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

Kristi Kuljus; Fekadu L. Bayisa; David Bolin; Jüri Lember; Jun Yu

doi:10.1080/23737484.2018.1473059

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

Kristi Kuljus, Fekadu L. Bayisa, David Bolin, Jüri Lember, Jun Yu

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

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.

Original language	English (US)
Pages (from-to)	46-55
Number of pages	10
Journal	Communications in Statistics Case Studies Data Analysis and Applications
Volume	4
Issue number	1
DOIs	https://doi.org/10.1080/23737484.2018.1473059
State	Published - Jan 2 2018
Externally published	Yes

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10.1080/23737484.2018.1473059

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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.",

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AU - Bolin, David

AU - Lember, Jüri

AU - Yu, Jun

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AB - 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.

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

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