Bayesian multiscale analysis of images modeled as Gaussian Markov random fields
Computational Statistics & Data Analysis, 2012•Elsevier
A Bayesian multiscale technique for the detection of statistically significant features in noisy
images is proposed. The prior is defined as a stationary intrinsic Gaussian Markov random
field on a toroidal graph, which enables efficient computation of the relevant posterior
marginals. Hence the method is applicable to large images produced by modern digital
cameras. The technique is demonstrated in two examples from medical imaging.
images is proposed. The prior is defined as a stationary intrinsic Gaussian Markov random
field on a toroidal graph, which enables efficient computation of the relevant posterior
marginals. Hence the method is applicable to large images produced by modern digital
cameras. The technique is demonstrated in two examples from medical imaging.
A Bayesian multiscale technique for the detection of statistically significant features in noisy images is proposed. The prior is defined as a stationary intrinsic Gaussian Markov random field on a toroidal graph, which enables efficient computation of the relevant posterior marginals. Hence the method is applicable to large images produced by modern digital cameras. The technique is demonstrated in two examples from medical imaging.
Elsevier
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