A New Algorithm for Image Segmentation Based on Gibbs Random Field and Fuzzy C-Means Clustering

Fuzzy c-means(FCM) clustering is one of well-known unsuperviaed clustering techniques, which has been widely used in automated image segmentation.However, when the classical FCM algorithm is used for image segmentation, no spatial information is taken into account.This causes the FCM algorithm to work only on well-defined images with low level of noise;unfortunately, this is not often the case in reality. In order to overcome this limitation of FCM, the prior spatial constraint is incorporated based on Gibbs random field theory.The definition of re/usable level is presented and then new clustering object function is presented.This new algorithm connects Gibbs random field with FCM algorithm and is shown to be most effective in our experiments.