Classifiers for material discrimination with dual modality X-ray transmission and coherent X-ray diffraction system

R. Thamvichai, Dan Pike, J. Greenberg, A. Ashok

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationAnomaly Detection and Imaging with X-Rays (ADIX) VIII
EditorsAmit Ashok, Joel A. Greenberg, Michael E. Gehm
ISBN (Electronic)9781510661769
StatePublished - 2023
EventAnomaly Detection and Imaging with X-Rays (ADIX) VIII 2023 - Orlando, United States
Duration: May 3 2023May 4 2023

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceAnomaly Detection and Imaging with X-Rays (ADIX) VIII 2023
Country/TerritoryUnited States


  • Classifications
  • Experiment
  • Machine learning
  • Simulation
  • X-ray diffraction
  • X-ray transmission

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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