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

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

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.

Suggested Citation

  • 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 & Data Analysis, Elsevier, vol. 86(C), pages 27-41.
    • Handle: RePEc:eee:csdana:v:86:y:2015:i:c:p:27-41
    DOI: 10.1016/j.csda.2014.12.001
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    References listed on IDEAS

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    1. Alston, C.L. & Mengersen, K.L. & Robert, C.P. & Thompson, J.M. & Littlefield, P.J. & Perry, D. & Ball, A.J., 2007. "Bayesian mixture models in a longitudinal setting for analysing sheep CAT scan images," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4282-4296, May.
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    1. Maria Masotti & Lin Zhang & Ethan Leng & Gregory J. Metzger & Joseph S. Koopmeiners, 2023. "A novel Bayesian functional spatial partitioning method with application to prostate cancer lesion detection using MRI," Biometrics, The International Biometric Society, vol. 79(2), pages 604-615, June.
    2. Adrien Ickowicz & Jessica Ford & Keith Hayes, 2019. "A Mixture Model Approach for Compositional Data: Inferring Land-Use Influence on Point-Referenced Water Quality Measurements," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(4), pages 719-739, December.

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