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Uncertainty assessment for reconstructions based on deformable geometry
Author(s) -
Hanson K. M.,
Cunningham G. S.,
McKee R. J.
Publication year - 1997
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/(sici)1098-1098(1997)8:6<506::aid-ima2>3.0.co;2-e
Subject(s) - markov chain monte carlo , bayesian probability , boundary (topology) , context (archaeology) , computer science , algorithm , monte carlo method , artificial intelligence , iterative reconstruction , mathematics , computer vision , statistics , mathematical analysis , paleontology , biology
Deformable geometric models can be used in the context of Bayesian analysis to solve ill‐posed tomographic reconstruction problems. The uncertainties associated with a Bayesian analysis may be assessed by generating a set of random samples from the posterior, which may be accomplished using a Markov Chain Monte Carlo (MCMC) technique. We demonstrate the combination of these techniques for a reconstruction of a two‐dimensional object from two orthogonal noisy projections. The reconstructed object is modeled in terms of a deformable geometrically defined boundary with a uniform interior density yielding a nonlinear reconstruction problem. We show how an MCMC sequence can be used to estimate uncertainties in the location of the edge of the reconstructed object. © 1997 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 8, 506–512, 1997