KEYWORDS: Principal component analysis, Image processing, Statistical analysis, Liquids, Image classification, Feature extraction, 3D modeling, Education and training
The Transportation Security Laboratory (TSL) conducts thorough assessments of explosives detection systems (EDSs), encompassing a wide range of explosive materials and hazardous substances. When necessary, inert simulants are employed, but they undergo a stringent verification process to accurately replicate specific properties of threat materials. Whether developed by the TSL or commercially acquired, simulants must undergo verification testing to ensure they mirror the desired threat properties. Historically, these assessments relied on rudimentary metrics like average density and effective atomic number, lacking insight into structural properties possibly being exploited by machine learning detection algorithms. Initial research focused on expanding the verification process by incorporating texture metrics extracted from computed tomography (CT) imagery aimed at deriving features that machine learning detection algorithms might also be utilizing. Two avenues of analysis were devised; first, we calculated 22 metrics through statistical analysis of pixel-based grayscale data, and second, we utilized a convolutional neural network (CNN) to classify images. Both of these methods were subsequently refined and are reported in this work. We augmented the number of metrics for the statistical analysis from 22 to 112, and within the CNN framework we harnessed the flattened array originating from the fully connected layer as a feature map. In both processes the analysis transitioned from a 2-dimensional to a 3-dimensional approach. We assessed the effectiveness of both procedures by testing them on imagery of 50 various materials, such as powders, liquids, putties, and emulsions, using Linear Discriminant Analysis (LDA) to evaluate their ability to distinguish between different materials. Finally, Principal Component Analysis (PCA) loadings were used to define 2-dimensional tolerance intervals for comparisons with loadings from other materials as a way to enhance the current simulant quality control process, ultimately improving the robustness of simulants.
As part of its mission, the Transportation Security Laboratory (TSL) of the Department of Homeland Security (DHS) Science and Technology Directorate (S&T) develops methods for characterizing materials that are of interest to transportation security and first responders. For emerging technologies, new metrics that meaningfully differentiate materials must be identified and evaluated. Although X-ray diffraction (XRD) is an established technique for identifying solid crystalline materials, the ability to complement the current generation of X-ray-based threat detection using XRD is still being investigated. The TSL has constructed a high-energy X-ray diffraction system to measure a material’s scattering signature, which can vary based on the presence of organic and inorganic materials, solid crystals, and water or other liquids within a sample. Measurements have been performed on a wide range of household items as well as explosives and other threats. The scattering intensity as a function of momentum transfer was examined for each material to identify several potential metrics for distinguishing threats from inert substances.
Synthetic data are commonly used to train machine learning models in domains where real data are sparse. In this work, we describe a method to generate synthetic x-ray imaging data by inserting objects into a dual-energy computed tomography scan while simultaneously inserting the beam-hardening and noise artifacts that corrupt real data. This type of data augmentation is useful for training classifiers, for example, by artificially increasing the prevalence of objects of interest in a dataset. This work extends existing 3D Threat Image Projection methods by using dual-energy decomposition to model the energy-dependence of attenuation values in the sinogram data. By summing linear attenuation coefficient functions, objects can be inserted directly into a sinogram while accounting for beam-hardening in the insertion region. In addition, we introduce a calibration method to model the change in noise levels resulting from the insertion of attenuating objects. The performance of the method is demonstrated on a simple phantom scanned with a benchtop microCT system.
The Transportation Security Laboratory (TSL) performs testing of explosives detection systems using explosives and other hazardous materials. Inert simulants are also used as substitutes in potentially dangerous testing situations or at testing locations where explosives are prohibited. Each simulant must first be verified that it accurately represents the material on the specific detection platform it was designed for. In addition to the simulant-threat matching, lot-to-lot quality control testing is performed for simulants and threats to ensure that their physical properties remain consistent. Historically, x-ray verification has been limited to using features such as electron density and effective atomic number. While efficient, these features are limited in their application, as they do not provide information related to the material’s structural properties. In this study, four classification methods were tested using imagery-derived texture features to characterize materials and distinguish them from one another. The first three approaches (k-nearest neighbors, support-vector machine, and artificial neural network) were tested using 22 first- and second-order texture features derived from computed tomography images. The fourth method (convolutional neural network) used internally derived features. Based on the test results, a determination was made that the CNN and k-NN were the best algorithms to use to characterize materials based on their texture features.
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