A Generic Probabilistic Active Shape Model for Organ Segmentation

  • Andreas Wimmer
  • Grzegorz Soza
  • Joachim Hornegger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5762)


Probabilistic models are extensively used in medical image segmentation. Most of them employ parametric representations of densities and make idealizing assumptions, e.g. normal distribution of data. Often, such assumptions are inadequate and limit a broader application. We propose here a novel probabilistic active shape model for organ segmentation, which is entirely built upon non-parametric density estimates. In particular, a nearest neighbor boundary appearance model is complemented by a cascade of boosted classifiers for region information and combined with a shape model based on Parzen density estimation. Image and shape terms are integrated into a single level set equation. Our approach has been evaluated for 3-D liver segmentation using a public data base originating from a competition ( With an average surface distance of 1.0 mm and an average volume overlap error of 6.5 %, it outperforms other automatic methods and provides accuracy close to interactive ones. Since no adaptions specific to liver segmentation have been made, our probabilistic active shape model can be applied to other segmentation tasks easily.


Shape Model Active Shape Model Medical Image Segmentation Liver Segmentation Shape Term 
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.
    Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models – their training and application. CVIU 61(1), 38–59 (1995)Google Scholar
  2. 2.
    Cootes, T.F., Taylor, C.J.: Statistical models of appearance for medical image analysis and computer vision. In: SPIE Medical Imaging, vol. 4322, pp. 236–248 (2001)Google Scholar
  3. 3.
    Sethian, J.A.: Level Set Methods and Fast Marching Methods, 2nd edn. Cambridge University Press, New York (1999)zbMATHGoogle Scholar
  4. 4.
    Heimann, T., Styner, M., van Ginneken, B.: 3D Segmentation in the Clinic – A Grand Challenge. In: MICCAI Workshop Proceedings (2007)Google Scholar
  5. 5.
    Heimann, T., van Ginneken, B., Styner, M., et al.: Comparison and evaluation of methods for liver segmentation from CT datasets. In: IEEE TMI (2009)Google Scholar
  6. 6.
    Kainmüller, D., Lange, T., Lamecker, H.: Shape constrained automatic segmentation of the liver based on a heuristic intensity model. In: 3D Segmentation in the Clinic – A Grand Challenge, pp. 109–116 (2007)Google Scholar
  7. 7.
    Okada, T., Shimada, R., Sato, Y., Hori, M., Yokota, K., Nakamoto, M., et al.: Automated segmentation of the liver from 3D CT images using probabilistic atlas and multi-level statistical shape model. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 86–93. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  8. 8.
    Ling, H., Zhou, S.K., Zheng, Y., Georgescu, B., Suehling, M., Comaniciu, D.: Hierarchical, learning-based automatic liver segmentation. In: CVPR (2008)Google Scholar
  9. 9.
    Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. International Journal of Computer Vision 22(1), 61–79 (1997)zbMATHCrossRefGoogle Scholar
  10. 10.
    van Ginnecken, B., Frangi, A.F., Staal, J.J., ter Haar Romeny, B.M., Viergever, M.A.: Active shape model segmentation with optimal features. IEEE Transactions on Medical Imaging 21(8), 924–933 (2002)CrossRefGoogle Scholar
  11. 11.
    Wimmer, A., Soza, G., Hornegger, J.: Implicit active shape model employing boundary classifier. In: ICPR (2008)Google Scholar
  12. 12.
    Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. JCSS 55(1), 119–139 (1997)zbMATHMathSciNetGoogle Scholar
  13. 13.
    Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Chapman & Hall, New York (1984)zbMATHGoogle Scholar
  14. 14.
    Viola, P., Jones, M.: Robust real-time face detection. International Journal of Computer Vision 57(2), 137–154 (2004)CrossRefGoogle Scholar
  15. 15.
    Cremers, D., Osher, S.J., Soatto, S.: Kernel density estimation and intrinsic alignment for shape priors in level set segmentation. IJCV 69(3), 335–351 (2006)CrossRefGoogle Scholar
  16. 16.
    Parzen, E.: On the estimation of a probability density function and the mode. Annals of Mathematical Statistics 33, 1065–1076 (1962)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Andreas Wimmer
    • 1
    • 2
  • Grzegorz Soza
    • 2
  • Joachim Hornegger
    • 1
  1. 1.Chair of Pattern Recognition, Department of Computer ScienceFriedrich-Alexander UniversityErlangen-Nuremberg
  2. 2.Siemens Healthcare SectorComputed TomographyForchheimGermany

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