Skip to main content
Book cover

Biennial International Conference on Information Processing in Medical Imaging

IPMI 1997: Information Processing in Medical Imaging pp 1–14Cite as

Automatic construction of eigenshape models by Genetic Algorithm

Part of the Lecture Notes in Computer Science book series (LNCS,volume 1230)

Abstract

A new approach to the problem of automatic construction of eigenshape models is presented. Eigenshape models have proved to be successful in a variety of medical image analysis problems. However, automatic model construction is a difficult problem, and in many applications the models are built by hand — a painstaking process. Previous attempts to produce models automatically have been applicable only in specific cases or under certain assumptions. We show that the problem can be understood very simply in terms of shape symmetries. The pose and parameterisation of each shape must be chosen so as to produce a model that is compact and specific. We define an objective function that measures these properties. The problem of automatic model construction is thus reduced to an optimisation problem. We show that the objective function we define can be optimised by a Genetic Algorithm, and produces models that are better than hand built ones.

Keywords

  • Genetic Algorithm
  • Symmetry Transformation
  • Bezier Curve
  • Active Shape Model
  • Genetic Algorithm Parameter

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.

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/3-540-63046-5_1
  • Chapter length: 14 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   109.00
Price excludes VAT (USA)
  • ISBN: 978-3-540-69070-2
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   139.00
Price excludes VAT (USA)
Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. A. Baumberg and D. Hogg. Learning flexible models from image sequences. In 3nd European Conference on Computer Vision, pages 299–308, Stockholm, 1994.

    Google Scholar 

  2. A. Baumberg and D. Hogg. An adaptive eigenshape model. In D. Pycock, editor, 6th British Machine Vison Conference, pages 87–96. BMVA Press, Sept. 1995.

    Google Scholar 

  3. I. Cohen, N. Ayache, and P. Sulger. Tracking points on deformable objects using curvature information. In G. Sandini, editor, 2nd European Conference on Computer Vision, pages 458–466. Springer-Verlag, May 1992.

    Google Scholar 

  4. T. F. Cootes, A. Hill, and C. J. Taylor. Rapid and more accurate medical image interpretation using active shape models. In 14th Conference on Information Processing in Medical Imaging, pages (371–372), France, June 1995.

    Google Scholar 

  5. T. F. Cootes, A. Hill, C. J. Taylor, and J. Haslam. The use of active shape models for locating structures in medical images. In H. H. Barrett and A. F. Gmitro, editors, 13th Conference on Information Processing in Medical Imaging, pages 33–47, Flagstaff, Arizona, USA, June 1993. Springer-Verlag.

    Google Scholar 

  6. 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, Jan. 1995.

    Google Scholar 

  7. L. Davis. Genetic Algorithms and Simulated Annealing. Pitman, 1987.

    Google Scholar 

  8. K. Delibasis and P. E. Undrill. Anatomical object recognition using deformable geometric models. Image and Vision Computing, 12(7):423–433, Sept. 1994.

    Google Scholar 

  9. J. Duncan, R. L. Owen, L. H. Staib, and P. Anandan. Measurement of non-rigid motion using contour shape descriptors. In IEEE Conference on Computer Vision and Pattern Recognition, pages 318–324, 1991.

    Google Scholar 

  10. D. E. Goldberg. Genetic Algorithms in Search, Optimisation and Machine Learning. Addison-Wesley, 1989.

    Google Scholar 

  11. C. Goodall. Procrustes methods in the statistical analysis of shape. Journal of the Royal Statistical Society B, 53(2):285–339, 1991.

    Google Scholar 

  12. A. Hill, T. F. Cootes, C. J. Taylor, and K. Lindley. Medical image interpretation: A generic approach using deformable templates. Journal of Medical Informatics, 19(1):47–59, 1994.

    Google Scholar 

  13. A. Hill and C. J. Taylor. Automatic landmark generation for point distribution models. In E. Hancock, editor, 5th British Machine Vison Conference, pages 429–438. BMVA Press, Sept. 1994.

    Google Scholar 

  14. A. Hill and C. J. Taylor. A framework for automatic landmark identification using a new method of non-rigid correspondence. IEEE Transactions on Pattern Analysis and Machine Intelligence, page (submitted to), 1996.

    Google Scholar 

  15. A. Hill and C. J. Taylor. A method of non-rigid correspondence for automatic landmark identification. In 7th British Machine Vison Conference, pages 323–332. BMVA Press, Sept. 1996.

    Google Scholar 

  16. C. Kambhamettu and D. B. Goldgof. Point correspondence recovery in non-rigid motion. In IEEE Conference on Computer Vision and Pattern Recognition, pages 222–227, 1992.

    Google Scholar 

  17. K. V. Mardia, J. T. Kent, and A. N. Walder. Statistical shape models in image analysis. In 23rd Symposium on the Interface, pages 259–268. IEEE, Computer Society Press, 1995.

    Google Scholar 

  18. A. P. Pentland. Automatic extraction of deformable part models. International Journal of Computer Vision, 4(2):107–126, 1990.

    Google Scholar 

  19. S. Sclaroff and A. Pentland. A modal framework for correspondence and description. In 4th International Conference on Computer Vision, pages 308–313, Berlin, May 1993. IEEE Computer Society Press.

    Google Scholar 

  20. G. L. Scott and H. C. Longuet-Higgins. An algorithm for associating the features of two images. Proceedings of the Royal Statistical Society of London, 244:21–26, 1991.

    Google Scholar 

  21. L. S. Shapiro and J. M. Brady. A modal approach to feature-based correspondence. In P. Mowforth, editor, 2nd British Machine Vison Conference, pages 78–85. Springer-Verlag, Sept. 1991.

    Google Scholar 

  22. H. D. Tagare, D. O'Shea, and A. Rangarajan. A geometric criterion for shape-based non-rigid correspondence. In 5th International Conference on Computer Vision, pages 434–439, June 1995.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Wolfson Image Analysis Unit, Department of Medical Bio-Physics, University of Manchester, UK

    Aaron C. W. Kotcheff & Chris J. Taylor

Authors
  1. Aaron C. W. Kotcheff
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chris J. Taylor
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

    Rights and permissions

    Reprints and Permissions

    Copyright information

    © 1997 Springer-Verlag Berlin Heidelberg

    About this paper

    Cite this paper

    Kotcheff, A.C.W., Taylor, C.J. (1997). Automatic construction of eigenshape models by Genetic Algorithm. In: Duncan, J., Gindi, G. (eds) Information Processing in Medical Imaging. IPMI 1997. Lecture Notes in Computer Science, vol 1230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63046-5_1

    Download citation

    • DOI: https://doi.org/10.1007/3-540-63046-5_1

    • Published:

    • Publisher Name: Springer, Berlin, Heidelberg

    • Print ISBN: 978-3-540-63046-3

    • Online ISBN: 978-3-540-69070-2

    • eBook Packages: Springer Book Archive