计算机工程与应用 ›› 2009, Vol. 45 ›› Issue (16): 180-181.DOI: 10.3778/j.issn.1002-8331.2009.16.052

• 图形、图像、模式识别 • 上一篇    下一篇

结合免疫遗传和粗糙集的改进图像分割方法

张一栋,吴锡生   

  1. 江南大学 信息工程学院,江苏 无锡 214122
  • 收稿日期:2008-03-24 修回日期:2008-06-17 出版日期:2009-06-01 发布日期:2009-06-01
  • 通讯作者: 张一栋

Improved image segmentation method based on immune genetic algorithm and rough set

ZHANG Yi-dong,WU Xi-sheng   

  1. School of Information Technology,Southern Yangtze University,Wuxi,Jiangsu 214122,China
  • Received:2008-03-24 Revised:2008-06-17 Online:2009-06-01 Published:2009-06-01
  • Contact: ZHANG Yi-dong

摘要: 以往的免疫遗传聚类算法都要事先设置聚类数及聚类中心,采取的是有教师学习的方式,对环境的适应性不太。结合免疫网络算法和免疫遗传分类,提出了事先通过一种无教师学习,确定聚类数及聚类中心的免疫遗传分类算法,同时在聚类分类的基础上运用粗糙集对图像进行分割。通过对人脑MR图片的聚类和分割实验,验证了该方法的有效性。

关键词: 免疫遗传算法, 不确定理论, 粗糙集, 图像分割

Abstract: The former immune genetic algorithms have beforehand to establish gathering number and gathering center,adopt the way which the teacher studies,and aren’t good to the environment compatibility.This paper unifies the immunity network algorithm and the immunity heredity classification,proposes immunity heredity classification algorithm through one kind of non-teacher study,determines gathering number and gathering center,simultaneously utilizes the rough collection to carry on the division to the picture.Through gathered kind and the division experiment to the human brain MR picture,the result has confirmed this method validity.

Key words: immune genetic, uncertainty theory, rough set, image segmentation