This paper focuses on the detection and segmentation of multiple sclerosis (MS) lesions in magnetic resonance images. The proposed method performs healthy tissue segmentation using a probabilistic model for normal brain images. MS lesions are simultaneously identified as outlier Gaussian components. The probablistic model, termed constrained-GMM, is based on a mixture of many spatially-oriented Gaussians per tissue. The intensity of a tissue is considered a global parameter and is constrained to be the same value for a set of related Gaussians per tissue. An active contour algorithm is used to delineate lesion boundaries. Experimental results on both standard brain MR simulation data and real data, indicate that our method outperforms previously suggested approaches especially for highly noisy data.