Classification-aware dimensionality reduction methods for explosives detection using multi-energy x-ray computed tomography

Limor Eger; Prakash Ishwar; W. Clem Karl; Homer Pien

doi:10.1117/12.888064

7 February 2011 Classification-aware dimensionality reduction methods for explosives detection using multi-energy x-ray computed tomography

Limor Eger, Prakash Ishwar, W. Clem Karl, Homer Pien

Author Affiliations +

Proceedings Volume 7873, Computational Imaging IX; 78730Q (2011) https://doi.org/10.1117/12.888064
Event: IS&T/SPIE Electronic Imaging, 2011, San Francisco Airport, California, United States

Abstract

Multi-Energy X-ray Computed Tomography (MECT) is a non-destructive scanning technology in which multiple energyselective measurements of the X-ray attenuation can be obtained. This provides more information about the chemical composition of the scanned materials than single-energy technologies and potential for more reliable detection of explosives. We study the problem of discriminating between explosives and non-explosives using low-dimensional features extracted from the high-dimensional attenuation versus energy curves of materials. We study various linear dimensionality reduction methods and demonstrate that the detection performance can be improved by using more than two features and when using features different than the standard photoelectric and Compton coefficients. This suggests the potential for improved detection performance relative to conventional dual-energy X-ray systems.

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Limor Eger, Prakash Ishwar, W. Clem Karl, and Homer Pien "Classification-aware dimensionality reduction methods for explosives detection using multi-energy x-ray computed tomography", Proc. SPIE 7873, Computational Imaging IX, 78730Q (7 February 2011); https://doi.org/10.1117/12.888064

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