Hybrid Surface- and Voxel-Based Registration for MR-PET Brain Fusion

  • Ho Lee
  • Helen Hong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)


In this paper, we propose a novel technique of registration using hybrid approach for MR-PET brain image fusion. Hybrid approach uses merits of surface- and voxel-based registration. Thus, our method measures similarities using voxel intensities in MR images corresponding to the feature points of the brain in PET images. Proposed method selects the brain threshold using histogram accumulation ratio in PET images. And then, we automatically segment the brain using the inverse region growing with pre-calculated threshold and extract the feature points of the brain using sharpening filter in PET images. In order to find the optimal location for registration, we evaluate the Hybrid-based Cross-Correlation using the voxel intensities in MR images corresponding to the feature points in PET images. In our experiments, we evaluate our method using software phantom and clinical datasets in the aspect of visual inspection, accuracy, robustness, and computation time. Experimental results show that our method is dramatically faster than the voxel-based registration and more accurate than the surface-based registration. In particular, our method can robustly align two datasets with large geometrical displacement and noise at optimal location.


  1. 1.
    Maintz, J.B.A., Viergever, M.A.: A survey of medical image registration. Medical Image Analysis 2(1), 1–36 (1998)CrossRefGoogle Scholar
  2. 2.
    Dhawan, A.P., Arata, L.K., Levy, A.V., Mantil, J.: Iterative Principal Axes Registration Method for Analysis of MR-PET Brain Images. IEEE Transactions on Biomedical Engineering 42(11), 1079–1087 (1995)CrossRefGoogle Scholar
  3. 3.
    Hata, Y., Kobashi, S., Hirano, S., Ishikawa, M.: Registration of Multi-Modality Medical Images by Soft Computing Approach. In: ICONIP 6th International Conference on Neural Information Processing, vol. 3, pp. 878–883 (1999)Google Scholar
  4. 4.
    Hsu, L.Y., Loew, M.H.: Fully automatic 3D feature-based registration of multi-modality medical images. Image and Vision Computing 19, 75–85 (2001)CrossRefGoogle Scholar
  5. 5.
    Woods, R.P., Mazziotta, J.C., Cherry, S.R.: MRI-PET registration with automated algorithm. Journal of Computer Assisted Tomography 17, 536–546 (1993)CrossRefGoogle Scholar
  6. 6.
    Cizek, J., Herholz, K., Vollmar, S., Schrader, R., Klein, J., Heiss, W.-D.: Fast and robust registration of PET and MR images of human brain. Neuroimage 22(1), 434–442 (2004)CrossRefGoogle Scholar
  7. 7.
    Maes, F., Collignon, A., Marchal, G., Suetens, P.: Multimodality Image Registration by maximization of Mutual Information. IEEE Transaction on Medical Imaging 16(2), 187–198 (1997)CrossRefGoogle Scholar
  8. 8.
    Gonzalez, R.G., Woods, R.E.: Digital Image Processing, 1st edn. (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Ho Lee
    • 1
  • Helen Hong
    • 2
  1. 1.School of Electrical Engineering and Computer ScienceSeoul National University 
  2. 2.School of Electrical Engineering and Computer Science BK21: Information TechnologySeoul National UniversitySeoulKorea

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