华南理工大学学报(自然科学版) ›› 2017, Vol. 45 ›› Issue (7): 98-106.doi: 10.3969/j.issn.1000-565X.2017.07.014

• 计算机科学与技术 • 上一篇    下一篇

基于局部结构张量-互信息的多模态图像配准

张莉 李彬 田联房 李祥霞   

  1.  华南理工大学 自动化科学与工程学院,广东 广州 510640
  • 收稿日期:2016-09-18 修回日期:2016-12-16 出版日期:2017-07-25 发布日期:2017-06-01
  • 通信作者: 李彬( 1979-) ,男,副教授,主要从事医学图像处理与模式识别研究. E-mail:binlee@scut.edu.cn
  • 作者简介:张莉( 1987-) ,女,博士生,主要从事模式识别与医学图像处理研究. E-mail: 88zhangli0622@163. com
  • 基金资助:
    国家自然科学基金资助项目( 61305038,61273249) ; 海洋公益性行业科研专项经费资助项目( 201505002) ; 华南理工大学中央高校基本科研业务费专项资金重点资助项目( 2015ZZ028)

Multi-Modal Image Registration on the Basis of Local Structure Tensor-Mutual Information

ZHANG Li LI Bin TIAN Lian-fang LI Xiang-Xia   

  1. School of Automation Science and Engineering,South China University of Technology,Guangzhou 510640,Guangdong,China
  • Received:2016-09-18 Revised:2016-12-16 Online:2017-07-25 Published:2017-06-01
  • Contact: 李彬( 1979-) ,男,副教授,主要从事医学图像处理与模式识别研究. E-mail:binlee@scut.edu.cn
  • About author:张莉( 1987-) ,女,博士生,主要从事模式识别与医学图像处理研究. E-mail: 88zhangli0622@163. com
  • Supported by:
    Supported by the National Natural Science Foundation of China( 61305038,61273249)

摘要: 互信息测度仅考虑了图像全局一致的灰度统计特性而忽略了空间结构信息和图像灰度统计的局部性特点. 为克服以上缺点,提出了一种基于局部结构张量 - 互信息新测度的配准方法. 所提出的新测度充分考虑了图像邻域的结构信息,将结构越强的位置赋予较大的度量值,使得全局极值的区别性加强,减少了配准过程中陷入局部极值的风险,提高了配准成功率,增强了配准的鲁棒性. 采用模拟脑图像和临床图像进行了配准试验,结果表明,与基于互信息和局部互信息的配准方法相比,该方法配准成功率平均提高了 50%以上,配准鲁棒性显著增强.

关键词: 图像配准, 相似性测度, 局部结构张量, 互信息

Abstract: Mutual information ( MI) measure considers the global characteristics of image gray statistics only,and ignores spatial structure information and local characteristics of image gray statistics.In order to overcome these drawbacks,a registration method on the basis of the new measure of local structure tensor-mutual information ( LST- MI) is proposed.The proposed LST-MI measure considers the structure information of image neighborhood fully,gives the pixel position with greater importance larger weighting factor.Thus,the distinguishing of global extremum strengthens,the risk of trapping at local extremum reduces,the success rate improves,and the robustness of regis- tration enhances.Moreover,some registration experiments are conducted on simulated brain images and clinical im- ages.The results show that,in comparison with the registration method on the basis of mutual information and local mutual information,the proposed method improves the success rate of registration by more than 50%,and enhances the registration robustness significantly.

Key words: image registration, similarity measure, local structure tensor, mutual information