Joint reconstruction and material classification in spectral CTBabaheidarian, Parisa; Castañón, David A.
doi: 10.1117/12.2309663pmid: N/A
Detecting the presence of hazardous materials in luggage is an important problem in aviation security. The current generation of inspection systems is based on X-ray computed tomography, followed by recognition systems to identify potential prohibited materials. As such, the image formation algorithms are designed independently of the recognition algorithms. In this paper, we present a new class of algorithms for processing the X-ray data by simultaneously forming images from the collected X-ray observations and identifying the underlying materials in the images. These algorithms exploit information about the possible materials in the image to modify the image reconstruction techniques, as well as material identification. We evaluate our joint algorithm on simulated phantoms using multi-spectral computed tomography, and compare our reconstruction and classification results with alternative state of the art approaches. Our experiments indicate that there are significant improvements in recognition performance possible through our joint approach.
Consensus relaxation on materials of interest for adaptive ATR in CT images of baggagePaglieroni, David W.; Chandrasekaran, Hema; Pechard, Christian; Martz, Harry E.
doi: 10.1117/12.2309839pmid: N/A
An adaptive automatic threat recognition system (AATR) developed at the Lawrence Livermore National Laboratory (LLNL) is described for x-ray CT images of baggage. The AATR automatically adapts to the input object requirement specification (ORS), which can change or evolve over time. These specifications characterize materials of interest (MOIs), basic physical features of interest (FOIs) (such a mass and thickness) and performance goals (detection and false alarm probability) for objects of interest (OOIs). The need and technical requirements for an AATR were developed in collaboration with DHS’s Explosives Division and Northeastern University’s Awareness and Localization of Explosives-Related Threats (ALERT) Center, a DHS Center of Excellence (http://www.northeastern.edu/alert/). Independent of the input ORS, LLNL’s AATR always uses the same algorithm and codes to process CT images. The algorithm adapts in real-time to changes in the input ORS. LLNL’s AATR is thus suitable for dynamic scenarios in which the nature of the OOIs can change rapidly. The AATR uses a spatial consensus relaxation method to determine the most likely material composition for each CT image voxel. The resulting image of most likely material compositions is segmented. An OOI classification statistic (OOI score) is computed for each voxel and each extracted image volume. OOI recognition performance is reported using various metrics on a test set of ~180 plastic bins supplied by the ALERT Center of Excellence. A method is then proposed for automatic decision threshold estimation that can adapt to the detection performance goal, the most likely material composition, and the contents of the baggage.
Automatic threat recognition of prohibited items at aviation checkpoint with x-ray imaging: a deep learning approachLiang, Kevin J.; Heilmann, Geert; Gregory, Christopher; Diallo, Souleymane O.; Carlson, David ; Spell, Gregory P.; Sigman, John B.; Roe, Kris; Carin, Lawrence
doi: 10.1117/12.2309484pmid: N/A
The Transportation Security Administration safeguards all United States air travel. To do so, they employ human inspectors to screen x-ray images of carry-on baggage for threats and other prohibited items, which can be challenging. On the other hand, recent research applying deep learning techniques to computer-aided security screening to assist operators has yielded encouraging results. Deep learning is a subfield of machine learning based on learning abstractions from data, as opposed to engineering features by hand. These techniques have proven to be quite effective in many domains, including computer vision, natural language processing, speech recognition, self-driving cars, and geographical mapping technology. In this paper, we present initial results of a collaboration between Smiths Detection and Duke University funded by the Transportation Security Administration. Using convolutional object detection algorithms trained on annotated x-ray images, we show real-time detection of prohibited items in carry-on luggage. Results of the work so far indicate that this approach can detect selected prohibited items with high accuracy and minimal impact on operational false alarm rates.
Deep learning based sparse view x-ray CT reconstruction for checked baggage screeningMandava, Sagar; Ashok, Amit; Bilgin, Ali
doi: 10.1117/12.2309509pmid: N/A
X-ray computed tomography is widely used in security applications. With growing interest in view-limited systems, which have increased throughput, there is a significant interest in constrained image reconstruction techniques that allows high fidelity reconstruction from limited data. These image reconstruction techniques are commonly characterized by their intense computational requirements making their deployment in real-time imaging applications challenging. Recent success of deep learning techniques in various signal and image processing applications has sparked an interest in using these techniques for image reconstruction problems. In this work, we explore the use of deep learning techniques for reconstruction of baggage CT data and compare these techniques to constrained reconstruction methods.
Towards an x-ray-based coded aperture diffraction system for bulk material identificationDiallo, S. O.; Tadlock, K.; Gregory, C.; Wolter, S.; Greenberg, J. A.; Roe, K.
doi: 10.1117/12.2302513pmid: N/A
The detection of prohibited items at airport checkpoints, especially energetic materials, by means of x-ray imaging technology, is one of the most important tasks in transportation security. Conventional checkpoint X-ray systems exploit the energy dependence of the material- specific attenuation coefficient to estimate an ‘effective’ atomic number (or Zeff ) and, in some cases, the mass density () of a target material, which are then used to classify it. While this technology provides high quality imaging capabilities and satisfactory objects discrimination in many security applications, it also has known limitations. For example, differentiating objects with similar Zeff and/or , such as is often the case for many benign organic materials and explosives, can be a challenging task. X-ray Diffraction Tomography (XRDT), using a coded mask (down stream from the sample), provides structural information that can further enhance material discrimination from the unique chemical/molecular signatures. Here, we present experimental data obtained using our research prototype or ‘XRDT’ scanner, built with off-the shelf components. Using two different industrial solvents, one benign (H2O or water) and one prohibited chemical precursor (2-butanone or methyl-ethyl-ketone (MEK)), we have evaluated the detection performance against material type, sample size, beam size, and investigated the effects of background. Within the scope of our study, we find that a satisfactory tomographic reconstruction and reliable bulk material identification can be achieved with the XRDT. These results will help guide the future development of coded aperture based screening technology at security checkpoint.