A review of automated image understanding within 3D baggage computed tomography security screening
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paper_title: An experimental survey of metal artefact reduction in computed tomography

paper_content:

We present a survey of techniques for the reduction of streaking artefacts caused by metallic objects in X-ray Computed Tomography (CT) images. A comprehensive review of the existing state-of-theart Metal Artefact Reduction (MAR) techniques, drawn predominantly from the medical CT literature, is supported by an experimental comparison of twelve MAR techniques. The experimentation is grounded in an evaluation based on a standard scientific comparison protocol for MAR methods, using a software generated medical phantom image as well as a clinical CT scan. The experimentation is extended by considering novel applications of CT imagery consisting of metal objects in non-tissue surroundings acquired from the aviation security screening domain. We address the shortage of thorough performance analyses in the existing MAR literature by conducting a qualitative as well as quantitative comparative evaluation of the selected techniques. We find that the difficulty in generating accurate priors to be the predominant factor limiting the effectiveness of the state-of-the-art medical MAR techniques when applied to non-medical CT imagery. This study thus extends previous works by: comparing several state-of-the-art MAR techniques; considering both medical and non-medical applications and performing a thorough performance analysis, considering both image quality as well as computational demands.
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paper_title: A distance driven method for metal artefact reduction in computed tomography

paper_content:

This paper presents an extension to a recent intensity-limiting sino-gram completion-based Metal Artefact Reduction (MAR) algorithm for Computed Tomography (CT) images containing multiple metal objects. A novel weighting scheme is introduced, whereby the intensities of the MAR-corrected pixels are modified based on their spatial locations relative to the metal objects. Pixels falling within the straight-line regions connecting multiple metal objects are subjected to less intensive intensity-limiting, thereby compensating for the characteristic dark bands occurring in these regions. Extensive experimentation is performed on a state-of-the-art numerical simulation, a clinical CT data set and a baggage security CT data set. Comprehensive performance analysis, using reference and reference-free error metrics, Bland-Altman plots and visual comparisons, demonstrate an improvement in the restoration of the underestimated intensities occurring in the regions connecting multiple metal objects.
---
paper_title: Improving Weapon Detection in Single Energy X-Ray Images Through Pseudocoloring

paper_content:

This paper describes a series of linear and nonlinear pseudocoloring maps designed and applied to single energy X-ray luggage scans to assist airport screeners in identifying and detecting threat items, particularly hard to see low-density weapons in luggage. Considerations of the psychological and physiological processing involved in the human perception of color as well as the effects of using various color models, such as the RGB and HSI models, were explored. Original grayscale data, various enhanced images, and segmented scenes were used as input to the various color mapping schemes designed in this research. A highly interactive user-friendly graphical interface and a portable test were developed and used in a performance evaluation study involving a population of actual federal airport screeners. The study proved the advantages of using color over gray level data and also allowed the ranking of color maps and selection of the best performing color schemes. Rate improvements in weapon detection of up to 97% were achieved through the use of color
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paper_title: A novel intensity limiting approach to Metal Artefact Reduction in 3D CT baggage imagery

paper_content:

This paper introduces a novel technique for Metal Artefact Reduction (MAR) in the previously unconsidered context 3D CT baggage imagery. The output of a conventional sinogram completion-based MAR approach is refined by imposing an upper limit on the intensity of the corrected images and by performing post-filtering using the non-local means filter. Furthermore, performance is evaluated using a novel quantitative analysis technique, using the ratio of noisy 3D SIFT detection points identified, as well as a standard qualitative comparison (visual quality). The objective of the quantitative analysis is to evaluate the impact of MAR on the application of computer vision techniques for automatic object recognition. The study yields encouraging results in both the qualitative and quantitative analyses. The proposed method yields a significant improvement in performance when compared to algorithms based on linear interpolation and reprojection-reconstruction; especially in terms of reducing the occurrence of new artefacts in the corrected images. The results serve as a strong indication that MAR will aid human and computerised analyses of 3D CT baggage imagery for transport security screening.
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paper_title: Dual energy computed tomography for explosive detection

paper_content:

