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

Brain diseases, FCMC Method, Watershed Method, Proposed Method, Bilateral Filter, Brain MRI, Multimodal

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