Image Segmentation using Fuzzy C-means Algorithm Incorporating Weighted Local Complement Membership and Local Data Distances

Fuzzy C-Means (FCM) algorithm is widely used for unsupervised image segmentation. However, the FCM algorithm does not take into account the local information in the image context. This makes the FCM algorithm sensitive to additive noise degrading the image pixels features. In this paper, an approach to incorporating local data context and membership information into the FCM is presented. The approach consists of adding a weighted regularization function to the standard FCM algorithm. This function is formulated to resemble the standard FCM objective function but the distance is replaced by a new one generated from the local complement or residual membership. The applied regularizing weight is a constant weight or alternatively an adaptive one. The adaptive weight is the Euclidian distance between the center prototype and the local image data mean. The regularizing function aims at smoothing out additive noise and biasing the clustered image to piecewise homogenous regions. Simulation results of clustering and segmentation of synthetic and real-world noisy images have been presented. These results have shown that the presented approach enhances the performance of the FCM algorithm in comparison with the standard FCM and several previously modified FCM algorithms.