Maximum pseudolikelihood estimation for mixture-Markov random field segmentation of the brain

Chan, Amy, Wood, Ian A. and Fripp, Jurgen (2016). Maximum pseudolikelihood estimation for mixture-Markov random field segmentation of the brain. In: Alan Wee-Chung Liew, Brian Lovell, Clinton Fookes, Jun Zhou, Yongsheng Gao, Michael Blumstein and Zhiyong Wang, 2016 International Conference On Digital Image Computing: Techniques and Applications (DICTA). International Conference on Digital Image Computing - Techniques and Applications (DICTA), Gold Coast, Australia, (350-356). 30 November-2 December 2016. doi:10.1109/DICTA.2016.7797062


Author Chan, Amy
Wood, Ian A.
Fripp, Jurgen
Title of paper Maximum pseudolikelihood estimation for mixture-Markov random field segmentation of the brain
Conference name International Conference on Digital Image Computing - Techniques and Applications (DICTA)
Conference location Gold Coast, Australia
Conference dates 30 November-2 December 2016
Proceedings title 2016 International Conference On Digital Image Computing: Techniques and Applications (DICTA)
Journal name 2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016
Series 2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2016
Sub-type Fully published paper
DOI 10.1109/DICTA.2016.7797062
Open Access Status Not yet assessed
ISBN 9781509028962
Editor Alan Wee-Chung Liew
Brian Lovell
Clinton Fookes
Jun Zhou
Yongsheng Gao
Michael Blumstein
Zhiyong Wang
Start page 350
End page 356
Total pages 7
Language eng
Abstract/Summary A popular method for segmentation of magnetic resonance images (MRI) of the brain is to use a mixture model of tissue intensities with an underlying Markov Random Field (MRF) to incorporate spatial dependence between neighbouring voxels. Most current available mixture-MRF-based implementations require the user to fix the values of the MRF parameters. There is no clear method of choosing these values. In this paper we propose the use of maximum pseudolikelihood (MPL) estimation of the MRF parameters, which has not previously been used in the context of MRI segmentation, and compare this to an existing least-squares (LS) estimator. We compare the performance of both estimators on real brain MRI, and also to fixing the MRF parameters. We found that the MPL estimator was better able to recover expert manual segmentations than the LS estimator, as measured by Dice coefficient. Likewise, estimation by either method was superior to fixing the MRF parameters.
Subjects 1703 Computational Theory and Mathematics
1712 Software
1704 Computer Graphics and Computer-Aided Design
1706 Computer Science Applications
Keyword Mr-Images
Parameter-Estimation
Model
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Conference Paper
Sub-type: Fully published paper
Collections: School of Mathematics and Physics
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