Abstract
A multilevel thresholding method for the segmentation of Magnetic Resonance (MR) brain images using the concept of intuitionistic fuzzy and rough set is presented here. Intuitionistic fuzzy roughness measure, calculated by considering histogram as lower approximation of rough set and intuitionistic fuzzy histon as upper approximation of rough set, is used to find optimum valley points for segmentation of brain MR images. A new fuzzy complement function is proposed for intuitionistic fuzzy image representation to take into account intensity inhomogeneity and noise in brain MR images. The proposed algorithm segments brain MR image into three regions, gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF). The quantitative evaluation demonstrate the superiority of the proposed algorithm.
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