The early and accurate detection of brain tumors is key to improve the quality of life and the survival of cancer patients. Magnetic resonance imaging (MRI) is a widely used imaging technique to assess these tumors, but the large amount of data produced by MRI prevents manual segmentation in a reasonable time, limiting the use of precise quantitative measurements in the clinical practice. Consequently, automatic and reliable segmentation methods are required. However, the large spatial and structural variability among brain tumors make automatic segmentation a challenging problem. In this contribution, we present a new model of segmentation of brain magnetic resonance images. In order to obtain the region of interest, we propose a hybrid approach that carries out both fuzzy c-mean algorithm and multiobjective optimization taking into account both compactness and separation in the clusters with the purpose of improving the cluster center detection and speed up the convergence time. This new segmentation approach is a key component of the proposed magnetic resonance image-based classification system for brain tumors. Experimental results are presented to demonstrate the effectiveness and efficiency of the proposed approach using the DICOM MRI database.