EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Chiverton, J.

English

Name not available

Probabilistic partial volume modelling of biomedical tomographic image data

Portsmouth University Research Portal (Pure)

A fast model independent method for automatic correction of intensity non-uniformity in MRI data.

A novel method for volume determination using MR image sequences.

Adaptive segmentation of MRI data.

Bayesian estimation of layers from multiple images.

Biological underpinnings of anatomic consistency and variability in the human brain.

Eigenimage filtering in MR imaging.

Emission Tomography The Fundamentals of PET and SPECT.

Fast and robust parameter estimation for statistical partial volume models in brain MRI.

Further Engineering Mathematics.

Image Analysis, Random Fields, and Markov Chain Monte Carlo Methods Second Edition.

Image registration and the construction of multidimensional brain atlases.

Magnetic resonance image analysis by information theoretic criteria and stochastic site models.

Mixture models with adaptive spatial regularization for segmentation with an application to fMRI data.

Multi-dimensional medical image segmentation with gradient analysis.

Noise filtering and testing for MR using a multidimensional partial volume model.

Notes on characteristic functions.

Partial volume tissue segmentation using grey-level gradient.

Quantifying the partial volume effect in PET using Benford’s Law. Submitted to

Reduced hippocampal volume and total white matter volume in posttraumatic stress disorder.

Segmentation of brain MR images through a hidden markov random field model and the expectation-maximization algorithm.

Statistical Analysis of Finite Mixture Distributions.

Statistical Pattern Recognition.

Stochastic Image Processing.

The biological zero problem in laser doppler flowmetry.

The partial volume effect in PET/SPECT and Benford’s Law.

Tools for Statistical Inference.

Using Bayesian tissue classification to improve the accuracy of Vestibular Schwannoma volume and growth measurement.

Voxel based analysis of tissue volume from MRI data.

Voxel based analysis of tissue volume MRI data.

The partial volume effect is an imaging artefact associated with tomographic biomedical imaging data. Three-dimensional volumetric data points (voxels) enclose finite sized regions so that they may contain a mixture of signals which are then known as partial volume voxels. The limited spatial resolution of tomographic biomedical imaging data, due to the complex biomedical image acquisition processes, often results in large numbers of these partial volume voxels. Clinical applications of biomedical imaging data often require accurate estimates of tissues or metabolic activity, where many voxels in the data are partial volume voxels. Therefore accurate modelling of the partial volume effect can be very important for such quantitative applications. The probabilistic models discussed and presented in this thesis provide a generic mathematically consistent framework in which the partial volume effect is modelled. Novel developments include an improved model of an intensity and gradient magnitude feature space to model the PV effect; a novel analytically derived formulation of the ground truth (prior) description of the PV effect; a novel gradient controlled spatially regulated classifier that utilises Markov Chain Monte Carlo simulations; and a fully automatic brain isolation technique that identifies brain voxels in neurological MRI data. Simulated partial volume data and data from anatomical (MRI) and functional (PET) biomedical imaging modalities are utilized to assess the classification performance of the partial volume models. The data sets include: an imaged PET/CT phantom provided by the Royal Marsden Hospital, UK; publicly available simulated MR brain data together with the associated ground truths from the Montreal Neurological Institute, McGill University, Canada; and 20 normal MR data sets from the Center for Morphometric Analysis at Massachusetts General Hospital, USA. The performance of the developed classifiers were found to be competitive and in some cases superior to existing published quantitative estimation techniques

Chiverton, John P.

Surrey Research Insight