Single energy computed tomography (CT) scanners use measurements of densities to detect explosives in luggage. It is desirable to apply dual energy techniques to these CT scanners to obtain atomic number measurements to reduce false alarm rates. However, the direct application of existing dual energy techniques has practical problems, such as, approximation errors and lack of boundary constraints in dual energy decomposition, image artifacts, and x-ray spectral drifts. In this paper, we present methods to reduce these problems. The methods include constrained dual energy decomposition, adaptive scatter correction, nonlinear filtering of decomposed projections, and real-time image-based correction for x-ray spectral drifts. We demonstrate the effectiveness of the methods using simulated data and real data obtained from a commercial dual energy CT scanner.
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paper_title: Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab

paper_content:

This is an introductory to intermediate level text on the science of image processing, which employs the Matlab programming language to illustrate some of the elementary, key concepts in modern image processing and pattern recognition. The approach taken is essentially practical and the book offers a framework within which the concepts can be understood by a series of well chosen examples, exercises and computer experiments, drawing on specific examples from within science, medicine and engineering.Clearly divided into eleven distinct chapters, the book begins with a fast-start introduction to image processing to enhance the accessibility of later topics. Subsequent chapters offer increasingly advanced discussion of topics involving more challenging concepts, with the final chapter looking at the application of automated image classification (with Matlab examples) .Matlab is frequently used in the book as a tool for demonstrations, conducting experiments and for solving problems, as it is both ideally suited to this role and is widely available. Prior experience of Matlab is not required and those without access to Matlab can still benefit from the independent presentation of topics and numerous examples.Features a companion website www.wiley.com/go/solomon/fundamentals containing a Matlab fast-start primer, further exercises, examples, instructor resources and accessibility to all files corresponding to the examples and exercises within the book itself.Includes numerous examples, graded exercises and computer experiments to support both students and instructors alike.
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paper_title: Automatic segmentation of unknown objects, with application to baggage security

paper_content:

Computed tomography (CT) is used widely to image patients for medical diagnosis and to scan baggage for threatening materials. Automated reading of these images can be used to reduce the costs of a human operator, extract quantitative information from the images or support the judgements of a human operator. Object quantification requires an image segmentation to make measurements about object size, material composition and morphology. Medical applications mostly require the segmentation of prespecified objects, such as specific organs or lesions, which allows the use of customized algorithms that take advantage of training data to provide orientation and anatomical context of the segmentation targets. In contrast, baggage screening requires the segmentation algorithm to provide segmentation of an unspecified number of objects with enormous variability in size, shape, appearance and spatial context. Furthermore, security systems demand 3D segmentation algorithms that can quickly and reliably detect threats. To address this problem, we present a segmentation algorithm for 3D CT images that makes no assumptions on the number of objects in the image or on the composition of these objects. The algorithm features a new Automatic QUality Measure (AQUA) model that measures the segmentation confidence for any single object (from any segmentation method) and uses this confidence measure to both control splitting and to optimize the segmentation parameters at runtime for each dataset. The algorithm is tested on 27 bags that were packed with a large variety of different objects.
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paper_title: 3D CT baggage image enhancement based on order statistic decomposition

paper_content:

Baggage scanning systems are widely used at security checkpoint in airports for homeland security applications. However, the CT baggage images suffer from background noise and the presence of low contrast. To address this problem, this paper introduces a new 3D CT baggage image enhancement algorithm using order statistic decomposition. Computer simulation and comparisons are given to demonstrate the excellent enhancement performance of the presented new algorithm and its capability of significantly improving visual quality of CT images while removing the background noise.
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paper_title: A distance driven method for metal artefact reduction in computed tomography

paper_content:

This paper presents an extension to a recent intensity-limiting sino-gram completion-based Metal Artefact Reduction (MAR) algorithm for Computed Tomography (CT) images containing multiple metal objects. A novel weighting scheme is introduced, whereby the intensities of the MAR-corrected pixels are modified based on their spatial locations relative to the metal objects. Pixels falling within the straight-line regions connecting multiple metal objects are subjected to less intensive intensity-limiting, thereby compensating for the characteristic dark bands occurring in these regions. Extensive experimentation is performed on a state-of-the-art numerical simulation, a clinical CT data set and a baggage security CT data set. Comprehensive performance analysis, using reference and reference-free error metrics, Bland-Altman plots and visual comparisons, demonstrate an improvement in the restoration of the underestimated intensities occurring in the regions connecting multiple metal objects.
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paper_title: Computed Tomography: Physical Principles, Clinical Applications, and Quality Control

paper_content:

