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Comparison of hidden Markov chain models and hidden Markov random field models in estimation of computed tomography images
University of Tartu, Estonia.
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
Department of Mathematical Sciences, Chalmers University of Technology, Sweden.
University of Tartu, Estonia.
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2018 (English)In: Communications in Statistics: Case Studies, Data Analysis and Applications, ISSN 2373-7484, Vol. 4, no 1, p. 46-55Article in journal (Refereed) Published
Abstract [en]

Two principal areas of application for estimated computed tomography (CT) images are dose calculations in magnetic resonance imaging (MRI) based radiotherapy treatment planning and attenuation correction for positron emission tomography (PET)/MRI. The main purpose of this work is to investigate the performance of hidden Markov (chain) models (HMMs) in comparison to hidden Markov random field (HMRF) models when predicting CT images of head. Obtained results suggest that HMMs deserve a further study for investigating their potential in modeling applications, where the most natural theoretical choice would be the class of HMRF models.

Place, publisher, year, edition, pages
Taylor & Francis Group, 2018. Vol. 4, no 1, p. 46-55
Keywords [en]
Computed tomography; hidden Markov model; hidden Markov random field; magnetic resonance imaging; pseudo-CT; radiotherapy
National Category
Probability Theory and Statistics Medical Image Processing
Research subject
Mathematical Statistics
Identifiers
URN: urn:nbn:se:umu:diva-148242DOI: 10.1080/23737484.2018.1473059OAI: oai:DiVA.org:umu-148242DiVA, id: diva2:1211521
Projects
Statistical modelling and intelligent data sampling in MRI and PET measurements for cancer therapy assessment
Funder
Swedish Research Council, 340-2013-5342Available from: 2018-05-31 Created: 2018-05-31 Last updated: 2018-06-09

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Publisher's full texthttps://doi.org/10.1080/23737484.2018.1473059

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Bayisa, FekaduYu, Jun

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  • en-US
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  • nn-NO
  • nn-NB
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  • Other locale
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  • asciidoc
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