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.