Imaging plays a central role in the diagnosis and treatment br planning of brain tumor. An accurate segmentation is br critical, especially when the tumor morphological changes br remain subtle, irregular and difficult to assess by clinical br examination. Traditionally, segmentation is performed br manually in clinical environment that is operator dependent br and very tedious and time consuming labor intensive work. br However, automated tumor segmentation in MRI brain br tumor poses many challenges with regard to characteristics br of an image. A comparison of three different semiautomated methods, viz., modified gradient magnitude br region growing technique (MGRRGT) , level set and a br marker controlled watershed method is undertaken here for br evaluating their relative performance in the segmentation of br tumor.