Abstract
The accurate treatment of tumor is the major key for diagnosis and therapy, so the development in an area of image processing provide greater contribution in order to detect the tumors in human brain. A medical imaging technique such as MRI is generally used to capture the human brain images. In this paper, we addressed a PbET that is very effective process for reasoning and modelling with the presence of imprecise information and uncertainty. In the PbET function, we will propose an Optimize Evidential C-Means (OECM) approach for the delineation of Gliomas tumor in a MRI brain images. An OECM approach is integrated with spatial regularization and LM for the tumor segmentation in MRI brain image, where the LM is consider to measure the distance for better representation of comparisons between surrounding voxels and the clustering distortion. In order to validate our proposed model, we compared with different brain tumor segmented approach in terms of dice coefficient and sensitivity.
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