为了对图像进行准确、可靠的分割,提出了一种基于广义模糊集的软分割算法,并将广义模糊集和G ibbs场结合起来,提出了广义模糊G ibbs随机场模型,同时建立了广义模糊G ibbs分割(GFGS)算法。该算法是首先把每一个分割类看作是广义模糊类,并以最大后验概率(MAP)为判别准则来决定每一个像素值的归类以及它属于该类的隶属度;然后用广义隶属度函数中负的部分来刻划数据中的异常值,使得该算法能有效地处理异常值;最后用该模糊类的质心来更新类的中心,并以人脑的仿真图像和临床MR图像进行了实验。实验结果表明,该算法能有效地滤除噪声和处理部分容积效应,是一个分割能力强、稳健性好的算法。

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.