Skip to main content
Log in

Identification of partly destroyed objects using deformable templates

  • Published:
Statistics and Computing Aims and scope Submit manuscript

Abstract

This article addresses the problem of identification of partly destroyed human melanoma cancer cells in confocal microscopy imaging. Complete cancer cells are nearly circular and most of them have a nearly homogeneous boundary and interior region. A deformable template (Grenander, 1993) is well suited for these complete cells and models a cell as a natural deformed template or prototype. We will in this article focus on the remaining cells which have lost parts of the boundary region most probably due to a 'capping' phenomenon. We can interpret these cells as being partly destroyed, where in our statistical model the lost part of the boundary region is generated by a destructive deformation field acting and living on the cell or template. By doing simultaneous inference for both the natural and destructive deformation field, we are able to obtain reliable estimates of the outline in addition to where on the boundary the cell is destroyed. We apply our model to identifying partly destroyed human melanoma cancer cells with good results.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Baddeley, A. J. (1992) Errors in binary images and a L p version of the Hausdorff metric, Nieuw Archiefvoor Wiskunde, 10, 157–183.

    Google Scholar 

  • Baddeley, A. J. and Van Lieshout, M. N. M. (1993) Stochastic geometry models in high-level vision, In: Statistics and Images, K. V. Mardia and G. K. Kanji (eds), Vol. 20, Chapter 11. Carfax Publishing, Abingdon. pp. 235–256.

    Google Scholar 

  • Geman, S. and Geman, D. (1984) Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images, IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721–741.

    Google Scholar 

  • Gilks, W. R., Richardson, S. and Spiegelhalter, D. J. (1996) Markov Chain Monte Carlo in Practice, Oxford: Clarendon Press.

    Google Scholar 

  • Grenander, U. (1993) General Pattern Theory, London: Chapman & Hall.

    Google Scholar 

  • Grenander, U. and Miller, M. I. (1994) Representations of knowledge in complex systems (with discussion), Journal of the Royal Statistical Society, Series B, 56(4), 549–603.

    Google Scholar 

  • Grenander, U., Chow, Y. and Keenan, D. M. (1991) Hands: a Pattern Theoretic Study of Biological Shapes, Research Notes on Neural Computing, Springer, Berlin.

    Google Scholar 

  • Hurn, M. A. (1996) Bayesian image analysis in confocal fluorescence microscopy, Statistical Research Report 96:01. School of Mathematical Sciences, University of Bath, Bath, UK.

    Google Scholar 

  • Jain, A. K., Zhong, Y. and Lakshmanan, S. (1996) Object matching using deformable templates, IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(3), 267–278.

    Google Scholar 

  • Kent, J. T., Mardia, K. V. and Walder, A. N. (1996) Conditional cyclic Markov random fields, Advances in Applied Probability (SGSA), 28, 1–12.

    Google Scholar 

  • Qian, W. and Mardia, K. V. (1995) Recognition of multiple objects with occlusions, Stat 95/01. University of Leeds, Leeds, UK.

    Google Scholar 

  • Rue, H. (1995) New loss functions in Bayesian imaging, Journal of the American Statistical Association, 90, 900–908.

    Google Scholar 

  • Rue, H. and Hurn, M. A. (1997) Bayesian object identification, Statistics No. 6. Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway.

    Google Scholar 

  • Rue, H. and Syversveen, A. R. (1998) Bayesian object recognition with Baddeley's delta loss, Advances in Applied Probability (SGSA), Vol. 30.

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rue, H., Husby, O.K. Identification of partly destroyed objects using deformable templates. Statistics and Computing 8, 221–228 (1998). https://doi.org/10.1023/A:1008953210305

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1008953210305

Navigation