Performance estimation for threat detection in CT systems

Trent Montgomery; W. Clem Karl; David A. Castañón

doi:10.1117/12.2276654

1 May 2017 Performance estimation for threat detection in CT systems

Trent Montgomery, W. Clem Karl, David A. Castañón

Proceedings Volume 10187, Anomaly Detection and Imaging with X-Rays (ADIX) II; 101870M (2017) https://doi.org/10.1117/12.2276654
Event: SPIE Defense + Security, 2017, Anaheim, CA, United States

Abstract

Detecting the presence of hazardous materials in suitcases and carry-on luggage is an important problem in aviation security. As the set of threats is expanding, there is a corresponding need to increase the capabilities of explosive detection systems to address these threats. However, there is a lack of principled tools for predicting the performance of alternative designs for detection systems. In this paper, we describe an approach for computing bounds on the achievable classification performance of material discrimination systems based on empirical statistics that estimate the f-divergence of the underlying features. Our approach can be used to examine alternative physical observation modalities and measurement configurations, as well as variations in reconstruction and feature extraction algorithms.

Citation Download Citation

Trent Montgomery, W. Clem Karl, and David A. Castañón "Performance estimation for threat detection in CT systems", Proc. SPIE 10187, Anomaly Detection and Imaging with X-Rays (ADIX) II, 101870M (1 May 2017); https://doi.org/10.1117/12.2276654

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available