Chapter 1: CT: An Overview Chapter 2: Digital Image Processing Chapter 3: Physical Principles of CT Chapter 4: Data Acquisition Chapter 5: Image Reconstruction Chapter 6: Iterative Reconstruction Fundamentals Chapter 7: Basic Instrumentation Chapter 8: Image Post Processing Chapter 9: Image Quality Chapter 10: Radiation Dose Chapter 11: MuliSlice CT Principles Chapter 12: Other Technical Applications Chapter 13: Three-Dimensional CT Chapter 14: PET/SPECT CT Chapter 15: CT of the Head, Neck, and Spine Chapter 16: CT of the Body Chapter 17: Pediatric CT Chapter 18: Quality Control for CT Scanners Appendix A: Spiral Versus Helical Appendix B: Historical Look at Specific Features of CT Scanners Appendix C: Physics of Cardiac Imaging with Multiple-Row Detector CT
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paper_title: Computed Tomography: Principles, Design, Artifacts, and Recent Advances

paper_content:

Introduction Preliminaries Image Reconstruction Image Presentation Key Performance Parameters of a CT Scanner Major Components of CT Scanner Image Artifacts: Appearances, Causes, and Corrections Computer Simulation and Analysis. Helical or Spiral CT Multislice CT X-ray Dose and Reduction Techniques Advanced CT Applications.
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paper_title: A new image denoising framework based on bilateral filter

paper_content:

The bilateral filter is a nonlinear filter that does spatial averaging without smoothing edges; it has shown to be ::: an effective image denoising technique in addition to some other applications. There are two main contributions ::: of this paper. First, we provide an empirical study of the optimal parameter selection for the bilateral filter in ::: image denoising applications. Second, we present an extension of the bilateral filter: multi-resolution bilateral ::: filter, where bilateral filtering is applied to low-frequency subbands of a signal decomposed using an orthogonal ::: wavelet transform. Combined with wavelet thresholding, this new image denoising framework turns out to be ::: very effective in eliminating noise in real noisy images. We provide experimental results with both simulated ::: data and real data.
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paper_title: Scale-space and edge detection using anisotropic diffusion

paper_content:

A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced. The diffusion coefficient is chosen to vary spatially in such a way as to encourage intraregion smoothing rather than interregion smoothing. It is shown that the 'no new maxima should be generated at coarse scales' property of conventional scale space is preserved. As the region boundaries in the approach remain sharp, a high-quality edge detector which successfully exploits global information is obtained. Experimental results are shown on a number of images. Parallel hardware implementations are made feasible because the algorithm involves elementary, local operations replicated over the image. >
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paper_title: Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab

paper_content:

This is an introductory to intermediate level text on the science of image processing, which employs the Matlab programming language to illustrate some of the elementary, key concepts in modern image processing and pattern recognition. The approach taken is essentially practical and the book offers a framework within which the concepts can be understood by a series of well chosen examples, exercises and computer experiments, drawing on specific examples from within science, medicine and engineering.Clearly divided into eleven distinct chapters, the book begins with a fast-start introduction to image processing to enhance the accessibility of later topics. Subsequent chapters offer increasingly advanced discussion of topics involving more challenging concepts, with the final chapter looking at the application of automated image classification (with Matlab examples) .Matlab is frequently used in the book as a tool for demonstrations, conducting experiments and for solving problems, as it is both ideally suited to this role and is widely available. Prior experience of Matlab is not required and those without access to Matlab can still benefit from the independent presentation of topics and numerous examples.Features a companion website www.wiley.com/go/solomon/fundamentals containing a Matlab fast-start primer, further exercises, examples, instructor resources and accessibility to all files corresponding to the examples and exercises within the book itself.Includes numerous examples, graded exercises and computer experiments to support both students and instructors alike.
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paper_title: CT baggage image enhancement using a combination of alpha-weighted mean separation and histogram equalization

paper_content:

Baggage scanning systems are used for detecting the presence of explosives and other prohibited items in baggage at ::: security checkpoints in airports. However, the CT baggage images contain projection noise and are of low resolution. ::: This paper introduces a new enhancement algorithm combining alpha-weighted mean separation and histogram ::: equalization to enhance the CT baggage images while removing the background projection noise. A new enhancement ::: measure is introduced for quantitative assessment of image enhancement. Simulations and a comparative analysis are ::: given to demonstrate the new algorithm's performance.
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paper_title: 3D CT baggage image enhancement based on order statistic decomposition

paper_content:

