计算机工程与应用2009，Vol.45Issue(33)：179-182,4.DOI:10.3778/j.issn.1002-8331.2009.33.058
模糊C均值聚类图像分割的改进遗传算法研究
Research of improved genetic algorithm for image segmentation based on fuzzy C-means clustering
摘要
Abstract
Based on the fuzzy C-means clustering algorithm,taking advantage of genetic algorithm with the feature of global ran- dom search,a novel improved algorithm combining genetic algorithm and FCM clustering algorithm is proposed.First of all,the method adopts an initial algorithm to assure the initial searching scope of genetic algorithm.Then improvements are appropriately made on parameter.Lastly step of the new algorithm is proposed.The method solves the limitation of converging to the local in-finitesimal point in medical image segmentation,and adopts the initial algorithm to assure the initial searching scope of genetic algorithm which is better accommodable than standard genetic algorithm with fuzzy C-means clustering,speeding up the conver-gence of genetic algorithm.Contrast with results of experiment,the method is better than standard genetic algorithm fused with fuzzy C-means clustering.关键词
模糊C均值聚类/模糊C均值(FCM)聚类算法/遗传算法Key words
fuzzy C-means clustering/Fuzzy C-Means(FCM) ehstering algorithm/genetic algorithm分类
信息技术与安全科学引用本文复制引用
杨凯,蒋华伟..模糊C均值聚类图像分割的改进遗传算法研究[J].计算机工程与应用,2009,45(33):179-182,4.基金项目
河南省自然科学基金(the Natural Science Foundation of Henan Province of China under Grant No.2008A520005). （the Natural Science Foundation of Henan Province of China under Grant No.2008A520005）
