DocumentCode
3104317
Title
Multi-modal image registration by mutual information based on optimal region selection
Author
Zhaoying, Liu ; Fugen, Zhou ; Xiangzhi, Bai ; Hui, Wang ; Dongjie, Tan
Author_Institution
Image Process. Center, Beihang Univ., Beijing, China
Volume
2
fYear
2010
fDate
18-19 Oct. 2010
Abstract
Mutual information (MI) has been commonly used in multi-modal image registration. In this paper, we presented an optimal region selection method for MI based multi-modal image registration. Firstly, image preprocessing and initial registration were applied on the two images. Then we applied two region selection procedures to improve the performance of MI-based registration. The first procedure is hierarchical region detection that selects regions with more structure information in each of the two images. In this procedure, the images were divided into sub-images progressively, and candidate image blocks with high usability were selected based on entropy. The second procedure is optimal matching region selection based on local registration using MI. In this procedure, the regions selected in the first procedure were registered with MI, then the results are used to choose optimal matching regions with high reliability. Finally, the optimal regions were used for the entire image registration by computing the MI of the regions. The experiment results showed that this method could improve the efficiency and accuracy for MI-based image registration.
Keywords
data handling; image processing; image block; image division; image matching; image preprocessing; multimodal image registration; mutual information; optimal region selection; image division; multi-modal image registration; mutual information; optimal regions selection;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Networking and Automation (ICINA), 2010 International Conference on
Conference_Location
Kunming
Print_ISBN
978-1-4244-8104-0
Electronic_ISBN
978-1-4244-8106-4
Type
conf
DOI
10.1109/ICINA.2010.5636737
Filename
5636737
Link To Document