138
Views
1
CrossRef citations to date
0
Altmetric
Case Report

Comparison of hidden Markov chain models and hidden Markov random field models in estimation of computed tomography images

, , , &
Pages 46-55 | Received 19 May 2017, Accepted 02 May 2018, Published online: 30 May 2018
 

ABSTRACT

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.

Acknowledgments

This work is supported by the Swedish Research Council grant (Reg. No. 340-2013-5342) and Estonian institutional research funding IUT34-5. Adam Johansson is acknowledged for providing us with data. The authors would like to thank the Reviewer for several remarks that helped to improve the presentation of the article.

Additional information

Funding

Estonian Institutional Research Funding [IUT34-5]; Vetenskapsrådet [Reg. No. 340-2013-5342].

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 353.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.