郑州大学学报（理学版）2017，Vol.49Issue(2)：66-71,6.DOI:10.13705/j.issn.1671-6841.2016328
一种改进的模糊C均值图像分割算法
An Improved Fuzzy C-means Algorithm for Image Segmentation
摘要
Abstract
The traditional fuzzy C-means (FCM) image segmentation algorithm suffered from low efficiency and noise susceptibility.An improved fuzzy C-means algorithm for image segmentation was proposed.In the improved algorithm, the greyscale distribution of the original image was used to define the initial cluster centers;then the effects of the neighboring pixels on clustering were considered.Experimental results validated the effectiveness of the proposed method, demonstrating that the automatically chosen cluster centres enhanced the clustering efficiency.Additionally, the method was robust, with the clustering being less affected by noise in the original image.关键词
模糊C均值/初始聚类中心/图像分割/灰度直方图/邻域Key words
fuzzy C-means/initial cluster center/image segmentation/greyscale histogram/neighborhood分类
信息技术与安全科学引用本文复制引用
刘洪普,杨乐,侯向丹,顾军华..一种改进的模糊C均值图像分割算法[J].郑州大学学报（理学版）,2017,49(2):66-71,6.基金项目
天津市自然科学基金项目(16JCYBJC15600). （16JCYBJC15600）
