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Automatic Co-registration of MEG-MRI Data Using Multiple RGB-D Cameras

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International Conference on Biomedical and Health Informatics (ICBHI 2015)

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Abstract

Integration of functional and structural modalities is essential to functional brain mapping. This paper presents an automatic co-registration system for aligning the coordinate systems between magnetoencephalography/electroencephalo-graphy (MEG/EEG) and magnetic resonance image (MRI) using multiple off-the-shelf RGBD cameras. The system was constructed by using multiple Kinects for Windows V2, which were calibrated for the integration of the captured data of subjects’ heads from multiple views. The integrated point clouds of the head surface captured by Kinects played an intermediate role between MEG/EEG and MRI. MEG/EEG-to-Kinect co-registration was conducted by using 3D locations of three anatomical landmarks, whereas Kinect-to-MRI co-registration was performed by using Gaussian mixture model to align facial part of points automatically segmented from both Kinect data and MRI. Combination of these two co-registration results yields the MEG/EEG-to-MRI transformation. Our evaluation results showed that the proposed system can achieve coordinate system alignment with high accuracy.

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References

  1. J. George, P. Jackson, D. Ranken, and E. Flynn, “Three-dimensional volumetric reconstruction for neuromagnetic source localization,” in Advances in biomagnetism, Springer, 1989, pp. 737–740.

    Google Scholar 

  2. H. J. Wieringa, MEG, EEG and the integration with magnetic resonance images: HJ Wieringa, 1993.

    Google Scholar 

  3. S. J. Williamson and L. Kaufman, “Advances in neuromagnetic instrumentation and studies of spontaneous brain activity,” Brain Topography, vol. 2, pp. 129–139, 1989.

    Article  Google Scholar 

  4. S. J. Williamson, Z.-L. Lü, D. Karron, and L. Kaufman, “Advantages and limitations of magnetic source imaging,” Brain topography, vol. 4, pp. 169–180, 1991.

    Google Scholar 

  5. N. Hironaga and A. Ioannides, “Accurate co-registration for MEG reconstructions,” in Proceedings of the 13th International Conference on Biomagnetism. VDE Verlag, Berlin, 2002, pp. 931–933.

    Google Scholar 

  6. V. Napadow, R. Dhond, D. Kennedy, K. K. Hui, and N. Makris, “Automated brainstem co-registration (ABC) for MRI,” NeuroImage, vol. 32, pp. 1113–1119, 2006.

    Article  Google Scholar 

  7. P. Peer and F. Solina, “An automatic human face detection method,” in Proceedings of Computer Vision Winter Workshop (CVWW’99), Rastenfeld, Austria. 1999.

    Google Scholar 

  8. F. Solina, P. Peer, B. Batagelj, and S. Juvan, “15 seconds of fame-an interactive, computer-vision based art installation,” in Proceedings of the 7th International Conference on Control, Automation, Robotics and Vision, pp. 198–204, 2002.

    Google Scholar 

  9. Coordinate systems. Available: http://neuroimage.usc.edu/brainstorm/CoordinateSystems.

  10. N. Baka, C. Metz, C. Schultz, R.-J. van Geuns, W. J. Niessen, and T. van Walsum, “Oriented gaussian mixture models for nonrigid 2D/3D coronary artery registration,” IEEE Transactions on Medical Imaging, vol. 33, pp. 1023–1034, 2014.

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported in part by the Taiwan Ministry of Science and Technology (Grants MOST-103-2221-E-009-131 and MOST-102-2410-H-010-003-MY2), Taipei Veterans General Hospital (Grant V102E3-004), and the UST-UCSD International Center of Excellence in Advanced Bioengineering sponsored by the Taiwan Ministry of Science and Technology I-RiCE Program (Grant MOST-103-2911-I-009-101).

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Correspondence to Shih-Yen Lin or Yong-Sheng Chen .

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Lin, SY., Cheng, CH., Chen, LF., Chen, YS. (2019). Automatic Co-registration of MEG-MRI Data Using Multiple RGB-D Cameras. In: Zhang, YT., Carvalho, P., Magjarevic, R. (eds) International Conference on Biomedical and Health Informatics. ICBHI 2015. IFMBE Proceedings, vol 64. Springer, Singapore. https://doi.org/10.1007/978-981-10-4505-9_43

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  • DOI: https://doi.org/10.1007/978-981-10-4505-9_43

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