基于策略演化水平集的医学图像快速分割

医学图像分割在疾病诊断、手术规划和手术引导等实际应用中有着重要的作用。提出了一种基于策略演化水平集算法的快速医学图像分割方法，其策略是通过转换外部轮廓曲线/曲面上的点为内部轮廓曲线/曲面上的点（或做相反操作时），检验能量函数是否减小来决策水平集演化；如此扫描内外轮廓曲线/曲面，使得分割曲线/曲面向目标边界移动。相对于传统水平集算法，该方法不需要解偏微分方程，可极大地减小计算量、提高图像分割的速度。同时，该算法克服了直接计算能量函数水平集方法中存在的问题(陷入局部能量最小和需要扫描整个图像)。最后通过2维和3维医学图像的分割实验，展示了该算法的快速性与精确性。

Medical Image Segmentation Based on the Policy Evolution Level Sets

Medical image segmentation plays an important role in practical applications such as diseases diagnosis, surgical planning, and surgical guidance. In this article, we propose a fast medical image segmentation method based on the policy evolution level sets. Our evolution policy is to calculate the energy directly and check if the energy is decreased when we switch a point from the outer contour to the inner contour (or vice versa). By scan points of inner and outer contour, make the curve or surface move inward or outward to go to the boundary of object. This approach differs from the previous methods in that we do not need to solve PDEs, it can improves the computational speed dramatically. The problem (the local minimums and scan the whole image) of energy function calculate method is solved. At last some segmentation experiments is make on medical image in 2D image and 3D volume, and it demonstrated that our algorithm is fast and precision.