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However, owing to the fact that it is very susceptible to noise and other image artifacts, its usage is no longer a priority in the constantly changing real world application. The motivation of this paper is to propose a robust &amp; unsupervised Image Segmentation framework known as GIFMRCM for enhancing the underlying delicate architectures of the human brain with ease. GIFMRCM introduces a new objective function by utilising a degree of mutual connectivity factor between pixels and the center. The manuscript can be broken up into two major constituents - Image Segmentation using GIFMRCM, and Cluster-wise color space representation of the GIFMRCM image using k-means hard clustering approach in a CIE L*a*b* color space. Experimentation on medical images shows that the proposed algorithm can improve the performance of image segmentation, and remove noise efficiently. 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