Image segmentation by a robust generalized fuzzy c-means algorithm

Fuzzy c-means (FCM) has been considered as an effective algorithm for image segmentation. However, it lacks of sufficient robustness to image noise. In this paper, we propose a simple and effective method to make the traditional FCM more robust to noise, with the help of generalized mean. Traditional FCM can be considered as a linear combination of membership and distance (function) from the expression of its mathematical formula. The proposed generalized FCM (GFCM) is generated by applying generalized mean on these two items. We impose generalized mean on membership to incorporate local spatial information and cluster information, and on distance function to incorporate local spatial information and observation information (image intensity value). Thus, our GFCM is more robust to image noise with the spatial constraints: the generalized mean. The performance of our proposed algorithm, compared with state-of-the-art technologies including modified FCM, HMRF and their hybrid models, demonstrates its improved robustness and effectiveness.