Baggage scanning systems are widely used at security checkpoint in airports for homeland security applications. However, the CT baggage images suffer from background noise and the presence of low contrast. To address this problem, this paper introduces a new 3D CT baggage image enhancement algorithm using order statistic decomposition. Computer simulation and comparisons are given to demonstrate the excellent enhancement performance of the presented new algorithm and its capability of significantly improving visual quality of CT images while removing the background noise.
---
paper_title: An experimental survey of metal artefact reduction in computed tomography

paper_content:

We present a survey of techniques for the reduction of streaking artefacts caused by metallic objects in X-ray Computed Tomography (CT) images. A comprehensive review of the existing state-of-theart Metal Artefact Reduction (MAR) techniques, drawn predominantly from the medical CT literature, is supported by an experimental comparison of twelve MAR techniques. The experimentation is grounded in an evaluation based on a standard scientific comparison protocol for MAR methods, using a software generated medical phantom image as well as a clinical CT scan. The experimentation is extended by considering novel applications of CT imagery consisting of metal objects in non-tissue surroundings acquired from the aviation security screening domain. We address the shortage of thorough performance analyses in the existing MAR literature by conducting a qualitative as well as quantitative comparative evaluation of the selected techniques. We find that the difficulty in generating accurate priors to be the predominant factor limiting the effectiveness of the state-of-the-art medical MAR techniques when applied to non-medical CT imagery. This study thus extends previous works by: comparing several state-of-the-art MAR techniques; considering both medical and non-medical applications and performing a thorough performance analysis, considering both image quality as well as computational demands.
---
paper_title: A distance driven method for metal artefact reduction in computed tomography

paper_content:

This paper presents an extension to a recent intensity-limiting sino-gram completion-based Metal Artefact Reduction (MAR) algorithm for Computed Tomography (CT) images containing multiple metal objects. A novel weighting scheme is introduced, whereby the intensities of the MAR-corrected pixels are modified based on their spatial locations relative to the metal objects. Pixels falling within the straight-line regions connecting multiple metal objects are subjected to less intensive intensity-limiting, thereby compensating for the characteristic dark bands occurring in these regions. Extensive experimentation is performed on a state-of-the-art numerical simulation, a clinical CT data set and a baggage security CT data set. Comprehensive performance analysis, using reference and reference-free error metrics, Bland-Altman plots and visual comparisons, demonstrate an improvement in the restoration of the underestimated intensities occurring in the regions connecting multiple metal objects.
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paper_title: A three-dimensional statistical approach to improved image quality for multislice helical CT

paper_content:

Multislice helical computed tomography scanning offers the advantages of faster acquisition and wide organ coverage for routine clinical diagnostic purposes. However, image reconstruction is faced with the challenges of three-dimensional cone-beam geometry, data completeness issues, and low dosage. Of all available reconstruction methods, statistical iterative reconstruction (IR) techniques appear particularly promising since they provide the flexibility of accurate physical noise modeling and geometric system description. In this paper, we present the application of Bayesian iterative algorithms to real 3D multislice helical data to demonstrate significant image quality improvement over conventional techniques. We also introduce a novel prior distribution designed to provide flexibility in its parameters to fine-tune image quality. Specifically, enhanced image resolution and lower noise have been achieved, concurrently with the reduction of helical cone-beam artifacts, as demonstrated by phantom studies. Clinical results also illustrate the capabilities of the algorithm on real patient data. Although computational load remains a significant challenge for practical development, superior image quality combined with advancements in computing technology make IR techniques a legitimate candidate for future clinical applications.
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paper_title: Iterative deblurring for CT metal artifact reduction

paper_content:

