Medical image segmentation places a vital role in early detection and diagnosing the disease. Recent days, many researchers are working on enhancing the result of segmentation, which is crucial for treatment planning. Segmenting brain images is challenging due to the presence of noise and intensity in-homogeneity that creates uncertainty in segmenting the tissues. Neutrosophic sets are efficient tools to address these uncertainties present in images. A novel single valued triangular neutrosophic fuzzy c-means algorithm is proposed to segment the magnetic resonance brain images is shown in this paper. The image is represented with triangular neutrosophic sets to obtain truth, falsity and indeterminacy regions which are further used to obtain the centroids and membership function needed for fuzzy c-means to extract the tissues of the brain. The proposed algorithm has proved to be more efficient and outperformed algorithms exist in literature.