Fuzzy C-Means Clustering with Fast and Adaptive Non-local Spatial Constraint and Membership Linking for Noise Image Segmentation

Considering the problem of the low anti-noise performance when Fuzzy C-Means clustering (FCM) algorithm is applied to image segmentation, a FCM clustering algorithm with fast and adaptive non-local spatial constraint and membership linking is proposed in this paper. Firstly, in order to increase the computing speed of non-local spatial term, a fast method is proposed by modifying the loop based on all pixels in an image into a loop based on search window and by utilising spatial shift image and recursive Gaussian filter. Next, the squared difference between original image and non-local spatial term is calculated as adaptive weight of non-local information term. The squared difference is reciprocally transformed as adaptive weight of the original image. Finally, the membership linking is established to reduce the iteration steps before convergence by adding the square of the sum of all the membership degrees in every cluster in logarithmic form as the denominator of the objectvie function. Experiments on noisy artificial and natural images prove that this proposed algorithm has superior performance in terms of Segmentation accuracy, mean intersection over union, normalized mutual information, running time and iteration steps.