BRAIN TUMOR IMAGE SEGMENTATION USING INTELLIGENT MEAN SHIFT CLUSTERING TECHNIQUE
Journal Title: Elysium Journal of Engineering Research and Management - Year 2015, Vol 2, Issue 3
Abstract
Brain tumor is a deadly disease which challenges on detecting tumor cells. The tumor detection becomes more complicated for diagnosis as it exhibits complex characteristics. To address this problem, we propose a brain tumor segmentation method for identification of tumor cells by iterative process. Mean shift clustering technique combines with local independent projection – based classification (LIPC) to detect tumor by means of iterative process. We introduce statistical regional merging (SRM) the change does not require prior knowledge of the means of the average number of clusters and the clusters are not controlling in the form, it is a nonparametric clustering technique. It is used for locating the maxima of a density function. Local independent project - based classification (LIPC), where all the values applied to the closest match. Once the tumor cells and normal cells Bhattacharyya coefficient as a result of the segmentation is to improve the classification performance. The test results for several categories of data are evaluated in the global brain images and a common platform. The additional advantage of the proposed method is to apply the universal brain images and a common platform.
Authors and Affiliations
Priyanka V, Kathirvel K. , Batri K.
Keywords
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