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

Journal Article


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.

Authors


  •   Moores, Matt T.
  •   Hargrave, Catriona (external author)
  •   Deegan, Timothy (external author)
  •   Poulsen, Michael (external author)
  •   Harden, Fiona (external author)
  •   Mengersen, Kerrie (external author)

Publication Date


  • 2015

Citation


  • Moores, M. T., Hargrave, C. E., Deegan, T., Poulsen, M., Harden, F. & Mengersen, K. (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.

Scopus Eid


  • 2-s2.0-84922706423

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/1681

Number Of Pages


  • 14

Start Page


  • 27

End Page


  • 41

Volume


  • 86

Place Of Publication


  • Netherlands

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.

Authors


  •   Moores, Matt T.
  •   Hargrave, Catriona (external author)
  •   Deegan, Timothy (external author)
  •   Poulsen, Michael (external author)
  •   Harden, Fiona (external author)
  •   Mengersen, Kerrie (external author)

Publication Date


  • 2015

Citation


  • Moores, M. T., Hargrave, C. E., Deegan, T., Poulsen, M., Harden, F. & Mengersen, K. (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.

Scopus Eid


  • 2-s2.0-84922706423

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/1681

Number Of Pages


  • 14

Start Page


  • 27

End Page


  • 41

Volume


  • 86

Place Of Publication


  • Netherlands