Boosted metric learning for 3D multi-modal deformable registration

Defining a suitable metric is one of the biggest challenges in deformable image fusion from different modalities. In this paper, we propose a novel approach for multi-modal metric learning in the deformable registration framework that consists of embedding data from both modalities into a common met...

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Bibliographic Details
Published in2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro pp. 1209 - 1214
Main Authors Michel, F, Bronstein, M, Bronstein, A, Paragios, N
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2011
Subjects
3D Deformable Registration
Biomedical imaging
Feature extraction
Gabor Feature Descriptor
Harmonic analysis
Measurement
Metric Learning
Multi-Modal Registration
Optimization
Three dimensional displays
Training
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Summary:Defining a suitable metric is one of the biggest challenges in deformable image fusion from different modalities. In this paper, we propose a novel approach for multi-modal metric learning in the deformable registration framework that consists of embedding data from both modalities into a common metric space whose metric is used to parametrize the similarity. Specifically, we use image representation in the Fourier/Gabor space which introduces invariance to the local pose parameters, and the Hamming metric as the target embedding space, which allows constructing the embedding using boosted learning algorithms. The resulting metric is incorporated into a discrete optimization framework. Very promising results demonstrate the potential of the proposed method.
ISBN:1424441277
9781424441273
ISSN:1945-7928
DOI:10.1109/ISBI.2011.5872619