Image segmentation using PSO and PCM with Mahalanobis distance

Fuzzy clustering algorithm is widely used in image segmentation. Possibilistic c-means algorithm overcomes the relative membership problem of fuzzy c-means algorithm, and has been shown to have satisfied the ability of handling noises and outliers. This paper replaces Euclidean distance with Mahalanobis distance in the possibilistic c-means clustering algorithm, and optimizes the initial clustering centers using particle swarm optimization method. Experimental results show that the proposed algorithm has a significant improvement on the effect and efficiency of segmentation comparing with the standard FCM clustering algorithm.