Mammography associated with clinical breast examination is the only effective method for mass breast screening. Microcalcifications are one of the primary signs for early detection of breast cancer. In this paper 1 we propose a new kernel method for classification of difficult-to-diagnose regions in mammographic images. It consists of a novel class of Markov Random Fields, using techniques developed within the context of statistical mechanics. This method is used for the classification of positive Region of Interest (ROI's) containing clustered microcalcifications and negative ROI's containing normal tissue. The obtained results show that the proposed approach can be successfully employed for detection of microcalcifications.