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Consensus relaxation on materials of interest for adaptive ATR in CT images of baggage
Author(s): David W. Paglieroni; Hema Chandrasekaran; Christian Pechard; Harry E. Martz
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Paper Abstract

An adaptive automatic threat recognition system (AATR) developed at the Lawrence Livermore National Laboratory (LLNL) is described for x-ray CT images of baggage. The AATR automatically adapts to the input object requirement specification (ORS), which can change or evolve over time. These specifications characterize materials of interest (MOIs), basic physical features of interest (FOIs) (such a mass and thickness) and performance goals (detection and false alarm probability) for objects of interest (OOIs). The need and technical requirements for an AATR were developed in collaboration with DHS’s Explosives Division and Northeastern University’s Awareness and Localization of Explosives-Related Threats (ALERT) Center, a DHS Center of Excellence ( Independent of the input ORS, LLNL’s AATR always uses the same algorithm and codes to process CT images. The algorithm adapts in real-time to changes in the input ORS. LLNL’s AATR is thus suitable for dynamic scenarios in which the nature of the OOIs can change rapidly. The AATR uses a spatial consensus relaxation method to determine the most likely material composition for each CT image voxel. The resulting image of most likely material compositions is segmented. An OOI classification statistic (OOI score) is computed for each voxel and each extracted image volume. OOI recognition performance is reported using various metrics on a test set of ~180 plastic bins supplied by the ALERT Center of Excellence. A method is then proposed for automatic decision threshold estimation that can adapt to the detection performance goal, the most likely material composition, and the contents of the baggage.

Paper Details

Date Published: 27 April 2018
PDF: 15 pages
Proc. SPIE 10632, Anomaly Detection and Imaging with X-Rays (ADIX) III, 106320E (27 April 2018); doi: 10.1117/12.2309839
Show Author Affiliations
David W. Paglieroni, Lawrence Livermore National Lab. (United States)
Hema Chandrasekaran, Lawrence Livermore National Lab. (United States)
Christian Pechard, Lawrence Livermore National Lab. (United States)
Harry E. Martz, Lawrence Livermore National Lab. (United States)

Published in SPIE Proceedings Vol. 10632:
Anomaly Detection and Imaging with X-Rays (ADIX) III
Amit Ashok; Joel A. Greenberg; Michael E. Gehm; Mark A. Neifeld, Editor(s)

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