Automatic segmentation and detection of brain tumor is a notoriously complicated issue in Magnetic Resonance Image. The similar state-of-art segmentation methods and techniques are limited for the detection of tumor in multimodal brain MRI. Thus this work deals about the accurate segmentation and detection of tumor in multimodal brain MRI and this research work is focused to improve automatic segmentation results. This work analyses the segmentation performance of existing state-of-art method improved Fuzzy C-Means Clustering (FCMC) method and marker controlled Watershed method and this research work proposed method to amalgamated segmentation results of improved Fuzzy C-Means Clustering (FCMC) method and marker controlled Watershed method to carry out accurate brain tumor detection and enhance the segmentation results. The performance of proposed method is evaluated with assorted performance metric, viz., Segmentation accuracy, Sensitivity and Specificity. The comparative performance of the Proposed Method, FCMC Method and Watershed method is demonstrated on real and benchmark multimodal brain MRI datasets, viz. FLAIR MRI, T1 MRI, MRI and T2 MRI and the experimental results of the proposed method exhibits better results for segmentation and detection of tumor in multimodal brain MR images.