Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.603263
Title: Computational approaches for the interpretation of ToF-SIMS data
Author: Moore, Jimmy Daniel
ISNI:       0000 0004 5356 0247
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2014
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Abstract:
High surface sensitivity and lateral resolution imaging make Time-of-Flight SecondaryIon Mass Spectrometry (ToF-SIMS) a unique and powerful tool for biologicalanalysis. Many of these biological systems, including drug-cell interactions, requireboth the identification and location of specific chemicals. ToF-SIMS, used in imagingmode, is making great strides towards the goal of single cell and tissue analysis. The experiments, however, result in huge volumes of data. Here advanced computationalapproaches employing sophisticated techniques to convert these data intoknowledge are introduced. This thesis aims to produce a framework for data analysis, integrating novel algorithms,image analysis and 3D visualisation. New schema outlined in this thesisaddress the issues of the immense size of 3D image stacks and the complexity containedwithin the enormous wealth of information in ToF-SIMS data. To deal with the issues of size and complexity of ToF-SIMS data, new techniquesto processing image data are investigated. Automated compression routines for ToF-SIMSimages using a peak picking routine tailored for ToF-SIMS are evaluated. Newuser friendly GUIs capable of processing and visualising very large image stacks areintroduced as part of a tool-kit designed to streamline the process of multivariateanalysis and image processing. Along with this two well known classification routines,namely AdaBoost and SVMs, are also applied to ToF-SIMS data of severalbacterial strains to test their ability to classify SIMS data accurately. This thesispresent several new approaches to data processing and interpretation of ToF-SIMSdata.
Supervisor: Lockyer, Nicholas Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.603263  DOI: Not available
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