The well-known fuzzy c-means algorithm (FCM) has been regarded as a useful tool for image segmentation application.However

it is still insufficient robustness to image noise due to the distance function selection in FCM.In this paper

we propose a new hierarchical fuzzy algorithm to make the traditional fuzzy c-means more robust to image noise and outliers.We introduce a more flexibility function which considers the distance function itself as a sub-FCM with student's

t

-distribution.Thus

our hierarchical model is general and flexible enough to deal with outliers and noises.Our algorithm proposed in this paper can be extended to any other FCM-based models to achieve superior performance.Experimental results demonstra

te the improved robustness and effectiveness of the proposed algorithm.