X-RAY absorption of a material strongly depends on the atomic weight of its components. „Light“ materials like organic tissue or aluminum can absorb „soft“ x-rays, as these used in medical examinations, but are translucent for the „hard“ x-rays used for luggage examination. „Hard“ x-rays are only absorbed by objects made of iron-type metals or high density materials, e.g., glass or ceramics. Very heavy materials, as lead, can block even the „hard“ x-rays used for scanning of luggage.
Modern X-ray scanners are using the „dual energy X-ray absorptiometry“ (DEXA) technology. Here, basically two images of the objects are taken: a high energy x-ray image (approx. 140 keV) and a low energy x-ray image (approx. 70 keV).
By overlay of these images, and some extensive mathematical processing, the material composition of the contents of the scanned object can be calculated and displayed in a false-color image.
Side view of bag, with X-RAY TAGs in the center. Shoes, glasses and electronics in lower part.
Now, we can see high-density organic materials, as shoes or cheese in shades of orange, (iron) metal and glass/ceramics objects in shades of blue and mixed materials in green.
Objects consisting of materials of very high atomic weight, e.g., lead, gold or uranium, are displayed in black.
X-RAY TAGs are also displayed in black, as they contain a non-toxic, non-radioactive heavy metal.
The false-color images allow inspectors to rapidly identify dangerous items made from metals, explosives or electronic devices, all in one picture.
Machine learning/AI enhanced image analysis system can improve the process even further.
You can learn more about the math and science behind the false-color images in these articles:
Krzysztof Dmitruk, Michal Mazur, Marcin Denkowski, Pawel Mikolajczak
Method for filling and sharpening false colour layers of dual energy X-ray images.
IFAC-PapersOnLine 48, 342-347 (2015)
https://doi.org/10.1016/j.ifacol.2015.07.058.
Dmitruk, K., Denkowski, M., Mazur, M. et al.
Sharpening filter for false color imaging of dual-energy X-ray scans.
SIViP 11, 613–620 (2017).
https://doi.org/10.1007/s11760-016-1001-7
Benedykciuk, E., Denkowski, M. & Dmitruk, K.
Material classification in X-ray images based on multi-scale CNN.
SIViP 15, 1285–1293 (2021). https://doi.org/10.1007/s11760-021-01859-9
A list of datasets and papers in X-ray security images (Computer vision/Machine Learning), https://github.com/NeelBhowmik/xray
If you’d like to test your abilities as luggage inspector, you may in the video game „Airport X-ray Simulator“: https://store.steampowered.com/app/3079970/Airport_XRay_Simulator/