Membership regularized fuzzy clustering methods apply an important prior that neighboring data points should possess similar memberships according to an affinity/similarity matrix. As result, they achieve good performance in many data mining tasks. However, these clustering methods fail to take full advantage of image spatial information in their regularizations. Their performance in image segmentation problem is still not promising. In this paper, we first focus on building a novel affinity matrix to store and present the image spatial information as the prior to help membership regularized fuzzy clustering methods get excellent segmentation results. To this end, the affinity value is calculated by the fusion of pixel and region level information to present the subtle relationship of two points in an image. In addition, to reduce the impact of image noise, we use fixed cluster centers in the iteration of algorithm, thus, the updating of membership values is only guided by the prior of fused information. Experimental results over synthetic and real image datasets demonstrate that the proposed method shows better segmentation results than state-of-the-art clustering methods.