基于空间邻域信息的模糊聚类图像分割

Fuzzy C-means Clustering Algorithm Based on Spatial Information for Image Segmentation

模糊C均值(Fuzzy C-means,FCM)聚类算法在图像分割中已获得广泛应用。为了克服传统FCM算法抗噪性能差的局限性,提出了一种新的基于空间邻域信息的模糊聚类图像分割方法。该方法将图像的聚类分割转化为一个优化问题,通过建立包含邻域信息的适应度函数考虑像素之间的相互影响,利用捕食者-食饵微粒群的全局优化能力获得最优聚类中心,实现图像分割。仿真结果表明,提出的算法不易陷入局部最优,抗噪能力强,分割效果好,是一种有效的图像分割算法。

The fuzzy C-means （FCM） clustering algorithm has widely been applied in image segmentation. A fuzzy C-means clustering method based on spatial information is proposed for solving the problem of poor anti-noise performance of the traditional fuzzy C-means （FCM） algorithm in image segmentation. In this method, the image segmentation is converted into an optimization problem. The fitness function contained neighbor information is set up based on the neighbor relations between the pixels. Utilizing the global searching ability of the predator-prey particle swarm optimization, the optimal cluster center can be obtained, and the image segmentation can be accomplished. The simulation results show the proposed method can effectively avoid getting into the local optimum. It has strong anti-noise capability and good segment effect, which is an effective method for image segmentation.