Multimodal Image Registration by Maximization of the Correlation Ratio

Abstract : Over the last five years, new «voxel-based» approaches have allowed important leaps in multimodal image registration, notably due to the increasing use of information-theoretic similarity measures. Their wide success has led to the progressive abandon of measures using standard image statistics (mean and variance). Until now, such measures have essentially been based on heuristics. In this paper, we address the determination of a new measure based on standard statistics from a theoretical point of view. We show that it naturally leads to a known concept of probability theory, the \textit{correlation ratio}. In our derivation, we take as the hypothesis the functional dependence between the image intensities. This means that one image is considered as a model of the other. Although such a hypothesis is not validate in every circumstance, it enables us to incorporate implicitely an a priori smoothness model. We also demonstrate preliminary results of multimodal rigid registration involving Magnetic Resonance (MR), Computed Tomography (CT), and Positron Emission Tomography (PET) images. These results suggest that the correlation ratio provides a good trade-off between accuracy and robustness.
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Rapport
RR-3378, INRIA. 1998
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Dernière modification le : jeudi 11 janvier 2018 - 16:40:57
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Alexis Roche, Grégoire Malandain, Nicholas Ayache, Xavier Pennec. Multimodal Image Registration by Maximization of the Correlation Ratio. RR-3378, INRIA. 1998. 〈inria-00073311〉

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