Advertisement

Conditional Variability of Statistical Shape Models Based on Surrogate Variables

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5762)

Abstract

We propose to increment a statistical shape model with surrogate variables such as anatomical measurements and patient-related information, allowing conditioning the shape distribution to follow prescribed anatomical constraints. The method is applied to a shape model of the human femur, modeling the joint density of shape and anatomical parameters as a kernel density. Results show that it allows for a fast, intuitive and anatomically meaningful control on the shape deformations and an effective conditioning of the shape distribution, allowing the analysis of the remaining shape variability and relations between shape and anatomy. The approach can be further employed for initializing elastic registration methods such as Active Shape Models, improving their regularization term and reducing the search space for the optimization.

Keywords

Conditional Distribution Shape Distribution Femur Length Surrogate Variable Human Femur 
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.

References

  1. 1.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 484–498. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  2. 2.
    Kelemen, A., Székely, G., Gerig, G.: Elastic model-based segmentation of 3-D neuroradiological data sets. IEEE Trans. on Medical Imaging 18(10), 828–839 (1999)CrossRefGoogle Scholar
  3. 3.
    Rajamani, K.T., Hug, J., Nolte, L.-P., Styner, M.: Bone Morphing with statistical shape models for enhanced visualization. In: Proc. SPIE, vol. 5367, pp. 122–130 (2004)Google Scholar
  4. 4.
    Rao, A., Aljabar, P., Rueckert, D.: Hierarchical statistical shape analysis and prediction of sub-cortical brain structures. Medical Image Analysis 12, 55–68 (2008)CrossRefGoogle Scholar
  5. 5.
    Yang, Y.M., Rueckert, D., Bull, A.M.J.: Predicting the shapes of bones at a joint: application to the shoulder. Computer Methods in Biomech. and Biomed. Eng. 11(1), 19–30 (2008)CrossRefGoogle Scholar
  6. 6.
    Reyes Aguirre, M., Linguraru, M.G., Marias, K., Ayache, N., Nolte, L.P., Gonzalez Ballester, M.A.: Statistical Shape Analysis via Principal Factor Analysis. In: IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1216–1219 (2007)Google Scholar
  7. 7.
    Üzümcü, M., Frangi, A.F., Sonka, M., Reiber, J.H.C., Lelieveldt, B.: ICA vs. PCA active appearance models: Application to cardiac MR segmentation. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2878, pp. 451–458. Springer, Heidelberg (2003)Google Scholar
  8. 8.
    Üzümcü, M., Frangi, A.F., Reiber, J.H.C., Lelieveldt, B.P.F.: Independent Component Analysis in Statistical Shape Models. In: Medical Imaging 2003: Image Processing, Proceedings of SPIE, vol. 5032, pp. 375–383 (2003)Google Scholar
  9. 9.
    Albrecht, T., Knothe, R., Vetter, T.: Modeling the Remaining Flexibility of Partially Fixed Statistical Shape Models. In: Workshop on the Mathematical Foundations of Computational Anatomy, MFCA 2008, New York, USA, September 6 (2008)Google Scholar
  10. 10.
    Sierra, R., Zsemlye, G., Székely, G., Bajka, M.: Generation of variable anatomical models for surgical training simulators. Medical Image Analysis 10, 275–285 (2006)CrossRefGoogle Scholar
  11. 11.
    Styner, M.A., Rajamani, K.T., Nolte, L.-P., Zsemlye, G., Székely, G., Taylor, C.J., Davies, R.H.: Evaluation of 3D correspondence methods for model building. In: Taylor, C.J., Noble, J.A. (eds.) IPMI 2003. LNCS, vol. 2732, pp. 63–75. Springer, Heidelberg (2003)Google Scholar
  12. 12.
    Timm, N.H.: Applied Multivariate Statistics. Springer, Heidelberg (2002)Google Scholar
  13. 13.
    Abdi, H.: Factor rotations. In: Lewis-Beck, M., Bryman, A., Futing, T. (eds.) Encyclopedia for research methods for the social sciences, pp. 978–982. Sage, Thousand Oaks (2003)Google Scholar
  14. 14.
    Wand, M.P., Jones, M.C.: Kernel Smoothing. Monographs on Statistics and Applied Probability, vol. 60. Chapman & Hall, Boca Raton (1995)Google Scholar
  15. 15.
    Scott, D.W.: Multivariate Density Estimation: Theory, Practice, and Visualization. J. Wiley & Sons, Chichester (1992)zbMATHCrossRefGoogle Scholar
  16. 16.
    Zhang, X., King, M.L., Hyndman, R.J.: Bandwidth Selection for Multivariate Kernel Density using MCMC. In: Australasian Meetings, p. 120. Econometric Society (2004)Google Scholar
  17. 17.
    Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Diffeomorphic Demons: Efficient Non-parametric Image Registration. NeuroImage 45(Suppl. 1), 61–72 (2009)CrossRefGoogle Scholar
  18. 18.
    Samaha, A.A., Ivanov, A.V., Haddad, J.J., Kolesnik, A.I., Baydoun, S., Yashina, I.N., Samaha, R.A., Ivanov, D.A.: Biomechanical and system analysis of the human femoral bone: correlation and anatomical approach. Journal of Orthopaedic Surgery and Research 2, 8 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  1. 1.Computer Vision LaboratoryETHZZürichSwitzerland
  2. 2.ARTORG Center for Biomedical Engineering ResearchUniversity of BernBernSwitzerland

Personalised recommendations