eprintid: 16359
rev_number: 28
eprint_status: archive
userid: 602
dir: disk0/00/01/63/59
datestamp: 2009-07-17 11:02:48
lastmod: 2015-07-23 09:37:10
status_changed: 2009-07-17 11:02:48
type: thesis
metadata_visibility: show
item_issues_count: 0
creators_name: Zhu, Y.
creators_id: YZHUX91
title: Statistical aspects of elastic scattering spectroscopy with applications to cancer diagnosis
ispublished: unpub
subjects: 11300
divisions: F61
abstract: Elastic scattering spectroscopy (ESS), is a non-invasive and real-time in vivo
optical diagnosis technique sensitive to changes in the physical properties of human
tissue, and thus able to detect early cancer and precancerous changes. This thesis
focuses on the statistical issue on how to eliminate irrelevant variations in the high-dimensional
ESS spectra and extract the most useful information to enable the
classification of tissue as normal or abnormal.
Multivariate statistical methods have been used to tackle the problems, among
which principal component discriminant analysis and partial least squares
discriminant analysis are the most explored throughout the thesis as general tools for
supervised dimension reduction and classification. Customized multivariate methods
are proposed in the specific context of ESS.
When ESS spectra are measured in vivo by a hand-held optical probe, differences
in the angle and pressure of the probe are a major source of variability between the
spectra from replicate measurements. A customized spectral pre-treatment called error
removal by orthogonal subtraction (EROS) is designed to ameliorate the effect of this
variability. This pre-treatment reduces the complexity and increases both the accuracy
and interpretability of the subsequent classification models when applied to early
detection of cancer risk in Barrett’s oesophagus.
For the application of ESS to diagnosis of sentinel lymph node metastases in
breast cancer, an automated ESS scanner was developed to take measurements from a
larger area of tissue to produce ESS images for cancer diagnosis. Problems arise due
to the existence of background area in the image with considerable between-node
variation and no training data available. A partially supervised Bayesian multivariate
finite mixture classification model with a Markov random field spatial prior in a
reduced dimensional space is proposed to recognise the background area
automatically at the same time as distinguishing normal from metastatic tissue.
date: 2009-06-28
vfaculties: VMPS
oa_status: green
thesis_class: doctoral_open
language: eng
thesis_view: UCL_Thesis
dart: DART-Europe
primo: open
primo_central: open_green
lyricists_name: Zhu, Y
lyricists_id: YZHUX91
full_text_status: public
pages: 219
institution: UCL (University College London)
department: Department of Statistical Science
thesis_type: Doctoral
citation:        Zhu, Y.;      (2009)    Statistical aspects of elastic scattering spectroscopy with applications to cancer diagnosis.                   Doctoral thesis , UCL (University College London).     Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/16359/1/16359.pdf