Extended Fuzzy C-Means Clustering Algorithm in Segmentation of Noisy Images

A major problem in noisy image processing is the effective segmentation of its components. In this work, we are proposing an Extended Fuzzy C means clustering algorithm for noisy image segmentation, which is able to segment all types of noisy images efficiently. As the presented clustering algorithm selects the centroids randomly hence it is less sensitive, to any type of noise as compare to other clustering algorithms. And we will try to prove that Extended Fuzzy C means clustering converges to approximate the optimal solution based on this criteria theoretically as well as experimentally. Here we will also compare the efficiency of available algorithm for segmentation of gray as well as noisy images.