PuSH - Publication Server of Helmholtz Zentrum München

Heinrich, M.P.* ; Papiez, B.W.* ; Schnabel, J.A.* ; Handels, H.*

Multispectral image registration based on local canonical correlation analysis.

In: (International Conference on Medical Image Computing and Computer-Assisted Intervention). Berlin [u.a.]: Springer, 2014. 202-209 (Lect. Notes Comput. Sc. ; 8673 LNCS)
DOI
Open Access Green as soon as Postprint is submitted to ZB.
Medical scans are today routinely acquired using multiple sequences or contrast settings, resulting in multispectral data. For the automatic analysis of this data, the evaluation of multispectral similarity is essential. So far, few concepts have been proposed to deal in a principled way with images containing multiple channels. Here, we present a new approach based on a well known statistical technique: canonical correlation analysis (CCA). CCA finds a mapping of two multidimensional variables into two new bases, which best represent the true underlying relations of the signals. In contrast to previously used metrics, it is therefore able to find new correlations based on linear combinations of multiple channels. We extend this concept to efficiently model local canonical correlation (LCCA) between image patches. This novel, more general similarity metric can be applied to images with an arbitrary number of channels. The most important property of LCCA is its invariance to affine transformations of variables. When used on local histograms, LCCA can also deal with multimodal similarity. We demonstrate the performance of our concept on challenging clinical multispectral datasets.
Altmetric
Additional Metrics?
[➜Log in]
Edit extra informations Login
Publication type Article: Conference contribution
Corresponding Author
Keywords Canonical Correlation ; Mri ; Multichannel ; Multimodal
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Conference Title International Conference on Medical Image Computing and Computer-Assisted Intervention
Journal Lecture Notes in Computer Science
Quellenangaben Volume: 8673 LNCS, Issue: PART 1, Pages: 202-209 Article Number: , Supplement: ,
Publisher Springer
Publishing Place Berlin [u.a.]
Non-patent literature Publications
Institute(s) Institute for Machine Learning in Biomed Imaging (IML)