Optimal surface segmentation in volumetric images - A graph-theoretic approach
Optimal surface segmentation in volumetric images - A graph-theoretic approach
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DOI：
10.1109/tpami.2006.19
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发表时间：
2006-01-01
影响因子：
23.6
通讯作者：
Sonka, M
中科院分区：
文献类型：
--
作者：
Li, K;Wu, XD;Sonka, M
在许多医学图像分析应用中，有效分割体数据集中表示对象边界的全局最优表面是重要且具有挑战性的。我们已经开发出一种最佳的表面检测方法，能够同时检测多个相互作用的表面，其中的最优性是由成本函数控制设计的个别表面和几个几何约束定义的表面光滑度和相互关系。该方法通过将曲面分割问题转化为求一个弧加权有向图的最小s-t割来解决曲面分割问题。该算法具有低阶多项式时间复杂度，计算效率高。它已经在300多个计算机合成体积图像、72个不同尺寸树脂玻璃管的CT扫描数据集和数十个跨越各种成像模式的医学图像上得到了广泛验证。在所有情况下，该方法都产生了高度准确的结果。我们的方法可以很容易地扩展到高维图像分割。
Efficient segmentation of globally optimal surfaces representing object boundaries in volumetric data sets is important and challenging in many medical image analysis applications. We have developed an optimal surface detection method capable of simultaneously detecting multiple interacting surfaces, in which the optimality is controlled by the cost functions designed for individual surfaces and by several geometric constraints defining the surface smoothness and interrelations. The method solves the surface segmentation problem by transforming it into computing a minimum s-t cut in a derived arc-weighted directed graph. The proposed algorithm has a low-order polynomial time complexity and is computationally efficient. It has been extensively validated on more than 300 computer-synthetic volumetric images, 72 CT-scanned data sets of different-sized plexiglas tubes, and tens of medical images spanning various imaging modalities. In all cases, the approach yielded highly accurate results. Our approach can be readily extended to higher-dimensional image segmentation.