A Robust Fuzzy Algorithm Based on Student's t-Distribution and Mean Template for Image Segmentation Application

Abstract

Fuzzy c-means (FCM) with spatial constraints has been considered as an effective algorithm for image segmentation. Student's t-distribution has come to be regarded as an alternative to Gaussian distribution, as it is heavily tailed and more robust for outliers. In this letter, we propose a new algorithm to incorporate the merits of these two approaches. The advantages of our method are as follows: First, we incorporate the local spatial information and pixel intensity value by considering the labeling of an image pixel influenced by the labels in its immediate neighborhood. Second, we introduce additional parameter <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</i> to control the extent of this influence. The larger <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</i> indicates heavier extent of influence in the neighborhoods. Finally, we utilize a mean template instead of the traditional hidden Markov random field (HMRF) model for estimation of prior probability. Compared with HMRF, our method is simple, easy and fast to implement. Experimental results on synthetic and real images demonstrate the improved robustness and effectiveness of our approach.