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

作者：Moores Matthew T^*; Hargrave Catriona E; Deegan Timothy; Poulsen Michael; Harden Fiona; Mengersen Kerrie

来源：Computational Statistics & Data Analysis, 2015, 86: 27-41.

DOI：10.1016/j.csda.2014.12.001

• Bayesian image analysis
• Hidden Markov random field
• Image-guided radiation therapy
• Ising/Potts model
• Longitudinal imaging

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.

• 出版日期2015-6