Zhou Yong, Toga Arthur W
IEEE Trans Vis Comput Graph. 1999 Jul;5(3):196-209. doi: 10.1109/2945.795212.
Skeletonization promises to become a powerful tool for compact shape description, path planning, and other applications. However, current techniques can seldom efficiently process real, complicated 3D data sets, such as MRI and CT data of human organs. In this paper, we present an efficient voxel-coding based algorithm for Skeletonization of 3D voxelized objects. The skeletons are interpreted as connected centerlines. consisting of sequences of medial points of consecutive clusters. These centerlines are initially extracted as paths of voxels, followed by medial point replacement, refinement, smoothness, and connection operations. The voxel-coding techniques have been proposed for each of these operations in a uniform and systematic fashion. In addition to preserving basic connectivity and centeredness, the algorithm is characterized by straightforward computation, no sensitivity to object boundary complexity, explicit extraction of ready-to-parameterize and branch-controlled skeletons, and efficient object hole detection. These issues are rarely discussed in traditional methods. A range of 3D medical MRI and CT data sets were used for testing the algorithm, demonstrating its utility.
骨架化有望成为用于紧凑形状描述、路径规划及其他应用的强大工具。然而，当前技术很少能有效处理真实、复杂的三维数据集，比如人体器官的磁共振成像（MRI）和计算机断层扫描（CT）数据。在本文中，我们提出了一种基于体素编码的高效算法，用于对三维体素化对象进行骨架化处理。骨架被解释为相连的中心线，由连续聚类的中间点序列组成。这些中心线最初被提取为体素路径，随后进行中间点替换、细化、平滑及连接操作。针对这些操作中的每一项，均以统一且系统的方式提出了体素编码技术。除了保留基本的连通性和中心性外，该算法的特点还包括计算简单直接、对对象边界复杂性不敏感、能明确提取可直接参数化且可控制分支的骨架以及高效的对象孔洞检测。这些问题在传统方法中很少被讨论。使用了一系列三维医学MRI和CT数据集来测试该算法，证明了其效用。
