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A Bayesian analysis of dual autoradiographic images

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  • Johnson, Timothy D.
  • Piert, Morand

Abstract

We present a Bayesian bivariate image model and apply it to a study that was designed to investigate the relationship between hypoxia and angiogenesis in an animal tumor model. Two radiolabeled tracers (one measuring angiogenesis, the other measuring hypoxia) were simultaneously injected into the animals, the tumors were removed and autoradiographic images of the tracer concentrations were obtained. We model correlation between tracers with a mixture of bivariate normal distributions and the spatial correlation inherent in the images by means of the Potts model. Although the Potts model is typically used for image segmentation, we use it solely as a device to account for spatial correlation. The number of classes in the model is assumed unknown and is estimated via reversible jump MCMC, marginalizing over the number of classes for posterior inference. We present the model and estimation method using set theory notation which will assist us in introducing a novel reallocation scheme used in the reversible jump proposals. We also estimate the spatial regularization parameter in the Potts model prior. Via simulation studies, we show that it is necessary to account for both the spatial correlation and the correlation between the two tracers.

Suggested Citation

  • Johnson, Timothy D. & Piert, Morand, 2009. "A Bayesian analysis of dual autoradiographic images," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4570-4583, October.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:12:p:4570-4583
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    References listed on IDEAS

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    1. Green P.J. & Richardson S., 2002. "Hidden Markov Models and Disease Mapping," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1055-1070, December.
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    Cited by:

    1. Jin, Ick Hoon & Liang, Faming, 2014. "Use of SAMC for Bayesian analysis of statistical models with intractable normalizing constants," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 402-416.

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