ASM Driven Snakes in Rheumatoid Arthritis Assessment

  • Georg Langs
  • Philipp Peloschek
  • Horst Bischof
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2749)

Abstract

In this paper a method is proposed that combines active shape models (ASM) and active contours (snakes) in order to identify fine structured contours with high accuracy and stability. Based on an estimate of the contour position by an active shape model the accuracy of the landmarks and the contour in between is enhanced by applying an iterative active contour algorithm to a set of gray value profiles extracted orthogonally to the interpolation obtained by the ASM. The active shape model is trained with a set of training shapes, whereas the snake detects the contour with fewer constraints. This is of particular importance for the assessment of pathological changes of bones like erosive destructions caused by rheumatoid arthritis.

Keywords

Active Contour Active Shape Model Landmark Position Hand Radiograph Bone Contour 
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.
    F. J. Canny. A computational approach to edge detection. IEEE Trans. Pattern Analysis Machine Intelligence, 8(6):679–698, 1986.CrossRefGoogle Scholar
  2. 2.
    Timothy F. Cootes, A. Hill, Christopher J. Taylor, and J. Haslam. The use of active shape models for locating structures in medical images. Image and Vision Computing, 12(6):355–366, 1994.CrossRefGoogle Scholar
  3. 3.
    Timothy F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham. Training models of shape from sets of examples. In Proc. British Machine Vision Conference, pages 266–275, Berlin, 1992. Springer.Google Scholar
  4. 4.
    Daniel Cremers, Timo Kohlberger, and Christoph Schnorr. Nonlinear shape statistics in mumford-shah based segmentation. In ECCV (2), pages 93–108, 2002.Google Scholar
  5. 5.
    G. Hamarneh and T. Gustavsson. Combining snakes and active shape models for segmenting the human left ventricle in echocardiographic images. IEEE Computers in Cardiology, 27:115–118, 2000.Google Scholar
  6. 6.
    M. Kass, A. Witkin, and D. Terzopoulos. Snakes: Active contour models. International Journal on Computer Vision, 1:321–331, 1988.CrossRefGoogle Scholar
  7. 7.
    Georg Langs, Philipp Peloschek, and Horst Bischof. Locating joints in hand radiographs. In Proceedings of Computer Vision Winter Workshop, 2003.Google Scholar
  8. 8.
    Eugene T.Y. Lee. Choosing nodes in parametric curve interpolation. Computer-Aided Design, 21:363–370, 1989.MATHCrossRefGoogle Scholar
  9. 9.
    T. Nopola, A. Järvi, E. Svedström, and O. Nevalainen. Segmenting bones from wristhand radiographs. Technical Report TUCS Technical Report No. 371, Turku Centre for Computer Science, 2000.Google Scholar
  10. 10.
    Désirée van der Heijde. Structural damage in rheumatoid arthritis as visualized through radiographs. Arthritis Res, 4(2):29–33, 2002.CrossRefGoogle Scholar
  11. 11.
    Chenyang Xu and Jerry L. Prince. Snakes, shapes and gradient vector flow. IEEE Transactions on image Processing, 7(3):359–369, March 1998.MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Georg Langs
    • 1
  • Philipp Peloschek
    • 2
  • Horst Bischof
    • 3
  1. 1.Pattern Recognition and Image Processing Group 183/2Vienna University of TechnologyViennaAustria
  2. 2.Department of Diagnostic RadiologyUniversity of ViennaViennaAustria
  3. 3.Institute for Computer Graphics and VisionTU GrazGrazAustria

Personalised recommendations