关键词: 模糊C均值聚类, Gibbs随机场, 各向异性Gibbs随机场

Abstract: Fuzzy C-means (FCM) clustering model is one of the well known unsupervised clustering techniques, which has been widely used. However, the classical FCM model only uses the intensity information and no spatial information is taken into account, so it is sensitive to the noise. In order to overcome this limitation of FCM, this paper used the Gibbs theory and the image structure information to construct anisotropic Gibbs random field and incorporated it to FCM model. The new model can reduce the effect of the noise and contain the information of beam structure regions and corner regions. Experiments on the segmentation of brain magnetic resonance images show this model has better performance in image segmentation.

Key words: Fuzzy C-Means, Gibbs random field, anisotropic Gibbs random field