting all isolated points；finally, we obtain the vein skeleton by using algorithm of maximal neighborhood point tracking from the ending points of the axle wires. Experimental results show that vein skeleton extracted by the algorithm is smooth and stable, and superposes on actual axle wires. At the same time, the algorithm is provided with scale and rotation invariance，and strong anti-noise ability, so it is effective to extract vein skeleton.