Tackling the X-ray cargo inspection challenge using machine learning

The current infrastructure for non-intrusive inspection of cargo containers cannot accommodate exploding com-merce volumes and increasingly stringent regulations. There is a pressing need to develop methods to automate parts of the inspection workflow, enabling expert operators to focus on a manageable number of high-risk images. To tackle this challenge, we developed a modular framework for automated X-ray cargo image inspection. Employing state-of-the-art machine learning approaches, including deep learning, we demonstrate high performance for empty container verification and specific threat detection. This work constitutes a significant step towards the partial automation of X-ray cargo image inspection

Measuring and correcting wobble in large-scale transmission radiography

BACKGROUND: Large-scale transmission radiography scanners are used to image vehicles and cargo containers. Acquired images are inspected for threats by a human operator or a computer algorithm. To make accurate detections, it is important that image values are precise. However, due to the scale (∼5 m tall) of such systems, they can be mechanically unstable, causing the imaging array to wobble during a scan. This leads to an effective loss of precision in the captured image. OBJECTIVE: We consider the measurement of wobble and amelioration of the consequent loss of image precision. METHODS: Following our previous work, we use Beam Position Detectors (BPDs) to measure the cross-sectional profile of the X-ray beam, allowing for estimation, and thus correction, of wobble. We propose: (i) a model of image formation with a wobbling detector array; (ii) a method of wobble correction derived from this model; (iii) methods for calibrating sensor sensitivities and relative offsets; (iv) a Random Regression Forest based method for instantaneous estimation of detector wobble; and (v) using these estimates to apply corrections to captured images of difficult scenes. RESULTS: We show that these methods are able to correct for 87% of image error due wobble, and when applied to difficult images, a significant visible improvement in the intensity-windowed image quality is observed. CONCLUSIONS: The method improves the precision of wobble affected images, which should help improve detection of threats and the identification of different materials in the image

Detection of concealed cars in complex cargo X-ray imagery using Deep Learning

BACKGROUND: Non-intrusive inspection systems based on X-ray radiography techniques are routinely used at transport hubs to ensure the conformity of cargo content with the supplied shipping manifest. As trade volumes increase and regulations become more stringent, manual inspection by trained operators is less and less viable due to low throughput. Machine vision techniques can assist operators in their task by automating parts of the inspection workflow. Since cars are routinely involved in trafficking, export fraud, and tax evasion schemes, they represent an attractive target for automated detection and flagging for subsequent inspection by operators. OBJECTIVE: Development and evaluation of a novel method for the automated detection of cars in complex X-ray cargo imagery. METHODS: X-ray cargo images from a stream-of-commerce dataset were classified using a window-based scheme. The limited number of car images was addressed by using an oversampling scheme. Different Convolutional Neural Network (CNN) architectures were compared with well-established bag of words approaches. In addition, robustness to concealment was evaluated by projection of objects into car images. RESULTS: CNN approaches outperformed all other methods evaluated, achieving 100% car image classification rate for a false positive rate of 1-in-454. Cars that were partially or completely obscured by other goods, a modus operandi frequently adopted by criminals, were correctly detected. CONCLUSIONS: We believe that this level of performance suggests that the method is suitable for deployment in the field. It is expected that the generic object detection workflow described can be extended to other object classes given the availability of suitable training data

Automated X-ray image analysis for cargo security: Critical review and future promise

We review the relatively immature field of automated image analysis for X-ray cargo imagery. There is increasing demand for automated analysis methods that can assist in the inspection and selection of containers, due to the ever-growing volumes of traded cargo and the increasing concerns that customs- and security-related threats are being smuggled across borders by organised crime and terrorist networks. We split the field into the classical pipeline of image preprocessing and image understanding. Preprocessing includes: image manipulation; quality improvement; Threat Image Projection (TIP); and material discrimination and segmentation. Image understanding includes: Automated Threat Detection (ATD); and Automated Contents Verification (ACV). We identify several gaps in the literature that need to be addressed and propose ideas for future research. Where the current literature is sparse we borrow from the single-view, multi-view, and CT X-ray baggage domains, which have some characteristics in common with X-ray cargo

Threat Image Projection (TIP) into X-ray images of cargo containers for training humans and machines

We propose a framework for Threat Image Projection (TIP) in cargo transmission X-ray imagery. The method exploits the approximately multiplicative nature of X-ray imagery to extract a library of threat items. These items can then be projected into real cargo. We show using experimental data that there is no significant qualitative or quantitative difference between real threat images and TIP images. We also describe methods for adding realistic variation to TIP images in order to robustify Machine Learning (ML) based algorithms trained on TIP. These variations are derived from cargo X-ray image formation, and include: (i) translations; (ii) magnification; (iii) rotations; (iv) noise; (v) illumination; (vi) volume and density; and (vii) obscuration. These methods are particularly relevant for representation learning, since it allows the system to learn features that are invariant to these variations. The framework also allows efficient addition of new or emerging threats to a detection system, which is important if time is critical. We have applied the framework to training ML-based cargo algorithms for (i) detection of loads (empty verification), (ii) detection of concealed cars (ii) detection of Small Metallic Threats (SMTs). TIP also enables algorithm testing under controlled conditions, allowing one to gain a deeper understanding of performance. Whilst we have focused on robustifying ML-based threat detectors, our TIP method can also be used to train and robustify human threat detectors as is done in cabin baggage screening

