基于文化算法的C-V水平集图像分割

针对基于梯度变化的水平集图像分割对噪声敏感、不能很好地保持图像中的边缘信息、分割结果依赖初始参数、取得最优解时不能及时结束等问题，提出了一种基于文化算法的水平集图像分割算法，将文化算法应用到C-V(Chan-Vese)水平集模型之中，实现了水平集模型图像分割参数的自动选取，通过信度空间的形势知识和规范知识不断优化指导种群进化，并通过判定图像熵适应度值的变化适时终止分割过程。实验结果表明，本文方法能够准确分割出医学图像的病变区域，在抗噪声性能和分割效率方面明显优于常规方法。

C-V level set image segmentation based on cultural algorithm

The gradient level set model has several disadvantages,it is sensitive to noise,it is unsatisfied on keeping the image edge,the segmentation result depends on initially parameters,and the segmentation process can not stop when obtaining the optimal solution.In order to solve the problems,a level set image segmentation algorithm based on cultural algorithm is proposed and the cultural algorithm is applied to the C-V(Chan-Vese) level set model.Firstly the parameter selection is automatically realized.Secondly the situational knowledge and the normative knowledge are used to guide the population evolution in belief space.And finally the image segmentation process is timely stopped by judging a change in the image entropy fitness value.The experimental results show that the algorithm is superior to conventional methods in the anti-noise performance and segmentation efficiency,and can accurately segment the medical image lesion areas.