Classification of terahertz-pulsed imaging data from excised breast tissue

Fitzgerald, Anthony J.; Pinder, Sarah; Purushotham, Anand D; O’Kelly, Padraig; Ashworth, Philip C.; Wallace, Vincent P.

doi:doi:10.1117/1.JBO.17.1.016005

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Journal of Biomedical Optics / Volume 17 / Issue 1 / Research Papers: Imaging

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Classification of terahertz-pulsed imaging data from excised breast tissue

J. Biomed. Opt. 17, 016005 (Feb 06, 2012); http://dx.doi.org/10.1117/1.JBO.17.1.016005

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Abstract

References (49)

Anthony J. Fitzgerald and Vincent P. Wallace

University of Western Australia, School of Physics, Crawley 6009, Australia

Sarah Pinder and Anand D Purushotham

King’s College London, Section of Research Oncology, Guy’s Hospital, London SE1 9RT, United Kingdom

Padraig O’Kelly and Philip C. Ashworth

TeraView Ltd., Platinum Building, John’s Innovation Park, Cowley Road, Cambridge, CB4 0WS, United Kingdom

University of Cambridge, Semiconductor Physics Group, Cavendish Laboratory, Cambridge, CB3 0HE, United Kingdom

We investigate the efficacy of using data reduction techniques to aid classification of terahertz (THz) pulse data obtained from tumor and normal breast tissue. Fifty-one samples were studied from patients undergoing breast surgery at Addenbrooke’s Hospital in Cambridge and Guy’s Hospital in London. Three methods of data reduction were used: ten heuristic parameters, principal components of the pulses, and principal components of the ten parameter space. Classification was performed using the support vector machine approach with a radial basis function. The best classification accuracy, when using all ten components, came from using the principal components on the pulses and principal components on the parameter, with an accuracy of 92%. When less than ten components were used, the principal components on the parameter space outperformed the other methods. As a visual demonstration of the classification technique, we apply the data reduction/classification to several example images and demonstrate that, aside from some interpatient variability and edge effects, the algorithm gives good classification on terahertz data from breast tissue. The results indicate that under controlled conditions data reduction and SVM classification can be used with good accuracy to classify tumor and normal breast tissue.

History

Received Sep 12, 2011
Accepted Nov 09, 2011
Revised Nov 05, 2011
Published online Feb 06, 2012

Digital Object Identifier

http://dx.doi.org/10.1117/1.JBO.17.1.016005

Citation

Anthony J. Fitzgerald, Sarah Pinder, Anand D Purushotham, Padraig O’Kelly, Philip C. Ashworth and Vincent P. Wallace, "Classification of terahertz-pulsed imaging data from excised breast tissue", J. Biomed. Opt. 17, 016005 (Feb 06, 2012); http://dx.doi.org/10.1117/1.JBO.17.1.016005