In medical imaging, lesion segmentation (differentiation between lesioned and non-lesioned tissue) is a crucial and difficult task. Automated segmentation algorithms based on intensity analysis have been already proposed and recent developments have shown that integrating spatial information enhances automatic image segmentation. However, spatial modeling is often limited to short-range spatial interactions that deal only with noise or small artifacts. Previous tissue alterations (e.g. white matter disease (WMD)) similar in intensity with the lesion of interest require a broader-scale approach to be corrected. On the other hand, imaging techniques offer now a multiparametric voxel characterization that may help differentiating lesioned from non-lesioned voxels. We developed an unsupervised multivariate segmentation algorithm based on finite mixture modeling that incorporates spatial information. We extended the usual spatial Potts model to the regional scale using a 'multi-order' neighborhood potential, with internal adjustment of the regional scale according to the lesion size. We validate the ability of this new algorithm to deal with noise and artifacts (linear and spherical) using artificial data. We then assess its performance on real magnetic resonance imaging brain scans of stroke patients with history of WMD and show that regional regularization was able to remove large-scale WMD artifacts.