Iterative deblurring methods using the expectation maximization (EM) formulation and the algebraic reconstruction technique (ART), respectively, are adapted for metal artifact reduction in medical computed tomography (CT). In experiments with synthetic noise-free and additive noisy projection data of dental phantoms, it is found that both simultaneous iterative algorithms produce superior image quality as compared to filtered backprojection after linearly fitting projection gaps. Furthermore, the EM-type algorithm converges faster than the ART-type algorithm in terms of either the I-divergence or Euclidean distance between ideal and reprojected data in the authors' simulation. Also, for a given iteration number, the EM-type deblurring method produces better image clarity but stronger noise than the ART-type reconstruction. The computational complexity of EM- and ART-based iterative deblurring is essentially the same, dominated by reprojection and backprojection. Relevant practical and theoretical issues are discussed.
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paper_title: The Radon Transform And Some Of Its Applications

paper_content:

the radon transform and some of its applications is available in our book collection an online access to it is set as public so you can get it instantly. Our digital library spans in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the the radon transform and some of its applications is universally compatible with any devices to read.
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paper_title: CT metal artifact reduction method correcting for beam hardening and missing projections.

paper_content:

We present and validate a computed tomography (CT) metal artifact reduction method that is effective for a wide spectrum of clinical implant materials. Projections through low-Z implants such as titanium were corrected using a novel physics correction algorithm that reduces beam hardening errors. In the case of high-Z implants (dental fillings, gold, platinum), projections through the implant were considered missing and regularized iterative reconstruction was performed. Both algorithms were combined if multiple implant materials were present. For comparison, a conventional projection interpolation method was implemented. In a blinded and randomized evaluation, ten radiation oncologists ranked the quality of patient scans on which the different methods were applied. For scans that included low-Z implants, the proposed method was ranked as the best method in 90% of the reviews. It was ranked superior to the original reconstruction (p = 0.0008), conventional projection interpolation (p < 0.0001) and regularized limited data reconstruction (p = 0.0002). All reviewers ranked the method first for scans with high-Z implants, and better as compared to the original reconstruction (p < 0.0001) and projection interpolation (p = 0.004). We conclude that effective reduction of CT metal artifacts can be achieved by combining algorithms tailored to specific types of implant materials.
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paper_title: Improving Weapon Detection in Single Energy X-Ray Images Through Pseudocoloring

paper_content:

This paper describes a series of linear and nonlinear pseudocoloring maps designed and applied to single energy X-ray luggage scans to assist airport screeners in identifying and detecting threat items, particularly hard to see low-density weapons in luggage. Considerations of the psychological and physiological processing involved in the human perception of color as well as the effects of using various color models, such as the RGB and HSI models, were explored. Original grayscale data, various enhanced images, and segmented scenes were used as input to the various color mapping schemes designed in this research. A highly interactive user-friendly graphical interface and a portable test were developed and used in a performance evaluation study involving a population of actual federal airport screeners. The study proved the advantages of using color over gray level data and also allowed the ranking of color maps and selection of the best performing color schemes. Rate improvements in weapon detection of up to 97% were achieved through the use of color
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paper_title: A unified approach to statistical tomography using coordinate descent optimization

paper_content:

Over the past years there has been considerable interest in statistically optimal reconstruction of cross-sectional images from tomographic data. In particular, a variety of such algorithms have been proposed for maximum a posteriori (MAP) reconstruction from emission tomographic data. While MAP estimation requires the solution of an optimization problem, most existing reconstruction algorithms take an indirect approach based on the expectation maximization (EM) algorithm. We propose a new approach to statistically optimal image reconstruction based on direct optimization of the MAP criterion. The key to this direct optimization approach is greedy pixel-wise computations known as iterative coordinate decent (ICD). We propose a novel method for computing the ICD updates, which we call ICD/Newton-Raphson. We show that ICD/Newton-Raphson requires approximately the same amount of computation per iteration as EM-based approaches, but the new method converges much more rapidly (in our experiments, typically five to ten iterations). Other advantages of the ICD/Newton-Raphson method are that it is easily applied to MAP estimation of transmission tomograms, and typical convex constraints, such as positivity, are easily incorporated.
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paper_title: Fast hybrid algorithms for PET image reconstruction

paper_content:

