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

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