Paper Title
Single Valued Neutrosophic Fuzzy C-Means for MR Brain Image Segmentation

The image segmentation in medical field plays a vital role in early detection and diagnosing the disease. Recent days, many researchers are working on enhancing the result of segmentation, which are crucial for treatment planning. The Segmentation of brain images are challenging due to the presence of noise and intensity in-homogeneity that creates uncertainty in segmenting the tissues. The neutrosophic sets are efficient tools to address these uncertainties present in the images. In this paper, a novel single valued triangular neutrosohic fuzzy c-means algorithm is proposed to segment the magnetic resonance brain images that can effectively model the uncertainty with truth, falsity and indeterminacy regions. The experimental results reveal that the proposed work out performs the other relevant methods.