this research work presents a new technique for brain tumor detection by the combination of Watershed algorithm with Fuzzy K-means and Fuzzy C-means (KIFCM) clustering. The MATLAB based proposed simulation model is used to improve the computational simplicity, noise sensitivities, and accuracy rate of segmentation, detection and extraction from MR images of brain tumor. The preprocessing stage consists of de- noising, skull stripping and image enhancement, after which MR images are segmented specially by using watershed algorithm followed by Fuzzy K-means and Fuzzy C-means (KIFCM) clustering algorithm. The experimental results of the proposed idea are also compared to the fuzzy C-mean, K-means, Maximization Expectation, and Mean Shift. Superiority of the proposed technique is evaluated through qualitative and quantitative validation experiments in term of noise sensitivity, capture range, computational simplicity and segmentation accuracy.