关键词: 图像分割, 核方法, 模糊C均值聚类算法, 图像的空间信息

Abstract: 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.

Key words: image segmentation, kernel method, Fuzzy C-Means algorithm（FCM）, spatial information of image