改进的基于遗传模糊C均值聚类的图像分割算法

模糊C均值（Fuzzy C-Means，FCM）聚类算法已广泛应用于图像分割领域，其本质是一种局部搜索算法，采用迭代爬山算法寻找最优解，对初始聚类中心敏感，很容易陷入局部极优值，且没有考虑图像的空间邻域信息，对噪声敏感。本文提出了改进的基于遗传模糊聚类的图像分割算法，利用遗传算法的全局寻优能力来克服FCM算法容易陷入局部极优值问题；并在FCM算法的目标函数中添加空间邻域信息来约束隶属度函数从而提高对噪声的鲁棒性，使分割更加符合期望。实验结果表明本文算法的有效性，图像分割时具有较强的抗噪能力和较好的分割效果。

An Improved Image Segmentation Algorithm Based on Genetic Fuzzy C-Means Clustering

Fuzzy C-Means （FCM） clustering algorithm has been widely used in the field of image segmentation, be-cause its nature is a local search algorithm. As using iterative climbing to find the optimal solution,so it is sensitive to initial cluster center and easy to fall into local excellent value. Without considering the image spatial information, it is sensitive to noise. Thus,an improved image segmentation algorithm based on genetic Fuzzy C-Means clustering is pro-posed, using genetic algorithm’s global optimization ability to overcome the problem of falling into local optimal value. It improve the robustness of image noise through adding the neighborhood spatial information to FCM objective function to constrain the membership function,so that the results of segmentation are more in line with expectation. Experimen-tal results indicate that the effectiveness of this algorithm can have a strong anti-noise ability and get better segmenta-tion effectiveness.