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A novel approach to the detection of multiple sclerosis (MS) lesions is presented, which uses an adaptive formulation of the anisotropic diffusion and fuzzy-c-means (FCM) clustering. In opposition to previous works of the same authors, FCM runs only on PD weighted slices that, for each examination, are composed in a unique data set. Images are preprocessed with an an isotropic diffusion filter whose diffusion function has been adaptively optimized to aggregate pixels belonging to lesions and cut off all the others. Adaptivity is used to achieve significant noise reduction. A detailed description of the proposed approach is presented, along with first experimental results.