Advertisement

Simultaneous Population Based Image Alignment for Template Free Spatial Normalisation of Brain Anatomy

  • C. Studholme
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2717)

Abstract

Current approaches to spatial normalisation of brain images have made use of a target image to which each subject image is matched. However, in many cases the use of a single brain template, or a statistical one derived from multiple subjects of another population, does not adequately capture the structure present in a population of anatomies under investigation. In such cases this paper proposes that a better approach may be to seek a method of driving subjects in the group into registration with each other, rather than with an unrepresentative template. This paper explores the approach of extending registration concepts from multi-modality registration, specifically those deriving criteria from the joint probability distribution of image values, to the general case of describing the alignment of a population of images simultaneously. Geometric constraints forcing the convergence to an average geometric shape are discussed and results presented on synthetic images and clinical brain image data.

Keywords

Mutual Information White Matter Lesion Spatial Normalisation Fronto Temporal Dementia Joint Probability Distribution 
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.
    Christensen, G.E., Johnson, H.J.: Consistent image registration. Transactions on Medical Imaging, 568–582 (2001)Google Scholar
  2. 2.
    Christensen, G.E., Rabbitt, R.D., Miller, M.I.: Deformable templates using large deformation kinematics. IEEE Transactions on Image Processing 5, 1435–1447 (1996)CrossRefGoogle Scholar
  3. 3.
    Collignon, A., Maes, F., Delaere, D., Vandermeulen, D., Suetens, P., Marchal, G.: Automated multimodality image registration using information theory. In: Bizais, Y., Barillot, C., Di Paola, R. (eds.) Proceedings of Information Processing in Medical Imaging, Brest, France, pp. 263–274. Kluwer Academic Publishers, Dordrecht (1995)Google Scholar
  4. 4.
    Collins, D.L., Evans, A.C., Holmes, C., Peters, T.M.: Automatic 3D segmentation of neuro-anatomical structures from MRI. In: Bizais, Y., Barillot, C., Di Paola, R. (eds.) Proceedings of Information Processing in Medical Imaging, Brest, France, pp. 139–152. Kluwer Academic Publishers, Dordrecht (1995)Google Scholar
  5. 5.
    Davatzikos, C.: Spatial transformation and registration of brain images using elastically deformable models. Comp Vision and Image Understanding 66(2), 207–222 (1997)CrossRefGoogle Scholar
  6. 6.
    Gee, J.C., Reivich, M., Bajcsy, R.: Elastically deforming 3d atlas to match anatomical brain images. J. Comput. Asst Tomogr 17, 225–236 (1993)CrossRefGoogle Scholar
  7. 7.
    Grenander, U., Miller, M.I.: Computational anatomy: An emerging discipline. Quart. Appl. Math. 56(4), 617–694 (1998)zbMATHMathSciNetGoogle Scholar
  8. 8.
    Hill, D.L.G., Studholme, C., Hawkes, D.J.: Voxel similarity measures for automated image registration. In: Proceedings of Visualisation in Biomedical Computing, Rochester Mn.,U.S.A., vol. SPIE 2359, pp. 205–216. SPIE Press, Bellingham (1994)Google Scholar
  9. 9.
    Lee, S., Wolberg, G., Shin, S.Y.: Scattered data interpolation with multilevel Bsplines. IEEE Transactions on Visualisation and Computer Graphics 3, 228–244 (1997)CrossRefGoogle Scholar
  10. 10.
    Lester, H., Arridge, S., Jansons, K., Lemieux, L., Hajnal, J., Oatridge, A.: Nonlinear registration with variable viscosity fluid algorithm. In: Proc. Image Processing in Medical Imaging, pp. 238–251. Springer, Heidelberg (1999)Google Scholar
  11. 11.
    Miller, E.: Learning from One Example in Machine Vision by Sharing Probability Densities. PhD thesis, Massachusetts Institute of Technology (2002)Google Scholar
  12. 12.
    Reza, F.M.: An Introduction to Information Theory. Dover, New York (1994)zbMATHGoogle Scholar
  13. 13.
    Studholme, C., Cardenas, V., Schuff, N., Rosen, H., Miller, B., Weiner, M.: Detecting spatially consistent structural differences in alzheimer’s and fronto temporal dementia using deformation morphometry. In: Proceedings of 4th International Conference on Medical Image Computing and Computer Assisted Interventions, pp. 41–48 (2001)Google Scholar
  14. 14.
    Studholme, C., Hill, D.L.G., Hawkes, D.J.: An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognition 32(1), 71–86 (1999)CrossRefGoogle Scholar
  15. 15.
    Subsol, G., Thirion, J.-P., Ayache, N.: A scheme for automatically building three-dimensional morphometric anatomical atlases: Application to a skull atlas. Medical Image Anal. 2, 37–60 (1998)CrossRefGoogle Scholar
  16. 16.
    Thirion, J.-P.: Image matching as a diffusion process: An analogy with maxwell’s demons. Medical Image Anal. 2(3), 243–260 (1998)CrossRefGoogle Scholar
  17. 17.
    Thompson, P., Woods, R., Mega, M., Toga, A.: Mathematical/Computational challenges in creating deformable and probabilistic atlases of the human brain. Human Brain Mapping 9, 81–92 (2000)CrossRefGoogle Scholar
  18. 18.
    Viola, P.A., Wells, W.M.: Alignment by maximisation of mutual information. In: Proceedings of the 5th International Conference on Computer Vision, pp. 15–23 (1995)Google Scholar
  19. 19.
    Woods, R.P., Grafton, S.T., Watson, J.D., Sicotte, N.L., Mazziotta, J.C.: Automated image registration II: Intersubject validation of linear and non-linear models. Journal of Computer Assisted Tomography 22, 153–165 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • C. Studholme
    • 1
  1. 1.Dept. RadiologyUniversity of CaliforniaSan Francisco

Personalised recommendations