基于Gibbs场与模糊C均值聚类的脑MR图像分割

模糊C均值聚类是一种经典的非监督聚类模型，已成功用于很多领域。但该算法对图像噪声比较敏感。为此，利用Gibbs理论和图像结构信息构造各向异性Gibbs随机场，并将其引入到FCM框架中，完善其分类效果，使其在克服噪声影响的同时还能够保持细长拓扑结构区域信息以及角点区域信息。应用于脑MR图像分割，实验表明新算法可以得到较好的分类结果。

Brain MR image segmentation based on anisotropic Gibbs random field and fuzzy C-means clustering model

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.