eprintid: 10099273
rev_number: 8
eprint_status: archive
userid: 695
dir: disk0/10/09/92/73
datestamp: 2020-06-01 19:45:38
lastmod: 2020-06-01 19:45:38
status_changed: 2020-06-01 19:45:38
type: thesis
metadata_visibility: show
creators_name: Kwee, Ivo W
title: Towards a Bayesian Framework for Optical Tomography
ispublished: unpub
keywords: Applied sciences; Medical physics; Optical Tomography
note: Thesis digitised by ProQuest.
abstract: Maximum likelihood (ML) is a well established method for general parameter estimation. However in its original formulation, ML applied to the image reconstruction problem in Optical Tomography has two serious inadequacies. One is that ML is incapable of distinguishing noise in the data, leading to spurious artifact in the image. The other drawback is that ML does not provide a way to include any prior information about the object that might be available. Noise regularisation is a major concern in imaging and ill-posed inverse problems in general. The aim of this research is to improve the existing imaging algorithm for Optical Tomography. In this thesis we have taken two approaches to the problem. In the first approach we introduce a full maximum likelihood (FML) method which estimates the noise level concurrently. We show that FML in combination with a proposed method of hold-out validation is able to determine a nearly optimal estimate without overfitting to the noise in the data. In the second approach, we will propose a Bayesian method that uses the so-called normal- Wishart density as a parametric prior. We will show that for low degrees of freedom this choice of prior has robust imaging properties and that in some cases the prior can even increase the image resolution (compared to the ML image) but still retain good suppression of noise. We show how graphical modelling can assist in building complex probabilistic models and give examples for the implementation using a developed C++ library. Throughout the thesis, methods are validated by reconstruction examples using simulated data. In the final chapter, we will also present images from real experimental data.
date: 2000
oa_status: green
full_text_type: other
thesis_class: doctoral_open
thesis_award: Ph.D
language: eng
thesis_view: UCL_Thesis
primo: open
primo_central: open_green
verified: verified_manual
full_text_status: public
pages: 272
institution: UCL (University College London)
department: Department of Medical Physics and Bioengineering
thesis_type: Doctoral
citation:        Kwee, Ivo W;      (2000)    Towards a Bayesian Framework for Optical Tomography.                   Doctoral thesis  (Ph.D), UCL (University College London).     Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10099273/1/U642583.pdf