Combining image segmentation based on the statistical classification with geometric prior information is supposed to increase robustness and reproducibility. A probability density function is initialized and a spatial constraint is defined which prevent segmentation that is not a part of the model. The goal of this work is a high quality image segmentation of healthy tissue and a precise delineation of tumor boundaries from multiple slices of MRI data. In this paper, algorithms like K-means, Watershed and Expectation Maximization algorithms are used for the investigation and the results of all the segmentations are compared. Based on the results, a common consensus on the robustness of each method is discussed. The Watershed segmentation and the Atlas are combined through markers and this has been applied in the Gray/White matter segmentation in MR images. A previous probability criterion is to be used for its calculation. These methods act as an aid in the early detection of many neurological disorders like Brain tumor, Paralysis, Alzheimer’s disease, etc. They also handle types of pathology, space occupying mass tumors, and infiltrating changes like edema aiding as a new technique for clinical routine for use in planning and monitoring in neurosurgery, radiology and radio-oncology. These methods can be enhanced to delineate tumor from surrounding tissues like edema aiding in image guided surgery. Both the off-line data and live patient data are used for the analysis. Testing of different algorithms for their robustness in segmenting the brain images are carried out using the image processing tool (IPT) of MATLAB.