Palágyi Kálmán, Tschirren Juerg, Hoffman Eric A, Sonka Milan
Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, USA.
Comput Biol Med. 2006 Sep;36(9):974-96. doi: 10.1016/j.compbiomed.2005.05.004. Epub 2005 Aug 1.
A method for computationally efficient skeletonization of three-dimensional tubular structures is reported. The method is specifically targeting skeletonization of vascular and airway tree structures in medical images but it is general and applicable to many other skeletonization tasks. The developed approach builds on the following novel concepts and properties: fast curve-thinning algorithm to increase computational speed, endpoint re-checking to avoid generation of spurious side branches, depth-and-length sensitive pruning, and exact tree-branch partitioning allowing branch volume and surface measurements. The method was validated in computer and physical phantoms and in vivo CT scans of human lungs. The validation studies demonstrated sub-voxel accuracy of branch point positioning, insensitivity to changes of object orientation, and high reproducibility of derived quantitative indices of the tubular structures offering a significant improvement over previously reported methods (p<<0.001).
报道了一种用于三维管状结构的高效计算骨架化方法。该方法专门针对医学图像中血管和气道树结构的骨架化，但具有通用性，适用于许多其他骨架化任务。所开发的方法基于以下新颖概念和特性：快速曲线细化算法以提高计算速度、端点重新检查以避免产生虚假侧支、深度和长度敏感修剪以及精确的树枝分割以允许进行分支体积和表面积测量。该方法在计算机和物理模型以及人体肺部的体内CT扫描中得到了验证。验证研究表明，分支点定位具有亚体素精度，对物体方向变化不敏感，并且管状结构衍生定量指标具有高重现性，与先前报道的方法相比有显著改进（p<<0.001）。
