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Hidden Markov random field (HMRF) models have been widely used for image segmentation, as they appear naturally in problems where a spatially constrained clustering scheme, taking into account the mutual influences of neighboring sites, is asked for. Fuzzy c-means (FCM) clustering has also been successfully applied in several image segmentation applications. In this paper, we combine the benefits of these two approaches, by proposing a novel treatment of HMRF models, formulated on the basis of a fuzzy clustering principle. We approach the HMRF model treatment problem as an FCM-type clustering problem, effected by introducing the explicit assumptions of the HMRF model into the fuzzy clustering procedure. Our approach utilizes a fuzzy objective function regularized by Kullback--Leibler divergence information, and is facilitated by application of a mean-field-like approximation of the MRF prior. We experimentally demonstrate the superiority of the proposed approach over competing methodologies, considering a series of synthetic and real-world image segmentation applications.