Multispectral image registration based on local canonical correlation analysis
Heinrich MP, Papiez BW, Schnabel JA, Handels H (2014)
Publication Type: Conference contribution
Publication year: 2014
Journal
Publisher: Springer Verlag
Book Volume: 8673 LNCS
Pages Range: 202-209
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: USA
ISBN: 9783319104034
DOI: 10.1007/978-3-319-10404-1_26
Abstract
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. © 2014 Springer International Publishing.
Involved external institutions
Germany (DE)
United Kingdom (GB)How to cite
APA:
Heinrich, M.P., Papiez, B.W., Schnabel, J.A., & Handels, H. (2014). Multispectral image registration based on local canonical correlation analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 202-209). USA: Springer Verlag.
MLA:
Heinrich, Mattias P., et al. "Multispectral image registration based on local canonical correlation analysis." Proceedings of the 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014, USA Springer Verlag, 2014. 202-209.
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