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

被引：0

作者：

Zhang L. ^{[1
]}

Li B. ^{[1
]}

Tian L.-F. ^{[1
]}

Li X.-X. ^{[1
]}

机构：

[1] School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, Guangdong

来源：

Huanan Ligong Daxue Xuebao/Journal of South China University of Technology (Natural Science) | 2017年 / 45卷 / 07期

基金：

中国国家自然科学基金;

关键词：

Image registration; Local structure tensor; Mutual information; Similarity measure;

D O I：

10.3969/j.issn.1000-565X.2017.07.014

中图分类号：

学科分类号：

摘要：

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 registration enhances. Moreover, some registration experiments are conducted on simulated brain images and clinical images. 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. © 2017, Editorial Department, Journal of South China University of Technology. All right reserved.

引用

页码：98 / 106

页数：8

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