  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%.
