FCM融合改进的GSA算法在医学图像分割中的研究

计算机科学 ›› 2018, Vol. 45 ›› Issue (6A): 252-254.

FCM融合改进的GSA算法在医学图像分割中的研究

冯飞,刘培学,李丽,陈玉杰

青岛黄海学院山东青岛266427

出版日期:2018-06-20 发布日期:2018-08-03
作者简介:冯飞(1978-),女,硕士,副教授,主要研究方向为智能与自适应控制及图像处理;刘培学(1983-),男,博士,副教授,主要研究方向为嵌入式系统应用;李丽(1982-),女,硕士,主要研究方向为计算机信息处理;陈玉杰(1979－),女,硕士,副教授,主要研究方向为智能控制。
基金资助:
国家自然科学基金项目(61471224),山东省高等学校科技计划项目(J16LN80,J16LN94)资助

Study of FCM Fusing ImprovedGravitational Search Algorithm in Medical Image Segmentation

FENG Fei, LIU Pei-xue,LI Li,CHEN Yu-jie

Qingdao Huanghai College,Qingdao,Shandong 266427,China

Online:2018-06-20 Published:2018-08-03

摘要/Abstract

摘要： 医学图像由于具有复杂性,在对其进行图像分割时存在很大的不确定性,为了提高模糊c均值聚类算法(FCM)在处理医学图像分割时的性能,提出一种新的混合方法进行图像分割。利用FCM算法将图像像素分成均匀的区域,融合引力搜索算法,将改进的引力搜索算法纳入模糊c均值聚类算法中,以找到最优聚类中心,使模糊c均值聚类的适应度函数值最小,从而提高分割效果。实验结果表明,相对于传统的聚类算法,所提算法在分割复杂的医学图像方面更具有效性。

关键词: FCM, 分割, 聚类中心, 引力搜索算法

Abstract: In order to improve the performance of the fuzzy c-means clustering algorithm in dealing with medical image segmentation,this paper presented a new hybrid method for image segmentation.The method uses fuzzy c-means clustering algorithm (FCM) to divide image pixel space into homogeneous area.Gravitational search algorithm is fused is putted into the fuzzy c-means clustering algorithm to find the optimal clustering center and make the fitness function value of fuzzy c-means clusteringminimal.Experimental results show that compared with traditional clustering algorithm,this method is more effective in the segmentation of different types of images.

Key words: Cluster centers, FCM, Gravitational search algorithm, Segmentation

中图分类号:

TN911.73

冯飞,刘培学,李丽,陈玉杰. FCM融合改进的GSA算法在医学图像分割中的研究[J]. 计算机科学, 2018, 45(6A): 252-254. https://doi.org/

FENG Fei, LIU Pei-xue,LI Li,CHEN Yu-jie. Study of FCM Fusing ImprovedGravitational Search Algorithm in Medical Image Segmentation[J]. Computer Science, 2018, 45(6A): 252-254. https://doi.org/

参考文献

[1]张慧哲,王坚.基于初始聚类中心选取的改进FCM聚类算法[J].计算机科学,2009,36(6):206-209.
[2]冯晓蒲,张铁峰.4种聚类方法之比较[J].微型机与应用,2010,29(16):1-3.
[3]张翡.基于模糊C均值聚类的医学图像分割研究[J].计算机工程与应用,2014,50(4):144-151.
[4]杨秀杰,陈平,李法平.基于改进的模糊聚类图像分割技术研究[J].科技通报,2012,28(45):123-124.
[5]纪震,周家锐,廖惠连,等.智能单粒子优化算法[J].计算机学报,2010,33(3):556-561.
[6]LIANG L,TU S L.MR brain image segmentation based on kernelized fuzzy clustering using fuzzy gibbs random field model[C]∥International Conference on Complex Medical Engineering.Beijing,2007:529-535.
[7]田存伟,葛广英,申哲.基于模糊C 均值的Mean-Shift目标跟踪算法[J].计算机应用,2009,29(12):3332-3335.
[8]黄洋文,王红亮.基于量子粒子群优化算法的图像分割方法[J].电视技术,2010,34(4):16-18.
[9]王永久.引力理论[M].北京:科学出版社,2011.
[10]杜隆胤.基于万有引力定律的分类方法研究[J].计算机应用与软件,2013,30(2):205-207.
[11]KRINIDIS S,CHATZIS V.A robust fuzzy local information C-means clustering algorithm[J].IEEE Transactions on Image Processing,2010,19:1328-1337.

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