Fuzzy C-means cluster segmentation algorithm based on hybridized particle swarm optimization

Fuzzy C-Means(FCM) algorithm is one of the most popular methods for image segmentation, but it is in essence a technology of searching local optimal solution. The algorithm's initial clustering centers are the stochastic selection which causes it to depend on the selection of the initial cluster centers excessively. It always converges at the local optimum and is sensitive to noise. In order to overcome those defects, the fuzzy C-means cluster segmentation algorithm based on hybridized particle swarm optimization is proposed in this paper. Firstly, the hybridized particle swarm algorithm is used to get the initial cluster centers. Then, the images are segmented using standard FCM algorithm. Experimental results show that the proposed algorithm used for image segmentation can segment images more effectively and can provide more robust segmentation results.