为了准确有效地提取三维图像的骨架，提出一种新的并行三维图像骨架化算法。首先对原图像进行距离变换，然后迭代并行细化。每轮迭代分为6个子迭代,每个子迭代处理一类边界点，标记满足初选规则的前景点。在6个子迭代结束后，按距离值升序复核初选标记点，批量删除满足条件的点，完成一轮迭代。重复这一过程直到没有点被删除，得到最终骨架。将该算法应用于二维和三维图像，得到了与人类视觉感知相一致的目标骨架。新算法能够保证得到的骨架的连通性和拓扑结构，通过计算骨架点的距离值验证了其居中性，反映了目标的本质结构特征。

In order to accurately and efficiently extract skeletons from 3D images, a new parallel 3D skeletonization algorithm is proposed. The distances of original images are firstly transformed, and then iterative parallel thinning is performed. Each iteration is divided into 6 sub iterations, one sub iteration is used to process a kind of boundary points,and the foreground points satisfying the primary selection rule are labeled. After 6 sub iterations are completed, labeled points are rechecked in an ascending distance order,and those who satisfy thinning conditions are deleted. This process is repeated until there is no point deleted. This algorithm is applied to 2D and 3D images, obtained skeletons are consistent with that by the visual apperception. This algorithm guarantees the connectivity and topology of extracted skeletons, and its median can be validated by caculating the distance values of skeleton points,which reflects the essence characteristics of the object.