Medical Image Segmentation Based on the Policy Evolution Level Sets
基于策略演化水平集的医学图像快速分割

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.