Comparison of hidden Markov chain models and hidden Markov random field models in estimation of computed tomography images
Journal article, 2018

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.


hidden Markov model

magnetic resonance imaging

hidden Markov random field


Computed tomography


Kristi Kuljus

University of Tartu

Fekadu L. Bayisa

Umeå University

David Bolin

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Jüri Lember

University of Tartu

Jun Yu

Umeå University

Communications in Statistics Case Studies Data Analysis and Applications

23737484 (eISSN)

Vol. 4 1 46-55

Subject Categories

Probability Theory and Statistics

Radiology, Nuclear Medicine and Medical Imaging

Medical Image Processing



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