Tumor Segmentation using Optimize Evidential C-Means at Brain MRI Image
Nagaveni B Sangolgi1, M. Sasikala2

1Nagaveni B Sangolgi*, Asst Prof, ECE Dept, Faculty of Engg and Technology (Ex- women), Sharnbasva University, Kalaburagi.
2Dr. M. Sasikala, Principal, Godutai Enginering College For Women, Kalaburagi.
Manuscript received on December 16, 2019. | Revised Manuscript received on December 25, 2019. | Manuscript published on January 10, 2020. | PP: 1316-1323 | Volume-9 Issue-3, January 2020. | Retrieval Number: A4187119119/2020©BEIESP | DOI: 10.35940/ijitee.A4187.019320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

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. 
Keywords: Magnetic Resonance Imaging (MRI), Probability based Evidence Theory (PbET), Learning Metric (LM), Optimize Evidential C-Means (OECM)
Scope of the Article:  Image Analysis and Processing