Presentation + Paper
14 June 2023 Classifiers for material discrimination with dual modality x-ray transmission and coherent x-ray diffraction system
R. Thamvichai, Dan Pike, J. Greenberg, A. Ashok
Author Affiliations +
Abstract
X-ray coherent diffraction-based measurements can provide more specific material information that is complementary to transmission-based material information. With increasing capability of the X-ray coherent diffraction-based systems and recent development of dual modality of X-ray transmission-based and diffraction-based systems, there is a significant potential for improving the overall system threat detection performance for material discrimination. Dual modality systems can yield higher detection probability (Pd) while lowering the probability of false alarm (Pfa), relative to the transmission modality. In this work, we analyze the material discrimination performance for two different machine learning classifiers: support vector machines (SVM) and neural networks (NN), using both simulation and experimental data obtained with a dual-modality X-ray system. Using simulation studies, we demonstrate significant improvement in material discrimination performance afforded by additional complementary information by coherent diffraction for a variety of materials. We further validate these improvements using an experimental dataset collected using real-world objects and materials.
Conference Presentation
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
R. Thamvichai, Dan Pike, J. Greenberg, and A. Ashok "Classifiers for material discrimination with dual modality x-ray transmission and coherent x-ray diffraction system", Proc. SPIE 12531, Anomaly Detection and Imaging with X-Rays (ADIX) VIII, 1253104 (14 June 2023); https://doi.org/10.1117/12.2664196
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KEYWORDS
X-ray diffraction

X-rays

Classification systems

Data modeling

Diffraction

Image segmentation

Palladium

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