An external field prior for the hidden Potts model, with application to cone-beam computed tomography
| Autor: | Matthew T. Moores, Catriona Hargrave, Fiona Harden, Timothy Deegan, Michael Poulsen, Kerrie Mengersen |
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| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
FOS: Computer and information sciences
Statistics and Probability Cone beam computed tomography G.3 Imaging phantom Methodology (stat.ME) Prior probability Computer vision Segmentation Statistics - Methodology Mathematics Image-guided radiation therapy I.5.1 Pixel business.industry Applied Mathematics I.4.6 Pattern recognition Image segmentation Computational Mathematics Computational Theory and Mathematics Computer Science::Computer Vision and Pattern Recognition Artificial intelligence 62F15 Hidden Markov random field business |
| Popis: | 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. External field prior improves image segmentation accuracy.Manual segmentation of one image is used as a prior for subsequent images.Applicable to longitudinal imaging, such as image-guided radiation therapy. |
| Databáze: | OpenAIRE |
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