One of the largest challenges within modern medicine is the increase in global cancer rates especially in western countries, which is often attributed to ageing populations, dietary and lifestyle changes. One of the fastest growing cancers within the western world is oesophageal cancer, which without reliable early diagnosis is often fatal due to the cancer spreading. It is therefore crucial for the development of reliable methods and tools for early cancer detection. This is especially important for those who are most at risk, which includes Barrett's oesophagus patients. Infrared (IR) spectroscopy techniques have been proven capable of gaining large amounts of information on the chemical composition of biological samples. This thesis therefore focuses on using a variety of IR spectroscopy techniques, including Fourier transform infrared spectroscopy (FTIR) and scanning near-field optical microscopy (SNOM) to image oesophageal samples, tissue biopsies and cell line samples. The thesis demonstrates how machine learning algorithms can be used in conjunction with FTIR to provide a quick, non-biased tissue diagnostic method, free from the issues associated with current histology techniques. As well as focusing on the processing of FTIR data, the thesis will assess the ability of an aperture SNOM to image biological samples as it is able to achieve diffraction limit breaking spatial resolutions and has to potential to give previously impossible insights.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:755733 |
| Date | January 2018 |
| Creators | Ingham, James |
| Contributors | Barrett, Steve ; Martin, David |
| Publisher | University of Liverpool |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://livrepository.liverpool.ac.uk/3022787/ |
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