NDLI: Textural mutual information based on cluster trees for multimodal deformable registration

Content Provider	IEEE Xplore Digital Library
Author	Heinrich, M.P. Jenkinson, M. Brady, M. Schnabel, J.A.
Copyright Year	2012
Description	Author affiliation: Department of Oncology, University of Oxford, UK (Brady, M.) \|\| Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK (Heinrich, M.P.; Schnabel, J.A.) \|\| Oxford University Centre for Functional MRI of the Brain, UK (Jenkinson, M.)
Abstract	Mutual information (MI) has been widely used in image analysis tasks such as feature selection and image registration. In particular, it is the most widely used similarity measure for intensity based registration of multimodal images. However, a major drawback of MI is that it does not take the spatial neighbourhood into account. An effective way of incorporating spatial information could be of great benefit in a number of challenging applications. We propose the use of cluster trees to efficiently incorporate textural information from the local neighbourhood of a voxel into the computation of MI, while at the same time limiting the number of bins used to represent this higher-order information. This new similarity metric is optimised using a Markov random field (MRF). We apply our new method to the registration of dynamic lung CT volumes with simulated contrast. Experimental results show the advantages of this technique compared to standard mutual information.
Starting Page	1471
Ending Page	1474
File Size	247623
Page Count	4
File Format	PDF
ISBN	9781457718571
ISSN	19457928
e-ISBN	9781457718588
DOI	10.1109/ISBI.2012.6235849
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-02
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Mutual information Histograms Joints Computed tomography Standards Image registration Lungs cluster trees multimodal image registration mutual information
Content Type	Text
Resource Type	Article

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