The process of spatially aligning two or more images acquired from different devices or imaging protocols is known as multi-modal image registration. As the similarity measure used is one of the most significant aspects of this process, certain measures have been proposed to enhance multi-modal image registration. However, the currently available measures are either not sufficiently accurate or are very computationally expensive. In this paper, a new hybrid multimodal registration approach is proposed. The new approach combines a fast measure, based on matching image edges, with a robust, but slow measure, which uses the joint probability distribution of the two images to be registered. Our experimental results reveal that using this hybrid approach provides a performance equivalent to the previously best measures but with a significantly reduced computational time.
Saadat, S., Pickering, M. R., Perriman, D., Scarvell, J. M., & Smith, P. N. (2017). Fast and Robust Multi-Modal Image Registration for 3D Knee Kinematics. In DICTA 2017 - 2017 International Conference on Digital Image Computing: Techniques and Applications (Vol. 2017-December, pp. 1–5). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/DICTA.2017.8227434