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Segmentation of brain lesions in Magnetic Resonance Imaging (MRI) is a difficult task to be mastered by the specialist. This is due to the presence of noise, partial volume effects and susceptibility artifacts in the images and on the borders of the regions of interest. These problems can interfere with the results when manual segmentation is used. Manual segmentation uses local anatomic information based on the user's background; that implies the necessity of constant human intervention. Deformable model approaches attempt to minimize these drawbacks by outlining the region of interest semi-automatically. These methods have been shown to be effective in the extraction of the lesion boundaries in brain MR images. The proposed method employs the multi-channel version of the Mumford-Shah model via level set methods in order to segment multi-sequence brain magnetic resonance (MR) images: FLAIR (Fluid attenuated inversion recovery), T1 and T2- weighted images. Results showed that segmentation of multi-sequence images using this methodology yielded superior results than using each sequence alone. As a consequence, medical doctors can exploit the segmentation results to follow up their patients' status by controlling the evolution or involution of brain lesions.