bayesImagesS: Bayesian methods for image segmentation using a hidden Potts model
R package, bayesimageS, implements Bayesian image analysis using the hidden Potts model with external field prior. Ltent labels are sampled using chequerboard updating or Swendsen-Wang. Algorithms for the smoothing parameter include pseudolikelihood, path sampling, the exchange algorithm and approximate Bayesian computation. This R package was written during a QUT based PhD, which was a collaborative project between QUT and the Radiation Oncology Mater Centre, Queensland Health titled, Bayesian computational methods for spatial analysis of images. It is an R source package (.tar.gz) containing.R and .cpp (R and C++) source code.
M. Moores, C. Hargrave, T. Deegan, M. Poulsen, F. Harden & K. Mengersen (2015). An external field prior for the hidden Potts model with application to cone-beam computed tomography. Computational Statistics & Data Analysis. http://dx.doi.org/10.1016/j.csda.2014.12.001
M. Moores, C. C. Drovandi, K. Mengersen & C. P. Robert (2014). Pre-processing for approximate Bayesian computation in image analysis. Statistics & Computing. http://dx.doi.org/10.1007/s11222-014-9525-6
Cite this collection
M. Moores & K.Mengersen (2015) bayesImageS: Bayesian methods for image segmentation using a hidden Potts model. R package version 0.1-21
Moores,Matthew; Mengersen,Kerrie. (2016): bayesImagesS: Bayesian methods for image segmentation using a hidden Potts model. [Queensland University of Technology]. https://doi.org/10.4225/09/584e37ae2a6b9
Radiation Oncology Mater Centre, Queensland Health http://cancercare.mater.org.au/home/services/radiation-oncology.aspx
© Queensland University of Technology, 2016.
Dates of development
From 2011-01-01 to 2015-01-01
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Published - Open Access