Advertisement

Registration of PET and MR Hand Volumes Using Bayesian Networks

  • Derek Magee
  • Steven Tanner
  • Michael Waller
  • Dennis McGonagle
  • Alan P. Jeavons
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3765)

Abstract

A method for the non-rigid, multi-modal, registration of volumetric scans of human hands is presented. PET and MR scans are aligned by optimising the configuration of a tube based model using a set of Bayesian networks. Efficient optimisation is performed by posing the problem as a multi-scale, local, discrete (quantised) search, and using dynamic programming. The method is to be used within a project to study the use of high-resolution HIDAC PET imagery in investigating bone growth and erosion in arthritis.

Keywords

Bayesian Network Positron Emission Tomography Imaging Magnetic Reso Visual Tracking Positron Emission Tomography Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Myers, R.: The application of PET-MR image registration in the brain. The British Journal of Radiology 75, 31–35 (2002)Google Scholar
  2. 2.
    Woods, R., Grafton, S., Holmes, C., Cherry, S., Mazziotta, J.: Automated image registration I. Journal of Computer Assisted Tomography 22(1), 139–152 (1998)CrossRefGoogle Scholar
  3. 3.
    Wells, W., Viola, P., Atsumi, H., Nakajima, S., Kikinis, R.: Multi-modal volume registration by maximisation of mutual information. Medical Image Analysis 1(1), 35–51 (1996)CrossRefGoogle Scholar
  4. 4.
    Studholme, C., Hill, D., Hawkes, D.: Automated 3D registration of magnetic resonance and positron emssion tomography brain images by multi-resolution optimization of voxel similarity measures. Medical Physics 24(1), 25–35 (1997)CrossRefGoogle Scholar
  5. 5.
    West, J., Fitzpatrick, M., Wang, M., et al.: Comparison and evaluation of retrospective intermodality brain image registration techniques. Journal of Computer Assisted Tomography 21(4), 554–566 (1997)CrossRefGoogle Scholar
  6. 6.
    Makela, T., Pham, Q., Clarysse, P., Neonen, J., Lotjonen, J., Sipila, O., Hanninen, H., Lauerma, K., Knuuti, J., Katila, T., Magnin, I.: A 3D model-based registration approach for the PET, MR and MCG cardiac data fusion. Medical Image Analysis 7(3), 377–389 (2003)CrossRefGoogle Scholar
  7. 7.
    Farahani, K., Slates, R., Shao, Y., Silverman, R., Cherry, S.: Contemporaneous positron emission tomography and MR imaging at 1.5T. Journal of Magnetic Resonance Imaging 9, 497–500 (1999)CrossRefGoogle Scholar
  8. 8.
    Hogg, D.: Model-based vision: A program to see a walking person. Image and Vision Computing 1, 5–20 (1983)CrossRefGoogle Scholar
  9. 9.
    Rehg, J., Kanade, T.: Visual tracking of high DOF atriculated structures: An application to human hand tracking. In: Proc. European Conference on Computer Vision, pp. 35–46 (1994)Google Scholar
  10. 10.
    Stenger, B., Mendonca, P., Cipolla, R.: Model-based hand tracking using an unscented kalman filter. In: Proc. British Machine Vision Conference, pp. 53–72 (2001)Google Scholar
  11. 11.
    Felzenswalb, P., Huttenlocher, D.: Efficient matching of pictorial structures. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (2000)Google Scholar
  12. 12.
    Horn, B.: Closed-form solution of absolute orientation using unit quaternions. Journal of the Optical Society of America 4(4), 629–642 (1987)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Derek Magee
    • 1
  • Steven Tanner
    • 1
  • Michael Waller
    • 2
  • Dennis McGonagle
    • 2
  • Alan P. Jeavons
    • 1
  1. 1.School of Computing/Academic Unit of Medical PhysicsUniversity of LeedsUK
  2. 2.Leeds Teaching Hospitals NHS TrustLeedsUK

Personalised recommendations