Brain tumor segmentation from MR brain images using improved fuzzy c-means clustering and watershed algorithm

Brain is the master and commanding organ of human body. Human brain is affected by many dangerous diseases. Brain tumor or neoplasm is the abnormal growth of tissues in the brain and surrounding regions. MRI is one of the method used for brain tumor diagnosis. Many algorithms are proposed for the automatic extraction of brain tumor tissues from MR brain images. Fuzzy c-Means (FCM) clustering and watershed algorithm are the two commonly used methods for brain tumor extraction. In this paper we implemented the improved version of fuzzy c-Means clustering and watershed algorithm. In fuzzy c-Means clustering we proposed an effective method for the initial centroid selection based on histogram calculation and in watershed algorithm we proposed an atlas based marker detection method for avoiding the over-segmentation problem. Before applying the segmentation algorithms as a pre-processing stage we performed three operations-noise removal, skull stripping and contrast enhancement. We achieved an accuracy of 88.91 and 81.56 of Dice and Tanimoto coefficients for the improved FCM clustering and an accuracy of 93.13 and 88.64 of Dice and Tanimoto coefficients for the improved watershed algorithm.

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