Morphometric Analysis of Brain Structures for Improved Discrimination

  • Li Shen
  • James Ford
  • Fillia Makedon
  • Yuhang Wang
  • Tilmann Steinberg
  • Song Ye
  • Andrew Saykin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2879)


We perform discriminative analysis of brain structures using morphometric information. Spherical harmonics technique and point distribution model are used for shape description. Classification is performed using linear discriminants and support vector machines with several feature selection approaches. We consider both inclusion and exclusion of volume information in the discrimination. We perform extensive experimental studies by applying different combinations of techniques to hippocampal data in schizophrenia and achieve best jackknife classification accuracies of 95% (whole set) and 90% (right-handed males), respectively. Our results find that the left hippocampus is a better predictor than the right in the complete dataset, but that the right hippocampus is a stronger predictor than the left in the right-handed male subset. We also propose a new method for visualization of discriminative patterns.


Support Vector Machine Feature Selection Bayesian Model Shape Description Volume Information 
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.
    Csernansky, J.G., Joshi, S., et al.: Hippocampal morphometry in schizophrenia by high dimensional brain mapping. Proc. Nat. Acad. Sci. USA 95, 11406–11411 (1998)CrossRefGoogle Scholar
  2. 2.
    Csernansky, J.G., Wang, L., et al.: Hippocampal deformities in schizophrenia characterized by high dimensional brain mapping. Am. J. Psy. 159, 2000–2006 (2002)CrossRefGoogle Scholar
  3. 3.
    Golland, P., Grimson, W.E.L., et al.: Small sample size learning for shape analysis of anatomical structures. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds.) MICCAI 2000. LNCS, vol. 1935, pp. 72–82. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  4. 4.
    Timoner, S.J., Golland, P., Kikinis, R., Shenton, M.E., et al.: Performance issues in shape classification. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002. LNCS, vol. 2488, pp. 355–362. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  5. 5.
    Saykin, A.J., Flashman, L.A., et al.: Principal components analysis of hippocampal shape in schizophrenia. In: Int. Congress on Schizophrenia Research (2003)Google Scholar
  6. 6.
    Brechbühler, C., Gerig, G., Kubler, O.: Parametrization of closed surfaces for 3D shape description. Computer Vision and Image Understanding 61, 154–170 (1995)CrossRefGoogle Scholar
  7. 7.
    Styner, M., Gerig, G., Pizer, S., Joshi, S.: Automatic and robust computation of 3D medial models incorporating object variability. Int. J. Computer Vision (2003)Google Scholar
  8. 8.
    Styner, M., Lieberman, J., Gerig, G.: Boundary and medial shape analysis of the hippocampus in schizophreni. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2879, pp. 464–471. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  9. 9.
    Kelemen, A., Szekely, G., Gerig, G.: Elastic model-based segmentation of 3-D neuroradiological data sets. IEEE Trans. on Medical Imaging 18, 828–839 (1999)CrossRefGoogle Scholar
  10. 10.
    Gerig, G., Styner, M., et al.: Hippocampal shape alterations in schizophrenia: Results of a new methodology. In: 11th Bi. W. Workshop on Schizophrenia (2002)Google Scholar
  11. 11.
    Gerig, G.: Selected Publications (2003),
  12. 12.
    Gerig, G., Styner, M.: Shape versus size: Improved understanding of the morphology of brain structures. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 24–32. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  13. 13.
    Shen, L., Ford, J., Makedon, F., Saykin, A.: Hippocampal shape analysis: Surfacebased representation and classification. In: SPIE Medical Imaging, Proc., vol. 5032, pp. 253–264 (2003)Google Scholar
  14. 14.
    Duda, R.O., Hart, P.E., et al.: Pattern Classification, 2nd edn. Wiley, NY (2000)Google Scholar
  15. 15.
    Ma, J., Zhao, Y., Ahalt, S.: OSU SVM Classifier Matlab Toolbox (ver 3.00) (2002),

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Li Shen
    • 1
  • James Ford
    • 1
  • Fillia Makedon
    • 1
  • Yuhang Wang
    • 1
  • Tilmann Steinberg
    • 1
  • Song Ye
    • 1
  • Andrew Saykin
    • 2
  1. 1.DEVLAB, Computer ScienceDartmouth CollegeHanoverUSA
  2. 2.Psychiatry and Radiology, Dartmouth Medical SchoolLebanonUSA

Personalised recommendations