MRI images play a vital role in clinical and research applications that requires segmentation as an important task .This requires classification of different intensity classes for pathological representations of biological tissues. Intensity in Brain MRI images plays a vital role since it is the main feature to classify the grade of the Glioma. We propose a region-based active contour model based on Charged Fluid Model that sketches upon intensity information in limited regions at a manageable scale, in order to overcome the difficulties caused by intensity inhomogeneities. A drawback of the basic Charged Fluid Model is the initial placement of the contour, which often fails to provide accurate segmentation results due to the intensity inhomogeneity. In this paper, we propose an extended CFM for brain MRI image segmentation by region based approach, which is able to deal with intensity in homogeneities in the input MRI image. Later incorporation of semantics is done in the region merging stage. Performance evaluation is done with the existing Watershed segmentation and our method is found to be more efficient in grouping the regions and identifying the grade of the Glioma with respect to the radiologist opinion since the approach is semantic.