Spline-Based Probabilistic Model for Anatomical Landmark Detection

  • Camille Izard
  • Bruno Jedynak
  • Craig E. L. Stark
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4190)


In medical imaging, finding landmarks that provide biologically meaningful correspondences is often a challenging and time-consuming manual task. In this paper we propose a generic and simple algorithm for landmarking non-cortical brain structures automatically. We use a probabilistic model of the image intensities based on the deformation of a tissue probability map, learned from a training set of hand-landmarked images. In this setting, estimating the location of the landmarks in a new image is equivalent to finding, by likelihood maximization, the ”best” deformation from the tissue probability map to the image. The resulting algorithm is able to handle arbitrary types and numbers of landmarks. We demonstrate our algorithm on the detection of 3 landmarks of the hippocampus in brain MR images.


Training Image Sagittal Slice Gradient Ascent Talairach Space Photometric Parameter 
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.


  1. 1.
    Dryden, I.L., Mardia, K.V.: Statistical Shape Analysis. Wiley, Chichester (1998)MATHGoogle Scholar
  2. 2.
    Talairach, J., Tournoux, P.: Co-planar stereotaxic Atlas of the Human Brain. Thieme Medical Publishers (1988)Google Scholar
  3. 3.
    Joshi, S., Miller, M.: Landmark matching via large deformation diffeomorphisms. IEEE Trans. in Image Processing 9, 1357–1370 (2000)MATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Rohr, K., Stiehl, H.S., Sprengel, R., Buzug, T.M., Weese, J., Kuhn, M.: Landmark-based elastic registration using approximating thin-plate splines. IEEE Trans. Med. Img. 20, 526–534 (2001)CrossRefGoogle Scholar
  5. 5.
    Bookstein, F.L.: Morphometric Tools for Landmark Data: Geometry and Biology. Cambridge University Press, Cambridge (1992)CrossRefGoogle Scholar
  6. 6.
    Thirion, J.P.: New feature points based on geometric invariants for 3D image registration. Int. J. of Computer Vision 18(2), 121–137 (1996)CrossRefGoogle Scholar
  7. 7.
    Wörz, S., Rohr, K.: Localization of anatomical point landmarks in 3D medical images by fitting 3D parametric intensity models. Medical Image Analysis 10, 41–58 (2006)CrossRefGoogle Scholar
  8. 8.
    Rohr, K.: Landmark based Image Analysis using Geometric and Intensity Models. Kluwer Academic, Dordrecht (2001)MATHGoogle Scholar
  9. 9.
    Ashburner, J., Friston, K.J.: Unified segmentation. NeuroImage, 839–851 (2005)Google Scholar
  10. 10.
    Fischl, B., Salat, D.H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., Montillo, A., Makris, N., Rosen, B., Dale, A.M.: Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341–355 (2002)CrossRefGoogle Scholar
  11. 11.
    Wells, W., Kikinis, R., Grimson, W., Jolesz, F.: Adaptive segmentation of mri data. IEEE Trans. Med. Imag. 15, 429–442 (1996)CrossRefGoogle Scholar
  12. 12.
    Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the em algorithm. J. R. Stat. Soc. 39, 1–38 (1977)MATHMathSciNetGoogle Scholar
  13. 13.
    Collins, D.L., Neelin, P., Peters, T.M., Evans, A.C.: Automatic 3d intersubject registration of mr volumetric data in standardized talairach space. Journal of Computer Assisted Tomography 18, 192–205 (1994)CrossRefGoogle Scholar
  14. 14.
    Stark, C., Okado, Y.: Making memories without trying: Medial temporal lobe activity associated with incidental memory formation during recognition. J. of Neurosci. 23, 6748–6753 (2003)Google Scholar
  15. 15.
    Camion, V., Younes, L.: Geodesic interpolating splines. In: Figueiredo, M., Zerubia, J., Jain, A.K. (eds.) EMMCVPR 2001. LNCS, vol. 2134, pp. 513–527. Springer, Heidelberg (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Camille Izard
    • 1
    • 2
  • Bruno Jedynak
    • 1
    • 2
  • Craig E. L. Stark
    • 3
  1. 1.Laboratoire Paul PainlevéUniversité des Sciences et Technologies de LilleFrance
  2. 2.Center for Imaging ScienceJohns Hopkins UniversityBaltimore
  3. 3.Department of Psychological and Brain SciencesJohns Hopkins UniversityBaltimore

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