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 journalArticlepeer-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 languageEnglish (US)
Pages (from-to)46-55
Number of pages10
JournalCommunications in Statistics Case Studies Data Analysis and Applications
Volume4
Issue number1
DOIs
StatePublished - Jan 2 2018
Externally publishedYes

Fingerprint Dive into the research topics of 'Comparison of hidden Markov chain models and hidden Markov random field models in estimation of computed tomography images'. Together they form a unique fingerprint.

Cite this