Brain Tumor Segmentation using hybrid of both Netrosopic Modified Nonlocal Fuzzy C-mean and Modified Level Sets

Shaima Elnazer, Mohamed Morsy, Mohy Eldin A.Abo-Elsoud

Abstract: an improved segmentation approach based on Neutrosophic sets (NS) and Modified Non local Fuzzy c-mean clustering (NLFCM) is proposed. The brain tumor MRI image is transformed into NS domain, which is described using three subsets namely, the percentage of truth in a subset T %, the percentage of indeterminacy in a subset I %, and the percentage of falsity in a subset F %. The entropy in NS is defined and employed to evaluate the indeterminacy. NS image is adapted also using Modified Non local Fuzzy C-mean algorithm (MNLFCM). Finally, MRI brain tumor image is segmented and tumor is selected using Modified Level Sets (MLS). The proposed approach denoted as NS- MNLFCM-MLS and compared with another paper using Jaccard Index and Dice Coefficient. The experimental results demonstrate that the proposed approach is less sensitive to noise and performs better on MRI

Keywords: Magnetic resonance imaging, Netrosophic, Nonlocal fuzzy c mean, Directional -mean operation, modified level sets