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Magnetic resonance imaging (MRI) has great contribution in diagnosis and treatment of brain tumor. Segmentation of grayscale tumor or low intensity tumor is the most important and challenging task. In this paper, we propose a method to extract grayscale tumor from MRI image. Due to low intensity profile of input image, global contrast enhancement method is applied as a pre-processing step. This enhanced grayscale image is converted into binary image. Further, algorithm segments largest connected region using mathematical morphological and segmentation methods. At last, tumor region is extracted using resultant image and input MRI image. Experimental results show the proposed method effectively extracts a grayscale brain tumor from MRI images.

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This paper describes a statistical approach for segmenting multiple sclerosis lesions (tumors) from magnetic resonance imaging (MRI) images. Proposed method detects and segments the areas inside the brain that are affected by tumors. Tumor regions are the areas of higher intensity in comparison to normal tissue. Our automated method gives satisfactory results showing that the proposed method is capable of segmenting multiple sclerosis lesions of different shapes and intensities. In order to show the efficacy of proposed approach, experimental results are compared with the results of other algorithm and also with the results of manual segmentation performed by experts. Index Terms-Multiple sclerosis (MS) lesions, intra -class variance, MRI image, skull extraction, morphological operations.IEEE Sponsored 2nd International Conference on Innovations in Information Embedded and Communication Systems ICIIECS'15 978-1-4799-6818-3/15/$31.00