This paper presents a new algorithm for image segmentation, which combines Hidden Markov Random Field (HMRF) and Gaussian Regression Model (GRM) to Fuzzy C-Means (FCM) clustering. The proposed algorithm uses the KL (Kullback-Leibler) information to regularize the objective function of FCM, and then utilizes HMRF and GRM to model the neighborhood relationship of the label field and feature field, respectively. The HMRF model characterizes the neighborhood relationship through its prior probability, while the GRM is established under the assumption that a pixel has the same label with its neighbors. This paper takes some experiments with the proposed algorithm and other FCM based algorithms on the simulation image, real SAR image and texture image, respectively, and the accuracy of segmentation is evaluated. By comparing the results of them, the proposed algorithm can provided more accuracy segmentation result.