基于模糊隶属度空间约束的FCM图像分割

FCM Image Segmentation Based on the Spatial Restrained Fuzzy Membership

针对模糊C均值(FCM)算法对噪声较为敏感,提出了基于隶属度空间约束的FCM图像分割方法,该方法将隶属度空间约束关系引入到FCM目标函数,在新的目标函数中,像素点的隶属度不仅仅与FCM标准目标函数有关,还与其领域像素点的隶属度有关。由于融合了图像像素点的空间信息,反映了领域像素点间的隶属度关联信息,因此该算法具有较强的抗噪性能。

The conventional FCM algorithm is sensitive to noise.To overcome the defect of FCM algorithm,we proposed a novel regularized fuzzy c-means algorithm for image segmentation based on the spatial restrained fuzzy membership.This approach introduced the relation of space restrained fuzzy membership into the modified FCM objective function.In the new objective function,the membership generated by the proposed algorithm is a product of two terms,the first term is the standard FCM membership responsible for data partitioning,and the second term is a robust constraint of neighborhood membership.Due to the introduction of the spatial information and the relation of neighborhood membership during the process of clustering,the algorithm has good performance in resisting noises.