Image Segmentation Techniques for Noisy Digital Images based upon Fuzzy Logic- A Review and Comparison

Abstract

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 (Vpc), Partition entropy (Vpe), Fukuyama-Sugeno (Vfs), and Xie-Beni (Vxb) functions and also compared the performance on the basis of their execution time.