Multi-channel image registration is an important challenge in medical image analysis. Multi-channel images result from modalities such as dual-energy CT or multispectral microscopy. Besides, multi-channel feature images can be derived from acquired images, for instance, by applying multi-scale feature banks to the original images to register. Multi-channel registration techniques have been proposed, but most of them are applicable to only two multi-channel images at a time. In the present study, we propose to formulate multi-channel registration as a groupwise image registration problem. In this way, we derive a method that allows the registration of two or more multi-channel images in a fully symmetric manner (i.e. all images play the same role in the registration procedure), and therefore has transitive consistency by definition. The method that we introduce is applicable to any number of multi-channel images, any number of channels per image, and it allows to take into account correlation between any pair of images and not just corresponding channels. In addition, it is fully modular in terms of dissimilarity measure, transformation model, regularisation method, and optimisation strategy. For two multimodal datasets, we computed feature images from the initially acquired images, and applied the proposed registration technique to the newly created sets of multi-channel images. MIND descriptors were used as feature images, and we chose total correlation as groupwise dissimilarity measure. Results show that groupwise multi-channel image registration is a competitive alternative to the pairwise multi-channel scheme, in terms of registration accuracy and insensitivity towards registration reference spaces.

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IEEE Journal of Biomedical and Health Informatics
Biomedical Imaging Group Rotterdam

Guyader, J.-M., Huizinga, W., Fortunati, V., Poot, D., Veenland, J., Paulides, M., … Klein, S. (2018). Groupwise multi-channel image registration. IEEE Journal of Biomedical and Health Informatics, 23(3). doi:10.1109/JBHI.2018.2844361