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PhD Final Seminar

July 8, 2014

Thesis Title: Bayesian Computational Methods for Spatial Analysis of Images.

Candidate: Matthew T. Moores

Discipline: Statistical Science

Review Panel:
Prof. Ian Turner
Prof. Tony Pettitt
Dr. Fiona Harden
Dr. Chris Drovandi

Abstract:
This thesis is motivated by an important applied problem in image-guided radiation therapy. The aim is to assist in interpreting cone-beam computed tomography (CT) of radiotherapy patients by labelling the image pixels according to tissue type. These medical images have poor contrast-to-noise ratio (CNR), thus external sources of information are needed for accurate segmentation. Such sources include the individualised treatment plan, which is based on a diagnostic-quality CT scan that has been manually labelled by a clinician. We also use published studies of physiological variability to derive an estimate of spatial uncertainty.

To address this problem, we adopt a hidden Potts model of the image lattice. We introduce a method for deriving and representing the spatial prior for each patient as an external field in the hidden Potts model. Tissue density estimates are derived from the planning CT and adjusted to account for differences in image modality, forming priors for the noise parameters. These priors can be updated sequentially as more images of the patient are acquired.

Scalable computational algorithms are required for Bayesian inference on 3D volumetric images of this size. We evaluate the existing methods for intractable likelihood problems, including path sampling, pseudolikelihood and the approximate exchange algorithm. We introduce a precomputation step that involves fitting a binding function between the parameters and sufficient statistic of the Potts model. Using this precomputation, we achieve two orders of magnitude improvement in the scalability of approximate Bayesian computation with sequential Monte Carlo (ABC-SMC).

SPEAKER BACKGROUND
Matthew completed a Bachelor of Information Technology at QUT in 1996 and a Master of Mathematical Science in 2008. Prior to commencing his PhD, he was involved with the Visible Cell project at the Institute for Molecular Bioscience, UQ. He has also worked in R&D for various international companies, including DEC, Compaq and Oracle. He was a member of the development teams for the FaceWorks facial animation software, SpeechBot multimedia search engine, AgileTV speech-enabled programme guide, and the Healthcare Transaction Base. Matthew’s research interests include computational statistics, Bayesian inference, and image analysis

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From → Imaging, MCMC

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