基于C-V模型的改进快速水平集图像分割法

针对水平集方法计算复杂度高，无法满足实时系统要求的缺陷，提出一种改进的快速水平集算法。该算法对快速水平集算法进行简化，采用单链表表示轮廓曲线。利用C-V模型的二值拟合项来设计曲线演化的速度函数，保留了C-V模型的全局优化特性。还给出了一个基于单链表中轮廓点个数变化的水平集演化终止准则。该算法不仅明显提高了分割速度，且对噪声图像也能实现高效的分割。

Improved image segmentation method based on fast level set and C-V model

Aim at solving the problem that the high computational complexity of level set methods excludes themselves from many real-time applications, an improved image segmentation method based on the fast level set algorithm is proposed in this paper. The proposed algorithm adopts an improved fast level set with a single list to realize the curve evolution, and it uses the binary fitting terms of the C-V model to design the speed function of curve evolution, preserving the global optimization characteristic of the C-V model. In addition, a termination criterion based on the number changing of contour points in the single list is proposed to ensure that the evolving curve can automatically stop on the true boundaries of objects. Experimental results show that the proposed algorithm can significantly improve the segmentation speed and can efficiently segment the noisy images.