A novel relative entropy based FCM algorithm using interval type-2 fuzzy set at local membership functions for brain MR image segmentation

In this proposed article, we develop a fuzzy c-means algorithm based on relative entropy for segmentation of noisy volumetric brain MR images and introduce interval type-2 fuzzy membership functions by using local membership functions as a secondary membership function. The brain magnetic resonance images are influenced by high level of intensity inhomogeneity (IIH) and noise, because of the limitations of machinery devices and cognitive limitations of the operators. As a result it produces blurry images, mostly at the soft tissue boundary regions. This phenomenon increases uncertainty for the image soft tissue classification. The proposed algorithm handle this issue by incorporating interval type-2 fuzzy (IT2F) set at the local membership functions to reduce the underlying uncertainty factors of each voxels more efficiently. It also integrates the total uncertainties for volumetric images by utilizing relative entropy. At first, the proposed algorithm generates the global and local membership functions for each voxel, next, it generates type-2 fuzzy (T2F) set by considering local membership functions as a secondary membership functions and finally, the algorithm generates IT2F set by doing direct defuzzificaton of the T2F set. We conduct qualitative and quantitative experiments on ten different simulated volumes of brain MR images. The results revel the outcome of the experiments and it indicates the supremacy of the proposed algorithm over the state-of-the-art algorithms established in near past.