We describe a hybrid approach to iterative PET image reconstruction in which we combine three algorithms: preconditioned conjugate gradient (PCG), ordered subsets separable paraboloidal surrogate (OSSPS), and emission reconstruction incremental optimization transfer (ERIOT). These algorithms exhibit quite different convergence behavior, e.g. the initial convergence of OSSPS is fast but it soon enters a limit cycle. Conversely, initial convergence of PCG is slow compared to OSSPS and ERIOT but its asymptotic behavior is the fastest. The hybrid approach estimates convergence behavior for each method by fitting an exponential to the objective function as a function of iteration number and switches between the algorithms to optimize the convergence behavior throughout the iterations. This hybrid approach is compared to each of the component algorithms in application to simulated PET data demonstrating a reduction of at least 50% in iterations required for effective convergence compared to use of PCG alone.
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paper_title: Fast Model-Based X-Ray CT Reconstruction Using Spatially Nonhomogeneous ICD Optimization

paper_content:

Recent applications of model-based iterative reconstruction (MBIR) algorithms to multislice helical CT reconstructions have shown that MBIR can greatly improve image quality by increasing resolution as well as reducing noise and some artifacts. However, high computational cost and long reconstruction times remain as a barrier to the use of MBIR in practical applications. Among the various iterative methods that have been studied for MBIR, iterative coordinate descent (ICD) has been found to have relatively low overall computational requirements due to its fast convergence. This paper presents a fast model-based iterative reconstruction algorithm using spatially nonhomogeneous ICD (NH-ICD) optimization. The NH-ICD algorithm speeds up convergence by focusing computation where it is most needed. The NH-ICD algorithm has a mechanism that adaptively selects voxels for update. First, a voxel selection criterion VSC determines the voxels in greatest need of update. Then a voxel selection algorithm VSA selects the order of successive voxel updates based upon the need for repeated updates of some locations, while retaining characteristics for global convergence. In order to speed up each voxel update, we also propose a fast 1-D optimization algorithm that uses a quadratic substitute function to upper bound the local 1-D objective function, so that a closed form solution can be obtained rather than using a computationally expensive line search algorithm. We examine the performance of the proposed algorithm using several clinical data sets of various anatomy. The experimental results show that the proposed method accelerates the reconstructions by roughly a factor of three on average for typical 3-D multislice geometries.
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paper_title: Computed Tomography: Principles, Design, Artifacts, and Recent Advances

paper_content:

Introduction Preliminaries Image Reconstruction Image Presentation Key Performance Parameters of a CT Scanner Major Components of CT Scanner Image Artifacts: Appearances, Causes, and Corrections Computer Simulation and Analysis. Helical or Spiral CT Multislice CT X-ray Dose and Reduction Techniques Advanced CT Applications.
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paper_title: A row-action alternative to the EM algorithm for maximizing likelihood in emission tomography.

paper_content:

The maximum likelihood (ML) approach to estimating the radioactive distribution in the body cross section has become very popular among researchers in emission computed tomography (ECT) since it has been shown to provide very good images compared to those produced with the conventional filtered backprojection (FBP) algorithm. The expectation maximization (EM) algorithm is an often-used iterative approach for maximizing the Poisson likelihood in ECT because of its attractive theoretical and practical properties. Its major disadvantage is that, due to its slow rate of convergence, a large amount of computation is often required to achieve an acceptable image. Here, the authors present a row-action maximum likelihood algorithm (RAMLA) as an alternative to the EM algorithm for maximizing the Poisson likelihood in ECT. The authors deduce the convergence properties of this algorithm and demonstrate by way of computer simulations that the early iterates of RAMLA increase the Poisson likelihood in ECT at an order of magnitude faster that the standard EM algorithm. Specifically, the authors show that, from the point of view of measuring total radionuclide uptake in simulated brain phantoms, iterations 1, 2, 3, and 4 of RAMLA perform at least as well as iterations 45, 60, 70, and 80, respectively, of EM. Moreover, the authors show that iterations 1, 2, 3, and 4 of RAMLA achieve comparable likelihood values as iterations 45, 60, 70, and 80, respectively, of EM. The authors also present a modified version of a recent fast ordered subsets EM (OS-EM) algorithm and show that RAMLA is a special case of this modified OS-EM. Furthermore, the authors show that their modification converges to a ML solution whereas the standard OS-EM does not.
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paper_title: A novel intensity limiting approach to Metal Artefact Reduction in 3D CT baggage imagery

paper_content:

