An image segmentation method based on robust regional constraint FCM (Fuzzy C-Means) is proposed

which combines hidden Markov random filed (HMRF) model with FCM.In order to improve the performance of the proposed method

the consistency of superpixels of the input image is adaptively used as a priori in clustering process.The proposed method first obtains the superpixels of the image

and for each superpixel

calculates a contribution of each pixel to the superpixel and the contributions are used to compute the superpixel's membership functions.And then the pointwise prior probabilities of pixels are calculated with pixel-level membership function or region-level membership function according to whether the superpixel to which the pixels belong has the dominant label.The use of region-level membership function is to guide the direction of clustering optimization

and thus there are some unused labels which are removed in the iteration process.Finally

the segmentation result is obtained after iteration stop.Experimental results demonstrate the good performance of the proposed method.