Learning optimization updates for multimodal registration
Gutiérrez-Becker B, Mateus D, Peter L, Navab N (2016)
Publication Type: Conference contribution
Publication year: 2016
Journal
Publisher: Springer Verlag
Book Volume: 9902 LNCS
Pages Range: 19-27
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISBN: 9783319467252
DOI: 10.1007/978-3-319-46726-9_3
Abstract
We address the problem of multimodal image registration using a supervised learning approach. We pose the problem as a regression task,whose goal is to estimate the unknown geometric transformation from the joint appearance of the fixed and moving images. Our method is based on (i) context-aware features,which allow us to guide the registration using not only local,but also global structural information,and (ii) regression forests to map the very large contextual feature space to transformation parameters. Our approach improves the capture range,as we demonstrate on the publicly available IXI dataset. Furthermore,it can also handle difficult settings where other similarity metrics tend to fail; for instance,we show results on the deformable registration of Intravascular Ultrasound (IVUS) and Histology images.
Involved external institutions
Germany (DE)How to cite
APA:
Gutiérrez-Becker, B., Mateus, D., Peter, L., & Navab, N. (2016). Learning optimization updates for multimodal registration. In Leo Joskowicz, Mert R. Sabuncu, William Wells, Gozde Unal, Sebastian Ourselin (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 19-27). Springer Verlag.
MLA:
Gutiérrez-Becker, Benjamín, et al. "Learning optimization updates for multimodal registration." Proceedings of the Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Ed. Leo Joskowicz, Mert R. Sabuncu, William Wells, Gozde Unal, Sebastian Ourselin, Springer Verlag, 2016. 19-27.
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