Generalized Fuzzy Gibbs Random Field and Research on Algorithm for MR Image Segmentation
广义模糊Gibbs随机场与MR图像分割算法研究

A soft image segmentation algorithm based on the generalized fuzzy set is presented in this paper.We incorporate Gibbs random field into the generalized fuzzy set to compensate for the spatial information,and a generalized fuzzy Gibbs random field model is proposed,and the generalized fuzzy Gibbs segmentation algorithm(GFGS) is developed.Each class is considered as a generalized fuzzy class,and the segmented image is regarded as a generalized fuzzy set on the label set in the proposed algorithm.With the proposed algorithm,the outliers in the image data are described by the negative part in the generalized fuzzy membership function,and can be dealt with effectively.Maximum a posteriori(MAP) is used as the statistical segmentation criteria,in which the generalized fuzzy Gibbs random field is used to obtain priori knowledge.Every class center is updated by the centroid of the generalized fuzzy class.Experimental results on both MR real data and the stimulated brain data show that the proposed algorithm is robust,which can filter the noise and partial volume effect significantly.