Automatic Construction of 3D Statistical Deformation Models Using Non-rigid Registration

  • D. Rueckert
  • A. F. Frangi
  • J. A. Schnabel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2208)


In this paper we introduce the concept of statistical deformation models (SDM) which allow the construction of average models of the anatomy and their variability. SDMs are built by performing a statistical analysis of the deformations required to map anatomical features in one subject into the corresponding features in another subject. The concept of SDMs is similar to active shape models (ASM) which capture statistical information about shapes across a population but offers several new advantages over ASMs: Firstly, SDMs can be constructed directly from images such as MR or CT without the need for segmentation which is usually a prerequisite for the construction of active shape models. Instead a non-rigid registration algorithm is used to compute the deformations required to establish correspondences between the reference subject and the subjects in the population class under investigation. Secondly, SDMs allow the construction of an atlas of the average anatomy as well as its variability across a population of subjects. Finally, SDMs take the 3D nature of the underlying anatomy into account by analysing dense 3D deformation fields rather than only the 2D surface shape of anatomical structures. We demonstrate the applicability of this new framework to MR images of the brain and show results for the construction of anatomical models from 25 different subjects.


Local Transformation Active Appearance Model Active Shape Model Average Deformation Population Class 
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.
    J. Gee, M. Reivich, and R. Bajcsy. Elastically deforming 3D atlas to match anatomical brain images. Journal of Computer Assisted Tomography, 17(2):225–236, 1993.CrossRefGoogle Scholar
  2. 2.
    G. E. Christensen, R. D. Rabbitt, and M. I. Miller. Deformable templates using large deformation kinematics. IEEE Transactions on Image Processing, 5(10):1435–1447, 1996.CrossRefGoogle Scholar
  3. 3.
    M. Bro-Nielsen and C. Gramkow. Fast fluid registration of medical images. In Proc. Visualization in Biomedical Computing (VBC’96), pages 267–276, 1996.Google Scholar
  4. 4.
    D. Rueckert. Non-rigid registration: Techniques and applications. In D. J. Hawkes, D. L. G. Hill, and J. Hajnal, editors, Medical Image Registration. CRC Press, 2001.Google Scholar
  5. 5.
    J. Talairach and P. Tournoux. Co-Planar Stereotactic Atlas of the Human Brain: 3-Dimensional Proportional System: An Approach to Cerebral Imaging. Stuttgart, 1988.Google Scholar
  6. 6.
    P. Thompson and A. W. Toga. Detection, visualization and animation of abnormal anatomic structure with a deformable probabilistic atlas based on random vector field transformations. Medical Image Analysis, 1(4):271–294, 1997.CrossRefGoogle Scholar
  7. 7.
    J. C. Gee and R. K. Bajcsy. Elastic Matching: Continuum Mechnanical and Probabilistic Analysis. In A. W. Toga, editor, Brain Warping, pages 183–197. Academic Press, 1999.Google Scholar
  8. 8.
    A. Guimond, J. Meunier, and J.-P. Thirion. Average brain models. Computer Vision and Image Understanding, 77:192–210, 2000.CrossRefGoogle Scholar
  9. 9.
    T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham. Active Shape Models-their training and application. Computer Vision and Image Understanding, 61(1):38–59, 1995.CrossRefGoogle Scholar
  10. 10.
    T. F. Cootes, C. Beeston, G. J. Edwards, and C. J. Taylor. A unified framework for atlas matching using active appearance models. In Proc. Information Processing in Medical Imaging (IPMI’99), pages 322–333, 1999.Google Scholar
  11. 11.
    Y. Wang and L. H. Staib. Elastic model based non-rigid registration incorporating statistical shape information. In Proc. MICCAI’ 98, pages 1162–1173, 1998.Google Scholar
  12. 12.
    A. F. Frangi, D. Rueckert, J. A. Schnabel, and W. J. Niessen. Automatic 3D ASM construction via atlas-based landmarking and volumetric elastic registration. In Proc. Information Processing in Medical Imaging: (IPMI’01), pages 78–91, 2001.Google Scholar
  13. 13.
    D. Rueckert, L. I. Sonoda, C. Hayes, D. L. G. Hill, M. O. Leach, and D. J. Hawkes. Nonrigid registration using free-form deformations: Application to breast MR images. IEEE Transactions on Medical Imaging, 18(8):712–721, 1999.CrossRefGoogle Scholar
  14. 14.
    J. A. Schnabel, D. Rueckert, M. Quist, J. M. Blackall, A. D. Castellano Smith, T. Hartkens, G. P. Penney, W. A. Hall, H. Liu, C. L. Truwit, F. A. Gerritsen, D. L. G. Hill and D. J. Hawkes. A Generic Framework for Non-Rigid Registration Based on Non-Uniform Multi-Level Free-Form Deformations. In Proc. MICCAI’ 01, 2001. In press.Google Scholar
  15. 15.
    C. Studholme, D. L. G. Hill, and D. J. Hawkes. An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognition, 32(1):71–86, 1998.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • D. Rueckert
    • 1
  • A. F. Frangi
    • 2
    • 3
  • J. A. Schnabel
    • 4
  1. 1.Visual Information Processing, Department of ComputingImperial CollegeLondonUK
  2. 2.Grupo de Tecnologia de las Comunicaciones, Departamento de Ingenieria Electronica y ComunicacionesUniversidad de ZaragozaSpain
  3. 3.Image Sciences InstituteUniversity Medical Center Utrecht (UMC)UtrechtNL
  4. 4.Computational Imaging Science GroupGuy’s Hospital, King’s CollegeLondonUK

Personalised recommendations