This paper presents a comparison of the three fuzzy based image segmentation methods namely Fuzzy C-Means (FCM), TYPE-II Fuzzy C-Means (T2FCM), and Intuitionistic Fuzzy C-Means (IFCM) for digital images with varied levels of noise. Apart from qualitative performance, the paper also presents quantitative analysis of these three algorithms using four validity functions-Partition coefficient (V_pc), Partition entropy (V_pe), Fukuyama-Sugeno (V_fs), and Xie-Beni (V_xb) functions and also compared the performance on the basis of their execution time.