A Fuzzy Clustering Image Segmentation Algorithm with Double Neighborhood System Combined with Markov Gaussian Model

Traditional fuzzy C-means（FCM） algorithm is sensitive to noises and outliers since it uses the Euclidean distance to describe the dissimilarity between the pixel and its cluster. In this paper, we establish a double neighborhood system on the feature field and label field by the Markov-Gaussian model and propose a fuzzy clustering image segmentation algorithm on this basis. First, the characteristic of Markov model is used to construct an energy function of neighbor pixels on the label field to ensure that pixels in the same neighborhood system could have a higher probability to be in the same cluster than pixels do not belongs to the same neighborhood system. Thus the neighborhood system on the label field is defined. Second, the dissimilarities between pixels and their clusters are described by a Gaussian model. Neighborhood system on the feature field is defined by taking the influence of neighbor pixels on the depiction of dissimilarities. In other word, the zero mean Gaussian noise between the observed data and its cluster is replaced by that between both the observed pixel and pixels in its neighborhood system and their clusters. Finally, the neighborhood system of both the label and feature fields are used to model a fuzzy clustering algorithm to realize the high segmentation accuracy. The efficiency of the proposed algorithm is demonstrated through experiments on simulated and real color images and the comparison of the segmentation results with other FCM based algorithms.