Title
Classification-Aware Dimensionality Reduction Methods For Explosives Detection Using Multi-Energy X-Ray Computed Tomography
Abstract
Multi-Energy X-ray Computed Tomography (MECT) is a non-destructive scanning technology in which multiple energy-selective 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.
Year
DOI
Venue
2011
10.1117/12.888064
COMPUTATIONAL IMAGING IX
Keywords
Field
DocType
multi-energy X-ray computed tomography, classification, dimensionality reduction, singular value decomposition (SVD), Fisher linear discriminant analysis (LDA), receiver operating characteristic (ROC)
Photoelectric effect,Explosive detection,Dimensionality reduction,X-ray,Explosive material,Optics,Computed tomography,Attenuation,Physics
Conference
Volume
ISSN
Citations 
7873
0277-786X
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Limor Eger110.96
Prakash Ishwar28511.54
W. Clem Karl322435.45
Homer Pien4446.48