Adaptive fuzzy clustering algorithm with local information and markov random field for image segmentation

Abstract

Fuzzy c-means (FCM) clustering as one of the clustering method is widely used in image segmentation field, but some methods based on FCM are unable to obtain satisfactory performance for image segmentation under intense noise condition. This paper presents a novel local spatial information based fuzzy c-means clustering and Markov random field method for image segmentation. In the method, a new dissimilarity function is proposed by using the prior relationship degree and local neighbor distances, which enhances its resistance to noise. And a novel prior probability approximation is considered with spatial Euclidean distance and the difference of the mean color level between the center pixel and its neighborhoods. Experiments over synthetic images, real-world images and brain MR images indicate that the proposed method obtains better segmentation performance, compared to the FCM extended methods.