Deformable Registration for MRI Medical Image

  • Li, Binglu (Dept. Electronics and Electrical Engineering, Dankook University) ;
  • Kim, YoungSeop (Dept. Electronics and Electrical Engineering, Dankook University) ;
  • Lee, Yong-Hwan (Dept. of Digital Contents, Wonkwang University)
  • Received : 2019.06.15
  • Accepted : 2019.06.25
  • Published : 2019.06.30

Abstract

Due to the development of medical imaging technology, different imaging technologies provide a large amount of effective information. However, different imaging method caused the limitations of information integrity by using single type of image. Combining different image together so that doctor can obtain the information from medical image comprehensively. Image registration algorithm based on mutual information has become one of the hotspots in the field of image registration with its high registration accuracy and wide applicability. Because the information theory-based registration technology is not dependent on the gray value difference of the image, and it is very suitable for multimodal medical image registration. However, the method based on mutual information has a robustness problem. The essential reason is that the mutual information itself is not have enough information between the pixel pairs, so that the mutual information is unstable during the registration process. A large number of local extreme values are generated, which finally cause mismatch. In order to overcome the shortages of mutual information registration method, this paper proposes a registration method combined with image spatial structure information and mutual information.

Keywords

References

  1. Jenkinson, Mark, and Stephen Smith. "A global optimization method for robust affine registration of brain images." Medical image analysis 5.2 (2001): 143-156. https://doi.org/10.1016/S1361-8415(01)00036-6
  2. Maes, Frederik, et al. "Multimodality image registration by maximization of mutual information." IEEE transactions on Medical Imaging 16.2 (1997): 187-198. https://doi.org/10.1109/42.563664
  3. Mattes, David, et al. "Nonrigid multimodality image registration." Medical Imaging 2001: Image Processing. Vol. 4322. International Society for Optics and Photonics, 2001.
  4. Pluim, Josien PW, JB Antoine Maintz, and Max A. Viergever. "Mutual-information-based registration of medical images: a survey." IEEE transactions on medical imaging 22.8 (2003): 986-1004. https://doi.org/10.1109/TMI.2003.815867
  5. Vercauteren, Tom, et al. "Non-parametric diffeomorphic image registration with the demons algorithm." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Berlin, Heidelberg, 2007.
  6. Van den Elsen, Petra A., E-JD Pol, and Max A. Viergever. "Medical image matching-a review with classification." IEEE Engineering in Medicine and Biology Magazine 12.1 (1993): 26-39. https://doi.org/10.1109/51.195938
  7. Liu, Changchun, Ke Li, and Zhongguo Liu. "Medical image registration by maximization of combined mutual information and edge correlative deviation." 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 2006.
  8. McAuliffe, Matthew J., et al. "Medical image processing, analysis and visualization in clinical research." Proceedings 14th IEEE Symposium on Computer-Based Medical Systems. CBMS 2001. IEEE, 2001.
  9. Nuyts, Johan. "The use of mutual information and joint entropy for anatomical priors in emission tomography." 2007 IEEE Nuclear Science Symposium Conference Record. Vol. 6. IEEE, 2007.
  10. Johnson, Kisha, et al. "Mutual information as a similarity measure for remote sensing image registration." Geospatial Image and Data Exploitation II. Vol. 4383. International Society for Optics and Photonics, 2001.
  11. Binglu, and Youngseop Kim. "Multimodality and Nonrigid Registration of MRI' Brain Image." Journal of Semiconductor and Display (2019) : 102-104.