中国组织工程研究

中国组织工程研究 ›› 2011, Vol. 15 ›› Issue (22): 4084-4086.doi: 10.3969/j.issn.1673-8225.2011.22.022

• 骨与关节图像与影像 bone and joint imaging • 上一篇    下一篇

点对称距离模糊C均值聚类算法在脑部MRI图像分割中的应用

邓  羽，黄  华   

  1. 四川大学电气信息学院，四川省成都市  610065
  • 收稿日期:2010-11-29 修回日期:2011-04-05 出版日期:2011-05-28 发布日期:2011-05-28
  • 通讯作者: 黄华，博士，教授，四川大学电气信息学院，四川省成都市 610065 hhua@scu.edu.cn
  • 作者简介:邓羽★，女，1986年生，四川省成都市人，汉族，2009四川大学在读硕士，主要从事医学信号及图像处理的研究。 86yu11@163.com

MRI brain image segmentation based on point symmetry distance - fuzzy C means algorithm

Deng Yu, Huang Hua   

  1. School of Electrical Engineering and Information, Sichuan University, Chengdu  610065, Sichuan Province, China
  • Received:2010-11-29 Revised:2011-04-05 Online:2011-05-28 Published:2011-05-28
  • Contact: Huang Hua, Doctor, Professor, School of Electrical Engineering and Information, Sichuan University, Chengdu 610065, Sichuan Province, China hhua@scu.edu.cn
  • About author:Deng Yu★, Master, School of Electrical Engineering and Information, Sichuan University, Chengdu 610065, Sichuan Province, China 86yu11@163.com

PDF

303

被引次数

5

摘要：

背景：在传统的图像分割方法中，模糊C均值聚类算法应用十分广泛。
目的：将改进的模糊C均值聚类算法应用到MRI图像的分割中，提高MRI图像分割的准确度。
方法：针对传统的基于Minkowski距离的模糊C均值聚类算法，提出了基于点对称距离的模糊C均值聚类算法，并将其运用到了脑部MRI图像分割中。
结果与结论：实验结果表明，与模糊C均值聚类算法相比，点对称距离的模糊C均值聚类算法有明显的优势。

关键词: 模糊C均值聚类, MRI图像, 点对称距离, 点对称距离的模糊C均值聚类算法, 数字化医学

Abstract:

BACKGROUND: Image segmentation is a significant step of image processing and analysis. Within the traditional segmentation methods, fuzzy C means clustering (FCM) is applied widely.
OBJECTIVE: To introduce point symmetry distance (PS)-FCM (PS-FCM) algorithm into the MRI brain image segmentation so as to promote the accuracy of MRI image segmentation.
METHODS: In connection with the traditional FCM algorithm based on Minkowski distance, this pepper introduces PS-FCM algorithm into the MRI brain image segmentation.
RESULTS AND CONCLUSION: Experimental results show that PS-FCM has obvious advantages compared with traditional FCM algorithm.

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