Comparison of hidden Markov chain models and hidden Markov random field models in estimation of computed tomography images
Artikel i vetenskaplig tidskrift, 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.

radiotherapy

hidden Markov model

magnetic resonance imaging

hidden Markov random field

pseudo-CT

Computed tomography

Författare

Kristi Kuljus

Tartu Ülikool

Fekadu L. Bayisa

Umeå universitet

David Bolin

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Jüri Lember

Tartu Ülikool

Jun Yu

Umeå universitet

Communications in Statistics Case Studies Data Analysis and Applications

23737484 (eISSN)

Vol. 4 1 46-55

Ämneskategorier

Sannolikhetsteori och statistik

Radiologi och bildbehandling

Medicinsk bildbehandling

DOI

10.1080/23737484.2018.1473059

Mer information

Senast uppdaterat

2020-03-10