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

Matthew T. Moores, Catriona E. Hargrave, Timothy Deegan, Michael Poulsen, Fiona Harden and Kerrie Mengersen

Computational Statistics & Data Analysis, 2015, vol. 86, issue C, 27-41

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.

Keywords: Bayesian image analysis; Hidden Markov random field; Image-guided radiation therapy; Ising/Potts model; Longitudinal imaging (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:86:y:2015:i:c:p:27-41

DOI: 10.1016/j.csda.2014.12.001

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