• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

J4 ›› 2016, Vol. 38 ›› Issue (04): 739-746.

• 论文 • 上一篇    下一篇

基于邻域关系模糊粗糙集的医学图像分类研究

胡学伟,蒋芸,邹丽,李志磊,沈健   

  1. (西北师范大学计算机科学与工程学院,甘肃 兰州 730070)
  • 收稿日期:2015-01-12 修回日期:2015-06-18 出版日期:2016-04-25 发布日期:2016-04-25
  • 基金资助:

    国家自然科学基金(61163036,61163039);2012年度甘肃省高校基本科研业务费专项资金项目;甘肃省高校研究生导师项目(120116);西北师范大学第三期知识与创新工程科研骨干项目(nwnukjcxgc0367)

Medical image classification based on
neighborhood relation fuzzy rough set   

HU Xuewei,JIANG Yun,ZOU Li,LI Zhilei,SHEN Jian   

  1. (School of Computer Science and Engineering,Northwest Normal University,Lanzhou 730070,China)
  • Received:2015-01-12 Revised:2015-06-18 Online:2016-04-25 Published:2016-04-25

摘要:

对医学图像进行分类时,特征选择是影响分类准确率的非常重要的因素。针对医学图像的特殊性,以及目前提出的特征选择算法在应用于医学图像分类时效果不够理想等问题,提出一种基于邻域关系的模糊粗糙集模型,基于该模型给出特征选择算法,并将其应用于乳腺X光图像。实验结果表明,同已有的算法相比,该方法能有效选择特征,分类精度有较大的提升。

关键词: 医学图像分类, 特征选择, 邻域关系

Abstract:

For medical images classification, feature selection is an important factor that affects classification accuracy. Aiming at the particularity of medical image as well as undesirable effects of current feature selection methods, we propose a fuzzy rough set model based on neighborhood relation, and a feature selection algorithm based on the model as well. We apply our algorithm to mammography, and the experimental results show that it can effectively select features and improve classification accuracy in comparison with the existing algorithms.

Key words: medical image classification;feature selection;neighborhood relation