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arXiv · 1404.2764

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

Matthew T. Moores·Catriona E. Hargrave·Fiona Harden·Kerrie Mengersen
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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. This paper introduces a method for representing spatial prior information as an external field in a hidden Potts model of the image lattice. The prior distribution of the latent pixel labels is a mixture of Gaussian fields, centred on the positions of the objects at a previous point in time. This model 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 model 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 on our test images from 87% to 6%.

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Matthew T. Moores, Catriona E. Hargrave, Fiona Harden, Kerrie Mengersen. 2014-04-10. An external field prior for the hidden Potts model, with application to cone-beam computed tomography. https://doi.org/10.1016/j.csda.2014.12.001

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