[1]廖林峰,邱晓晖.基于模糊 C-均值聚类医学图像分割的优化算法[J].计算机技术与发展,2017,27(12):81-84.[doi:10.3969/ j. issn.1673-629X.2017.12.018]
　LIAO Lin-feng,QIU Xiao-hui.An Optimal Algorithm for Medical Image Segmentation Based on Fuzzy C-Means Clustering[J].Computer Technology and Development,2017,27(12):81-84.[doi:10.3969/ j. issn.1673-629X.2017.12.018]
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基于模糊 C-均值聚类医学图像分割的优化算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年12期
页码:
81-84
栏目:
智能、算法、系统工程
出版日期:
2017-12-10

文章信息/Info

Title:
An Optimal Algorithm for Medical Image Segmentation Based on Fuzzy C-Means Clustering
文章编号:
1673-629X(2017)12-0081-04
作者:
廖林峰; 邱晓晖
南京邮电大学 通信与信息工程学院,江苏 南京 210003
Author(s):
LIAO Lin-feng; QIU Xiao-hui
School of Communication and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,China
关键词:
模糊 C-均值聚类; 遗传算法; 粒子群算法; 邻域像素; 核磁共振成像
Keywords:
Fuzzy C-Means clustering; genetic algorithm; particle swarm optimization; neighborhood pixels; nuclear magnetic resonance imaging
分类号:
TP301.6
DOI:
10.3969/ j. issn.1673-629X.2017.12.018
文献标志码:
A
摘要:
模糊 C-均值聚类(FCM)算法在分割模糊的医学图像中有很好的效果,通过设置初始聚类中心,根据每个像素的隶属度来划分属于哪一类,采用迭代的方式来得到分割结果。 针对 FCM 算法容易受到聚类中心初始值和噪声的影响,采用遗传算法和粒子群算法的结合算法来确定一组合适的初始聚类中心,通过遗传算法和粒子群算法的结合算法加快了单纯使用遗传算法确定初始聚类中心的收敛速度;再通过引入像素的邻域信息,重构标准 FCM 算法中的目标函数,以提高邻域像素和中心像素之间的相似程度,使得相邻的像素更容易划分到同一类别,克服了标准 FCM 算法只考虑像素间的灰度值而导致对噪声和异常值的敏感问题。 将该方法应用到核磁共振成像(MRI)脑部图像分割实验中,相比标准的 FCM 分割算法和遗传模糊聚类算法,分割效果更好。
Abstract:
Fuzzy C-Means Clustering (FCM) has a good effect in the segmentation of fuzzy medical images. By setting the initial cluster center,the division is carried out according to the membership degree of each pixel,and segmenting results are obtained by means of iteration. Aiming at the problem that FCM is susceptible to initial value of cluster center and noise,the genetic algorithm and particle swarm algorithm are combined to determine a set of suitable initial clustering centers. After combination,the convergence rate of initial clustering center is accelerated than that of only use of genetic algorithm. Then the objective function of standard FCM is reconstructed by introducing neighborhood information of pixels so as to improve the similarity between the neighborhood and the center pixel,which makes the adjacent pixels more divided into the same class and overcomes the problem that the standard FCM only considers the gray value between the pixels and causes the sensitivity to noise and outliers. The proposed method is applied into the MRI brain image segmentation experiment which shows that it is superior to the standard FCM and genetic algorithm on segmentation effect.

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更新日期/Last Update: 2018-03-06