 A Bayesian analysis of dual autoradiographic imagesTimothy D. Johnson and Morand Piert Computational Statistics & Data Analysis, 2009, vol. 53, issue 12, 4570-4583 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. Date: 2009 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:53:y:2009:i:12:p:4570-4583 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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