水平集方法在医学图像分割中的应用

水平集方法已广泛应用于医学图像分割中，该方法将界面看成高一维空间中的某一函数谚（称为水平集函数）的零水平集，同时界面的演化也扩充到高一维的空间中。其核心思想是利用水平集理论求解能量泛函的最小值．即当能量达到最小值时的曲线位置就是目标轮廓所在；有效解决曲线演化过程中的拓扑变化问题。介绍水平集发展过程中几个经典模型的基本思想，并通过大量实验证明该方法在医学图像分割中的适用性及有效性。

Application of Level-Set Method in Medical Image Segmentation

Evolution of the level set method has been widely applied in medical image segmentation, which will screen as a higher-dimensional space in a certain function （called the level set function） of the zero level set, while also expanding the interface to a higher-dimen- sional space. The core idea is to put the level set on the mathematical theory minimum energy functional solution process, when the curve reaches a minimum energy position is where the target contour lies, effectively solving the problem of topology change in the evolu- tion of the curve which has no proper algorithm to solve previously. Describes several classical models＂ basic idea of the level set devel- opment and by a large number of experiments proves the applicabilitv and effectiveness of this method in medical image segmentation.