Title
Optimizing multi-dimensional terahertz imaging analysis for colon cancer diagnosis
Abstract
Terahertz reflection imaging (at frequencies ~0.1-10THz/10^1^2Hz) 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.
Year
DOI
Venue
2013
10.1016/j.eswa.2012.10.019
Expert Syst. Appl.
Keywords
Field
DocType
data analysis,colon cancer diagnosis,ex vivo colon cancer,relevant imaging parameter,terahertz reflection imaging,optimum imaging parameter,medical imaging technique,colon cancer sample,abnormal sample,terahertz data,multi-dimensional terahertz imaging analysis,various intelligent analysis method,optimization,support vector machines,terahertz,colon cancer,decision tree,neural networks
Decision tree,Data mining,Multi dimensional,Medical imaging,Computer science,Support vector machine,Terahertz radiation,Artificial neural network,Medical diagnosis
Journal
Volume
Issue
ISSN
40
6
0957-4174
Citations 
PageRank 
References 
4
0.52
0
Authors
4
Name
Order
Citations
PageRank
Leila Eadie183.03
Caroline B. Reid240.52
Anthony J. Fitzgerald340.86
Vincent P. Wallace461.29