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 and 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 27-41. doi:10.1016/j.csda.2014.12.001


Author Moores, Matthew T.
Hargrave, Catriona E.
Deegan, Timothy
Poulsen, Michael
Harden, Fiona
Mengersen, Kerrie
Title An external field prior for the hidden Potts model with application to cone-beam computed tomography
Journal name Computational Statistics and Data Analysis   Check publisher's open access policy
ISSN 0167-9473
1872-7352
Publication date 2015-06
Year available 2015
Sub-type Article (original research)
DOI 10.1016/j.csda.2014.12.001
Open Access Status Not Open Access
Volume 86
Start page 27
End page 41
Total pages 15
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Collection year 2016
Language eng
Abstract 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.
Keyword Bayesian image analysis
Hidden Markov random field
Image-guided radiation therapy
Ising/Potts mode
Longitudinal imaging
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Mater Research Institute-UQ (MRI-UQ)
Non HERDC
 
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Created: Fri, 01 Apr 2016, 11:24:38 EST by Julia McCabe on behalf of Mater Clinical School