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Optimizing multi-dimensional terahertz imaging analysis for colon cancer diagnosis

Eadie, LH; Reid, CB; Fitzgerald, AJ; Wallace, VP; (2013) Optimizing multi-dimensional terahertz imaging analysis for colon cancer diagnosis. Expert Systems with Applications , 40 (6) 2043 - 2050. 10.1016/j.eswa.2012.10.019. Green open access

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Abstract

Terahertz reflection imaging (at frequencies ∼0.1–10 THz/1012 Hz) is non-ionizing and has potential as a medical imaging technique; however, there is currently no consensus on the optimum imaging parameters to use and the procedure for data analysis. This may be holding back the progress of the technique. This article describes the use of various intelligent analysis methods to choose relevant imaging parameters and optimize the processing of terahertz data in the diagnosis of ex vivo colon cancer samples. Decision trees were used to find important parameters, and neural networks and support vector machines were used to classify the terahertz data as indicating normal or abnormal samples. This work reanalyzes the data described in Reid et al. (2011) (Physics in Medicine and Biology, 56, 4333–4353), and improves on their reported diagnostic accuracy, finding sensitivities of 90–100% and specificities of 86–90%. This optimization of the analysis of terahertz data allows certain recommendations to be suggested concerning terahertz reflection imaging of colon cancer samples.

Type: Article
Title: Optimizing multi-dimensional terahertz imaging analysis for colon cancer diagnosis
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.eswa.2012.10.019
Publisher version: http://dx.doi.org/10.1016/j.eswa.2012.10.019
Language: English
Additional information: This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Keywords: Terahertz, Optimization, Neural networks, Support vector machines, Decision tree, Colon cancer
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/1399217
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