Hybrid Spline-Based Multimodal Registration Using Local Measures for Joint Entropy and Mutual Information

Authors:
Andreas Biesdorf;Stefan Wörz;Hans-Jürgen Kaiser;Christoph Stippich;Karl Rohr
Affiliations:
BIOQUANT, IPMB, and DKFZ Heidelberg Dept. Bioinformatics and Functional Genomics, Biomedical Computer Vision Group, University of Heidelberg,;BIOQUANT, IPMB, and DKFZ Heidelberg Dept. Bioinformatics and Functional Genomics, Biomedical Computer Vision Group, University of Heidelberg,;Dept. of Nuclear Medicine, University Hospital, RWTH Aachen University,;Dept. of Neuroradiology, University Hospital Heidelberg,;BIOQUANT, IPMB, and DKFZ Heidelberg Dept. Bioinformatics and Functional Genomics, Biomedical Computer Vision Group, University of Heidelberg,
Venue:
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Year:
2009

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Abstract

We introduce a new hybrid approach for spline-based elastic registration of multimodal medical images. The approach uses point landmarks as well as intensity information based on local analytic measures for joint entropy and mutual information. The information-theoretic similarity measures are computationally efficient and can be optimized independently for each voxel. We have applied our approach to synthetic images, brain phantom images, as well as clinically relevant multimodal medical images. We also compared our measures with previous measures.