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Proceedings Paper

Layer separation for material discrimination cargo imaging system
Author(s): Kenneth Fu; Dale Ranta; Pankaj Das; Clark Guest
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Paper Abstract

We propose an approach to boost the accuracy of the performance of a high-energy x-ray material discrimination imaging system. The theory of using two energies of x-rays to scan objects to extract the atomic information has been well developed. Such an approach is known as dual-energy imaging. At the beginning of this century, mega-volt-level dual-energy systems began to be applied to extract information regarding the materials inside a cargo container. For a system that scans at two x-ray energies, the ratio between the attenuations of the two energies will be different for different materials. Using this property, we can classify the content of a cargo container from the attenuation ratio image. However, thick shielding can reduce the signal-to-noise ratio such that correct material identification with low false alarm rate is unfeasible without further image processing. We have developed a method for high atomic number discrimination that can more accurately identify a region of high atomic number. The pixels of each object are clustered using our proposed clustering approach. The thickness and ratio of high- and low-energy attenuations of each object can then be more correctly calculated by separating it from its background. Our method can significantly improve the accuracy by suppressing false alarms and increasing the detection rate.

Paper Details

Date Published: 29 January 2010
PDF: 12 pages
Proc. SPIE 7538, Image Processing: Machine Vision Applications III, 75380Y (29 January 2010); doi: 10.1117/12.838646
Show Author Affiliations
Kenneth Fu, Univ. of California, San Diego (United States)
Dale Ranta, SAIC (United States)
Pankaj Das, Univ. of California, San Diego (United States)
Clark Guest, Univ. of California, San Diego (United States)


Published in SPIE Proceedings Vol. 7538:
Image Processing: Machine Vision Applications III
David Fofi; Kurt S. Niel, Editor(s)

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