Automated detection of smuggled high-risk security threats using Deep Learning

The security infrastructure is ill-equipped to detect and deter the smuggling of non-explosive devices that enable terror attacks such as those recently perpetrated in western Europe. The detection of so-called "Small Metallic Threats" (SMTs) in cargo containers currently relies on statistical risk analysis, intelligence reports, and visual inspection of X-ray images by security officers. The latter is very slow and unreliable due to the difficulty of the task: objects potentially spanning less than 50 pixels have to be detected in images containing more than 2 million pixels against very complex and cluttered backgrounds. In this contribution, we demonstrate for the first time the use of Convolutional Neural Networks (CNNs), a type of Deep Learning, to automate the detection of SMTs in fullsize X-ray images of cargo containers. Novel approaches for dataset augmentation allowed to train CNNs from-scratch despite the scarcity of data available. We report fewer than 6% false alarms when detecting 90% SMTs synthetically concealed in stream-of-commerce images, which corresponds to an improvement of over an order of magnitude over conventional approaches such as Bag-of-Words (BoWs). The proposed scheme offers potentially super-human performance for a fraction of the time it would take for a security officers to carry out visual inspection (processing time is approximately 3.5s per container image)

Transferring X-ray based automated threat detection between scanners with different energies and resolution

A significant obstacle to developing high performance Deep Learning algorithms for Automated Threat Detection (ATD) in security X-ray imagery, is the difficulty of obtaining large training datasets. In our previous work, we circumvented this problem for ATD in cargo containers, using Threat Image Projection and data augmentation. In this work, we investigate whether data scarcity for other modalities, such as parcels and baggage, can be ameliorated by transforming data from one domain so that it approximates the appearance of another. We present an ontology of ATD datasets to assess where transfer learning may be applied. We define frameworks for transfer at the training and testing stages, and compare the results for both methods against ATD where a common data source is used for training and testing. Our results show very poor transfer, which we attribute to the difficulty of accurately matching the blur and contrast characteristics of different scanners

Guillain-Barré syndrome: a century of progress

In 1916, Guillain, Barré and Strohl reported on two cases of acute flaccid paralysis with high cerebrospinal fluid protein levels and normal cell counts — novel findings that identified the disease we now know as Guillain–Barré syndrome (GBS). 100 years on, we have made great progress with the clinical and pathological characterization of GBS. Early clinicopathological and animal studies indicated that GBS was an immune-mediated demyelinating disorder, and that severe GBS could result in secondary axonal injury; the current treatments of plasma exchange and intravenous immunoglobulin, which were developed in the 1980s, are based on this premise. Subsequent work has, however, shown that primary axonal injury can be the underlying disease. The association of Campylobacter jejuni strains has led to confirmation that anti-ganglioside antibodies are pathogenic and that axonal GBS involves an antibody and complement-mediated disruption of nodes of Ranvier, neuromuscular junctions and other neuronal and glial membranes. Now, ongoing clinical trials of the complement inhibitor eculizumab are the first targeted immunotherapy in GBS

Automated Analysis of Cryptococcal Macrophage Parasitism Using GFP-Tagged Cryptococci

The human fungal pathogens Cryptococcus neoformans and C. gattii cause life-threatening infections of the central nervous system. One of the major characteristics of cryptococcal disease is the ability of the pathogen to parasitise upon phagocytic immune effector cells, a phenomenon that correlates strongly with virulence in rodent models of infection. Despite the importance of phagocyte/Cryptococcus interactions to disease progression, current methods for assaying virulence in the acrophage system are both time consuming and low throughput. Here, we introduce the first stable and fully characterised GFP–expressing derivatives of two widely used cryptococcal strains: C. neoformans serotype A type strain H99 and C. gattii serotype B type strain R265. Both strains show unaltered responses to environmental and host stress conditions and no deficiency in virulence in the macrophage model system. In addition, we report the development of a method to effectively and rapidly investigate macrophage parasitism by flow cytometry, a technique that preserves the accuracy of current approaches but offers a four-fold improvement in speed

Inverse planned stereotactic intensity modulated radiotherapy (IMRT) in the treatment of incompletely and completely resected adenoid cystic carcinomas of the head and neck: initial clinical results and toxicity of treatment

BACKGROUND: Presenting the initial clinical results in the treatment of complex shaped adenoid cystic carcinomas (ACC) of the head and neck region by inverse planned stereotactic IMRT. MATERIALS: 25 patients with huge ACC in different areas of the head and neck were treated. At the time of radiotherapy two patients already suffered from distant metastases. A complete resection of the tumor was possible in only 4 patients. The remaining patients were incompletely resected (R2: 20; R1: 1). 21 patients received an integrated boost IMRT (IBRT), which allow the use of different single doses for different target volumes in one fraction. All patients were treated after inverse treatment planning and stereotactic target point localization. RESULTS: The mean folllow-up was 22.8 months (91 – 1490 days). According to Kaplan Meier the three year overall survival rate was 72%. 4 patients died caused by a systemic progression of the disease. The three-year recurrence free survival was according to Kaplan Meier in this group of patients 38%. 3 patients developed an in-field recurrence and 3 patient showed a metastasis in an adjacent lymph node of the head and neck region. One patient with an in-field recurrence and a patient with the lymph node recurrence could be re-treated by radiotherapy. Both patients are now controlled. Acute side effects >Grade II did only appear so far in a small number of patients. CONCLUSION: The inverse planned stereotactic IMRT is feasible in the treatment of ACC. By using IMRT, high control rates and low side effects could by achieved. Further evaluation concerning the long term follow-up is needed. Due to the technical advantage of IMRT this treatment modality should be used if a particle therapy is not available