Hostname: page-component-7bb8b95d7b-lvwk9 Total loading time: 0 Render date: 2024-09-13T14:14:41.605Z Has data issue: false hasContentIssue false
  • English
  • Français

Markov models in image analysis. An ophthalmic application

Published online by Cambridge University Press:  01 July 2016

A. Simó  and
E. De Ves
A. Simó
Affiliation:
Universitat Jaume I
E. De Ves
Affiliation:
Universidad de Valencia
Get access Rights & Permissions [Opens in a new window]

Abstract

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Papers
Information
Advances in Applied Probability , Volume 30 , Issue 2 , June 1998 , pp. 290 - 291
Copyright
Copyright © Applied Probability Trust 1998 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

[1] Aykroyd, R.G. and Green, PJ. (1991). Global and local priors, and the location of lesions using gamma-camera imagery. Philosophical Trans. Roy. Soc. London Ser. A, 337, 323–342.Google Scholar
[2] Besag, J.E. (1975). Statistical analysis of non lattice data. The statistician 24, 179–195.Google Scholar
[3] Besag, J.E. (1986). On the statistical analysis of dirty pictures. J. R. Statist. Soc. B 48, 259–302.Google Scholar
[4] Cressie, N.A.C. (1993). Statistics for spatial data. Wiley, Chichester.CrossRefGoogle Scholar
[5] Geman, S. and Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the bayesian restoration of images. IEEE Trans. PAMI, pp. 721–741.Google Scholar
[6] Geman, S. and McClure, D.E. (1987). Statistical methods for tomographic image reconstruction. In Proc. 46th Sess. Inst. Stat. Inst. Bulletin ISI 52, pp. 5–21.Google Scholar
[7] Karssemeijer, N. (1992). Application of bayesian methods to segmentation in medical images. In Lecture Notes in Statistics. Stochastic Models, Statistical Methods, and Algorithms in Image Analysis, 74, eds. Barone, P., Frigesi, A. and Piccioni, M.. Springer-Verlag, pp. 203–218.Google Scholar