JIP_V2_N2_RP2 P. Ramesh S. Thilagamani Journal on Image Processing 2349-6827 2 2 6 13 Segmentation, Fuzzy Logics, Fuzzy Set Theory, Clustering, Uncertainty, Membership Function During the past several decades, there were tremendous developments in the field of Image Segmentation. Due to the extreme thrust in enhancing the quality of the image segmentation process, numerous segmentation techniques have evolved. Segmentation in color images is quite difficult due to the uncertainties that exist at the boundary. Fuzzy logic is an ideal concept which is well suited in such cases. It is an approach of computation which is based on the degrees of truth rather than the Boolean logic through which the modern computer works. Fuzzy techniques are highly popular due to its rapid extension of fuzzy set theory and are mainly based on the binary valued membership. Thus the Fuzzy techniques applied in image processing can efficiently manage the ambiguities present in the images. Hence this comparative analysis mainly indicates the working methodologies of different collections of Fuzzy logic techniques in image segmentation and this in turn helps the image processing researchers to innovate more advanced techniques in Fuzzy concept and solve the problem in hand. April - June 2015 Copyright © 2015 i-manager publications. All rights reserved. i-manager Publications http://www.imanagerpublications.com/Article.aspx?ArticleId=3401