Design and implementation of a dual-energy x-ray imaging system for organic material detection in an airport security application

Author(s):  
Richard D. R. Macdonald

2021 ◽  
Author(s):  
Muhammad Ghani ◽  
Laurie Fajardo ◽  
Aimin Yan ◽  
Xizeng Wu ◽  
Hong Liu




2021 ◽  
pp. 1-14
Author(s):  
Chia-Hao Chang ◽  
Yu-Ching Ni ◽  
Sheng-Pin Tseng

The study aims to develop a rational polynomial approximation method for improving the accuracy of the effective atomic number calculation with a dual-energy X-ray imaging system. This method is based on a multi-materials calibration model with iterative optimization, which can improve the calculation accuracy of the effective atomic number by adding a rational term without increasing the computation time. The performance of the proposed rational polynomial approximation method is demonstrated and validated by both simulated and experimental studies. The twelve reference materials are used to establish the effective atomic number calibration model, and the value of the effective atomic numbers are between 5.444 and 22. For the accuracy of the effective atomic number calculation, the relative differences between calculated and experimental values are less than 8.5%for all sample cases in this study. The average calculation accuracy of the method proposed in this study can be improved by about 40%compared with the conventional polynomial approximation method. Additionally, experimental quality assurance phantom imaging result indicates that the proposed method is compliant with the international baggage inspection standards for detecting the explosives. Moreover, the experimental imaging results reveal that the difference of color between explosives and the surrounding materials is in significant contrast for the dual-energy image with the proposed method.



2019 ◽  
Vol 46 (2) ◽  
pp. 528-543 ◽  
Author(s):  
Sahar Darvish‐Molla ◽  
Michael C. Reno ◽  
Mike Sattarivand




2021 ◽  
Vol 7 (7) ◽  
pp. 104
Author(s):  
Vladyslav Andriiashen ◽  
Robert van Liere ◽  
Tristan van Leeuwen ◽  
Kees Joost Batenburg

X-ray imaging is a widely used technique for non-destructive inspection of agricultural food products. One application of X-ray imaging is the autonomous, in-line detection of foreign objects in food samples. Examples of such inclusions are bone fragments in meat products, plastic and metal debris in fish, and fruit infestations. This article presents a processing methodology for unsupervised foreign object detection based on dual-energy X-ray absorptiometry (DEXA). A novel thickness correction model is introduced as a pre-processing technique for DEXA data. The aim of the model is to homogenize regions in the image that belong to the food product and to enhance contrast where the foreign object is present. In this way, the segmentation of the foreign object is more robust to noise and lack of contrast. The proposed methodology was applied to a dataset of 488 samples of meat products acquired from a conveyor belt. Approximately 60% of the samples contain foreign objects of different types and sizes, while the rest of the samples are void of foreign objects. The results show that samples without foreign objects are correctly identified in 97% of cases and that the overall accuracy of foreign object detection reaches 95%.



Author(s):  
M. Lundqvist ◽  
B. Cederstrom ◽  
V. Chmill ◽  
M. Danielsson ◽  
B. Hasegawa


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