改进的基于邻域隶属度约束的FCM图像分割算法

Improved FCM Algorithm Based on Neighboring Membership Constraint for Image Segmentation

传统模糊C均值(FCM:Fuzzy C-Means)聚类算法应用于图像分割时,因对噪声较敏感而达不到理想的分割效果。为此,提出了改进的基于邻域隶属度约束的FCM图像分割算法。该算法通过对FCM目标函数添加空间邻域信息约束隶属度函数,提高对图像噪声的鲁棒性,使分割的结果更加符合期望。实验结果表明,该算法对噪声具有较强的抑制能力,图像分割时能获得较好的分割效果。

When traditional FCM（ Fuzzy C-Means） clustering algorithm applied to image segmentation, because of not combining with image spatial information, it is sensitive to noise, can not produce ideal image segmentation. An improved FCM algorithm based on neighboring membership constraint for image segmentation is proposed, which improves the robustness of image noise through adding the neighborhood spatial information to FCM objective function to constrain the membership function. The results of segmentation are expected. Experimental results indicate that this algorithm has a strong ability to inhibit noise, and can get better segmentation effectiveness.