Intensity-based robust similarity for multimodal image registration

Du, Juan, Tang, Songyuan, Jiang, Tianzi and Lu, Zhensu (2006) Intensity-based robust similarity for multimodal image registration. International Journal of Computer Mathematics, 83 1: 49-57. doi:10.1080/00207160500112944

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Author Du, Juan
Tang, Songyuan
Jiang, Tianzi
Lu, Zhensu
Title Intensity-based robust similarity for multimodal image registration
Journal name International Journal of Computer Mathematics   Check publisher's open access policy
ISSN 0020-7160
Publication date 2006-01-01
Sub-type Article (original research)
DOI 10.1080/00207160500112944
Volume 83
Issue 1
Start page 49
End page 57
Total pages 9
Place of publication Essex, United Kingdom
Publisher Taylor & Francis
Language eng
Formatted abstract
This paper proposes a new intensity-based similarity metric that can be used for the registration of multimodal images. It combines the robust estimation with both the forward and inverse transformation to reduce the negative effects of outliers in the images. For this purpose, we firstly employ the multiresolution technique to downsample the original images, then resort to the simulated annealing method to initialize the transformation parameters at the coarsest resolution. Finally the Powell method is utilized to obtain the optimal transformation parameters at each resolution. In our experiments, the new method is compared to other popular similarity measures, on the synthetic data as well as the real data, and the experimental results are encouraging.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Queensland Brain Institute Publications
ERA 2012 Admin Only
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Citation counts: TR Web of Science Citation Count  Cited 9 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 12 times in Scopus Article | Citations
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Created: Wed, 19 Oct 2011, 21:17:03 EST by Debra McMurtrie on behalf of Queensland Brain Institute