基于蚁群算法的模糊C均值聚类医学图像分割

在医学图像分割研究中，针对模糊C均值(FCM)聚类算法聚类个数难于确定、搜索过程容易陷入局部最优的缺陷，把蚁群算法与FCM聚类算法有机结合，提出了一种基于蚁群算法的模糊C均值聚类图像分割算法. 该算法首先利用蚁群算法全局性和鲁棒性的优点，得到聚类中心和聚类个数，再将其作为模糊C均值聚类的初始聚类中心和聚类个数，弥补了传统FCM聚类算法的不足，得到了较好的分割效果. 实例分析证明了算法的有效性和实用性.

Medical image segmentation of fuzzy C-means clustering based on the ant colony algorithm

With the fuzzy C-means clustering(FCM) algorithm it is difficult to determine the number of clusters in the research of medical image segmentation,which is easy to get into a local optimum.To overcome these shortcomings,a fuzzy C-means clustering algorithm is presented for image segmentation based on the ant colony algorithm.First,the overall robustness advantages of the ant colony algorithm are used to get the cluster center and the number of the clusters of the image.Then the results are obtained as the initial cluster centers and the number of clusters of fuzzy C-means clustering algorithm.This algorithm overcomes the shortcomings of traditional ones and gets better results.Analysis of the examples is given to prove the effectiveness and practicality of this algorithm.