A Kernel Fuzzy Clustering Algorithm with Spatial Constraint Based on Improved Ezpectation Mazimization for Image Segmentation
In this paper, an unsupervised image segmentation algorithm is proposed, which combines spatial constraints with a kernel fuzzy c-means (KFCM) clustering algorithm. Conventional KFCM clustering segmentation algorithm does not incorporate the spatial context information of image, which makes it sensitive to the noise and intensity variations. In order to overcome the shortcomings, the contents of image is characterized by Gaussian mixture model, and the parameters of model are estimated by modified expectation maximization (EM) algorithm, which overcomes the classical EM algorithm drawbacks that easily trap in local maxima and be susceptible to initial value. According to the maximum a posterior theorem, we can get the pixel maximum posterior pobability. We redefine the objective function of the KFCM algorithm which incorporates the pixel maximum posteriori pobability, by minimizing the fuzzy objective function, the fuzzy segmentation algorithm is derived. The experimental results on a synthetic image and a real magnetic resonance image show that the proposed algorithm is more effective than the conventional FCM algorithm without local spatial constraints.
fuzzy c-means ezpectation mazimization image segmentation clustering
Xuchao Li Suxuan Bian
College of Information Science and Media Jinggangsha University Jian,China College of Nursing Jinggangsha University Jian,China
国际会议
长沙
英文
1473-1477
2009-04-11（万方平台首次上网日期，不代表论文的发表时间）