An external field prior for the hidden Potts model with application to cone-beam computed tomography

Moores, Matthew T., Hargrave, Catriona E., Deegan, Timothy, Poulsen, Michael, Harden, Fiona, & Mengersen, Kerrie (2015) An external field prior for the hidden Potts model with application to cone-beam computed tomography. Computational Statistics and Data Analysis, 86, pp. 27-41.

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In images with low contrast-to-noise ratio (CNR), the information gain from the observed pixel values can be insufficient to distinguish foreground objects. A Bayesian approach to this problem is to incorporate prior information about the objects into a statistical model. A method for representing spatial prior information as an external field in a hidden Potts model is introduced. This prior distribution over the latent pixel labels is a mixture of Gaussian fields, centred on the positions of the objects at a previous point in time. It is particularly applicable in longitudinal imaging studies, where the manual segmentation of one image can be used as a prior for automatic segmentation of subsequent images. The method is demonstrated by application to cone-beam computed tomography (CT), an imaging modality that exhibits distortions in pixel values due to X-ray scatter. The external field prior results in a substantial improvement in segmentation accuracy, reducing the mean pixel misclassification rate for an electron density phantom from 87% to 6%. The method is also applied to radiotherapy patient data, demonstrating how to derive the external field prior in a clinical context.

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ID Code: 82307
Item Type: Journal Article
Refereed: Yes
Additional URLs:
Keywords: Bayesian image analysis, Hidden Markov random field, Image-guided radiation therapy, Ising/Potts model, Longitudinal imaging
DOI: 10.1016/j.csda.2014.12.001
ISSN: 0167-9473
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Biostatistics (010402)
Divisions: Current > Research Centres > ARC Centre of Excellence for Mathematical & Statistical Frontiers (ACEMS)
Current > Schools > School of Chemistry, Physics & Mechanical Engineering
Current > Schools > School of Clinical Sciences
Current > QUT Faculties and Divisions > Faculty of Health
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Current > Institutes > Institute of Health and Biomedical Innovation
Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2015 Elsevier B.V.
Copyright Statement: Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Deposited On: 08 Mar 2015 23:11
Last Modified: 02 Jul 2016 10:05

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