Single Energy X-ray Image Colorization Using Convolutional Neural Network for Material Discrimination

Abstract

Colorization in X-ray material discrimination is considered one of the main phases in X-ray baggage inspection systems for detecting contraband and hazardous materials by displaying different materials with specific colors. The substructure of material discrimination identifies materials based on their atomic number. However, the images are checked and assigned by a human factor, which may decelerate the verification process. Therefore, researchers used computer vision and machine learning methods to expedite the examination process and ascertain the precise identification of materials and elements. This study proposes a color-based material discrimination method for single-energy X-ray images based on the dual-energy colorization. We use a convolutional neural network to discriminate materials into several classes, such as organic, non-organic substances, and metals. It highlights the details of the objects, including occluded objects, compared to commonly used segmentation methods, which do not show the details of the objects. We trained and tested our model on three popular X-ray datasets, which are Korean datasets comprising three kinds of scanners: (Rapiscan, Smith, Astrophysics), SIXray, and COMPASS-XP. The results showed that the proposed method achieved high performance in X-ray colorization in terms of peak-signal-to-noise ratio (PSNR), structural similarity index (SSIM), and learned perceptual image patch similarity (LPIPS). We applied the trained models to the single-energy X-ray images and we compared the results obtained from each model.