三维图像中阶梯型边缘曲面的追踪算法

A Novel Algorithm for Tracking Step-Like Edge Surfaces within 3D Images

首先选取能够代表不同边缘曲面的种子立方体,并根据立方体面的连通性追踪出由于除躁而丢失的边缘曲面.在追踪过程中,采用具有高效空间复杂度和时间复杂度的基于动态链栈的非递归深度优先遍历方法.实验结果表明,文中算法克服了边缘曲面抽取算法抽取的边缘曲面有洞的缺陷.与等值面抽取算法相比,该算法能够获得更高精度的边缘曲面的多边形曲面模型.

Based on 3D regional growing method, a novel surface tracking algorithm is proposed. Firstly, the seeding cubes, representative to different edge surfaces, are selected. Then, the edge surfaces lost during the de-noising step are recovered based on the connectivity of edge surfaces. Based on dynamic link stack, the DFS, a time and space efficient traversal algorithm, is employed during the tracking process. The experimental results show that the proposed tracking algorithm can overcome the defect of the traditional edge detecting methods which usually produce holes in the extracted surface. In addition, the proposed algorithm is shown to be able to reconstruct more accurate deformable surface models of edge surface in comparison with the equivalent surface extraction algorithm.