The Fuzzy C-Means algorithm is a widely used and flexible approach for brain tissue segmentation from 3D MRI. Despite its recent enrichment by addition of a spatial dependency to its formulation, it remains quite sensitive to noise. In order to improve its reliability in noisy contexts, we propose a way to select the most suitable example regions for regularisation. This approach inspired by the Non-Local Mean strategy used in image restoration is based on the computation of weights modelling the grey-level similarity between the neighbourhoods being compared. Experiments were performed on MRI data and results illustrate the usefulness of the approach in the context of brain tissue classification.