Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-27T22:14:31.898Z Has data issue: false hasContentIssue false
  • English
  • Français

BAYESIAN COMPUTATIONAL METHODS FOR SPATIAL ANALYSIS OF IMAGES

Part of: Parametric inference Multivariate analysis Applications

Published online by Cambridge University Press:  08 January 2016

MATTHEW T. MOORES
MATTHEW T. MOORES*
Affiliation:
University of Warwick, Coventry CV4 7AL, UK email M.T.Moores@warwick.ac.uk
Rights & Permissions [Opens in a new window]

Abstract

An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'

Keywords

Bayesian inferencecone-beam computed tomographyimage segmentationPotts/Ising modelapproximate Bayesian computation

MSC classification

Primary: 62F15: Bayesian inference
Secondary: 62P10: Applications to biology and medical sciences 62H35: Image analysis
Type
Abstracts of Australasian PhD Theses
Information
Bulletin of the Australian Mathematical Society , Volume 93 , Issue 2 , April 2016 , pp. 345 - 346
Copyright
© 2016 Australian Mathematical Publishing Association Inc. 

References

Moores, M. T., Drovandi, C. C., Mengersen, K. and Robert, C. P., ‘Pre-processing for approximate Bayesian computation in image analysis’, Stat. Comput. 25(1) (2015), 23–33.CrossRefGoogle Scholar
Moores, M. T., Hargrave, C. E., Deegan, T., Poulsen, M., Harden, F. and Mengersen, K., ‘An external field prior for the hidden Potts model with application to cone-beam computed tomography’, Comput. Statist. Data Anal. 86 (2015), 27–41.Google Scholar
Moores, M. T., Hargrave, C. E., Harden, F. and Mengersen, K., ‘Segmentation of cone-beam CT using a hidden Markov random field with informative priors’, Proc. 17th Int. Conf. ICCR, J. Phys.: Conf. Ser., Vol. 489 (Melbourne, Australia, 2014), 012076.Google Scholar
Moores, M. T. and Mengersen, K., ‘bayesImageS: Bayesian methods for image segmentation using a hidden Potts model’, R package version 0.1-21, 2015; http://researchdatafinder.qut.edu.au/display/n11957.Google Scholar
Moores, M. T., Pettitt, A. N. and Mengersen, K., ‘Scalable Bayesian inference for the inverse temperature of a hidden Potts model’, Preprint, 2015, arXiv:1503.08066; http://arxiv.org/abs/1503.08066.Google Scholar
Potts, R. B., ‘Some generalized order-disorder transformations’, Proc. Cambridge Philos. Soc. 48(1) (1952), 106–109.CrossRefGoogle Scholar