A curve-based material recognition method in MeV dual-energy X-ray imaging system

Abstract

High energy dual-energy X-ray Digital Radiography(DR) imaging is mainly used in material recognition of the cargo inspection. We introduce the development history and the principle of the technology and describe the data process flow of our system. The system corrects original data to get the dual-energy transparence image. Material categories of all points in the image are identified by the classification curve which is related to the X-ray energy spectrum. For the calibration of classification curve, our strategy involves a basic curve calibration and a real-time correction devoted to enhance the classification accuracy. Image segmentation and denoising methods are applied to smooth the image. The image contains more information after colorization. Some results show that our methods achieve the desired effect.

On the Evaluation of Semi-Supervised 2D Segmentation for Volumetric 3D Computed Tomography Baggage Security Screening

“…There exist some limited work on material classification based on X-ray imagery. For example, Chen et al [17] proposed a curve-based material recognition method by theoretic analysis of X-ray imaging processing using high-energy dual energy X-ray (6/3 MeV). Specifically, they consider the ratio of two X-ray energies after penetrating materials, resulting in a standard curve for a specific material which can be used to discriminate the material from others.…”

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

“…The X-ray operation system aims to detect the object under inspection and identify the different objects in the luggage or cargo to prevent any prohibited or illegal items from entering countries. Several researchers have studied X-ray operation systems for classification [1][2][3][4][5][6][7][8][9][10][11][12][13], object detection [14][15][16][17][18][19][20], and segmentation [21][22][23][24][25][26][27][28]. The most important aspect of this procedure is to recognize the objects or materials inside the luggage or container.…”

“…Bunrit et al [7] proposed a CNN transfer learning for GoogleNet [8] and attained a high accuracy of 95% on the X-ray material classification. Chen et al [9] proposed a curve calibration and a realtime correction technique by using a curved-based HSL color space image colorization and smoothing strategy to improve the classification performance. Andrews et al [10] presented an image classification method using a feed-forward neural network-based auto-encoder for threat and anomalous detection in cargo X-ray images based on one-class radial basis function (RBF) and support vector machines (SVM).…”