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.

Progressive sequential imaging on x-ray baggage inspection systems

“…The study "Single Energy X-ray Image Colorization Using Convolutional Neural Network for Material Discrimination" suggest a different method for coloring single-energy X-Ray images. 6 In this method, Convolutional Neural Network is used to identify the materials. After CNN identifies the item, it colors image according to the category of the item.…”

“…0 ≤ p ≤ 1 0 ≤ f ≤ 1, for thin material 1<f ≤ 90, for thick material (6) The whole frames can be obtained by using all f and p values, and they are combined. In this study, p is changed between 0.3 and 0.8, and the increment of p is 0.1. f is changed between 0.4 and 1, with the increment of p being 0.02.…”

Enhancing X-ray Security Image Synthesis: Advanced Generative Models and Innovative Data Augmentation Techniques

“…Innovative frameworks have been introduced to distinguish COVID-19 from other pneumonia types through X-ray analysis [8]. Strategies to enhance deep learning model detection accuracy and address complex challenges in training and testing include modifying activation functions in deep CNNs, employing transfer learning [9,10], utilising image inpainting [11,12], and applying models to tasks such as cancer diagnosis, detection [13], and classification, material discrimination [14], medical question-answering [15,16], and software engineering applications like optimizing project schedules, customer segmentation [17,18], and IoT intrusion detection [19,20]. Unique approaches, such as creatively combining activation functions and optimization systems, contribute to the advancement of deep learning models.…”