利用空间信息的核模糊C均值聚类算法

模糊聚类,特别是模糊C均值聚类算法（FCM）广泛地运用到图像的分割中。但是传统的算法未对数据对特征进行优化,亦未考虑图像的空间信息,对噪声图像分割不理想。在FCM目标函数中引入核函数,用内核引导距离代替传统的欧式距离,同时考虑到邻近象素的影响,增加了空间约束项,提出了利用空间信息的核FCM算法。通过对模拟图和仿真脑部MR图像的分割实验证明,该算法可以有效的分割含有噪声图像。

Kernel-based fuzzy C-Means clustering algorithm using spatial information

Fuzzy clustering techniques,especially Fuzzy C-Means(FCM) clustering algorithm is a popular model widely used in the segmentation of images.However,as the conventional FCM doesn't optimize data in feature space and doesn't involve any spatial information,it is sensitive to noise.In the paper,we presented a modified kernel-based FCM clustering algorithm for image segmentation.The algorithm by using kernel method the original euclidean distance in the FCM is replaced by a kernel-induced distance.Then,a spatial penalty term is added to the objective function to compensate the influence of the neighboring pixels on the center pixel.The new algorithm is applied to both synthetic images and simulation Magnetic Resonance(MR) images and is shown to be more robust to noise and outlier than the other FCM-based methods.