Robust material classification on dual-energy x-ray imaging devices

Cevahir Çığla; Büşra Küçükateş; Ozan Yalçın; Duygu Selin Ak; Şükrücan Taylan Işıkoğlu

doi:10.1117/12.2618486

3 June 2022 Robust material classification on dual-energy x-ray imaging devices

Cevahir Çığla, Büşra Küçükateş, Ozan Yalçın, Duygu Selin Ak, Şükrücan Taylan Işıkoğlu

Proceedings Volume 12104, Anomaly Detection and Imaging with X-Rays (ADIX) VII; 1210404 (2022) https://doi.org/10.1117/12.2618486
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

In this study, an efficient end-to-end material classification is proposed for dual energy x-ray imaging devices. Performing prompt geometric and radiometric calibrations, we exploit polynomial modeling on low-high energy ratios to estimate effective atomic numbers (EAN) of the objects, that is based and experimented over twentyfive different materials. Special attention is devoted for dense materials on which the ratio polynomial modeling performs poorly as the thickness increases. A novel material peeling approach is also proposed that uncovers blocked or encapsulated objects and enable precise EAN estimation in cluttered images. The proposed approach provides visually informative x-ray image segmentation.

Conference Presentation

Citation Download Citation

Cevahir Çığla, Büşra Küçükateş, Ozan Yalçın, Duygu Selin Ak, and Şükrücan Taylan Işıkoğlu "Robust material classification on dual-energy x-ray imaging devices", Proc. SPIE 12104, Anomaly Detection and Imaging with X-Rays (ADIX) VII, 1210404 (3 June 2022); https://doi.org/10.1117/12.2618486

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