This paper introduces a novel technique for Metal Artefact Reduction (MAR) in the previously unconsidered context 3D CT baggage imagery. The output of a conventional sinogram completion-based MAR approach is refined by imposing an upper limit on the intensity of the corrected images and by performing post-filtering using the non-local means filter. Furthermore, performance is evaluated using a novel quantitative analysis technique, using the ratio of noisy 3D SIFT detection points identified, as well as a standard qualitative comparison (visual quality). The objective of the quantitative analysis is to evaluate the impact of MAR on the application of computer vision techniques for automatic object recognition. The study yields encouraging results in both the qualitative and quantitative analyses. The proposed method yields a significant improvement in performance when compared to algorithms based on linear interpolation and reprojection-reconstruction; especially in terms of reducing the occurrence of new artefacts in the corrected images. The results serve as a strong indication that MAR will aid human and computerised analyses of 3D CT baggage imagery for transport security screening.
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paper_title: Reduction of CT artifacts caused by metallic implants.

paper_content:

A technique to reduce metallic implant artifacts on computed tomography scans is presented. The implant boundaries are determined semiautomatically; the missing projection data are replaced by linear interpolation. The complete procedure requires 1-2 minutes per scan. Images with greatly improved quality were obtained in the presence of surgical clips and pelvic implants; success is limited in highly structured regions, such as the facial skull.
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paper_title: METAL ARTIFACT REDUCTION FOR CT-BASED LUGGAGE SCREENING

paper_content:

In aviation security, checked luggage is screened by computed tomography (CT) scanning, followed by automatic target recognition from the CT images. Metal objects in the bags cause image artifacts that degrade object representation, leading to increased false alarms. We develop a new method, which isolates and reduces artifacts in an intermediate image, based on a numerical optimization that de-emphasizes metal and has a novel constraint for beam hardening and scatter. Results on test bags showed excellent artifact reduction, even for multiple metal objects.
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paper_title: Metal streak artifacts in X-ray computed tomography: a simulation study

paper_content:

Metal streak artifacts are an important problem in X-ray computed tomography. A high-resolution 2D fan-beam computed tomography simulator is presented. Several potential causes of metal streak artifacts are studied using phantom measurements and simulations. Beam hardening, scatter, noise and exponential edge-gradient effects are identified as important causes of metal streak artifacts. Furthermore, aliasing effects and object motion can be also responsible for certain metal streak artifacts.
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paper_title: Reduction of metal streak artifacts in X-ray computed tomography using a transmission maximum a posteriori algorithm

paper_content:

A maximum a posteriori algorithm for reduction of metal streak artifacts in X-ray computed tomography is presented. The algorithm uses a Markov random field smoothness prior and applies increased sampling in the reconstructed image. Good results are obtained for simulations and phantom measurements: streak artifacts are reduced while small, line-shaped details are preserved.
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paper_title: Metal artifact reduction in computed tomography using local models in an image block‐iterative scheme

paper_content:

Purpose: In iterative reconstruction, metal artifacts can be reduced by applying more accurate reconstruction models that are usually also more computationally demanding. The hypothesis of this work is that these complex models only need to be applied in the vicinity of the metals and that a less complex model can be used for the remainder of the reconstruction volume. Methods: A method is described that automatically divides the reconstruction volume into metal and nonmetal regions. The different regions are called patches. A different energy and resolution model can be assigned to each of the patches. The patches containing metals are reconstructed with a fully polychromatic spectral model (IMPACT) and if necessary with an increased resolution model. The patch without metals is reconstructed with a simple polychromatic model (MLTRC) that only includes the spectral behavior of water attenuation. Comparing the computational complexity of IMPACT and MLTRC gives a ratio of 8:3. The different patches are updated sequentially as in a grouped coordinate algorithm. Two phantoms were simulated and measured: a circular phantom containing small metal cylinders and a body phantom representing a human pelvis with two femoral implants. As a first test, the sequential update of the patches was applied while using the same energy model for all patches. Second, the local model approach was applied using MLTRC for nonmetal regions and IMPACT for metal regions. The results of different iterative reconstruction schemes are compared to the results of projection completion, another important method for the reduction of metal artifacts. Results: Reconstruction schemes including the sequential update of the patches result in images with less streak artifacts compared to a regular reconstruction. The sequential update of each of the metal regions improves the relative convergence of the metals (edges and attenuation values) against the rest of the image, which leads to an improved artifact reduction. Using the combined IMPACT+MLTRC model results in a similar image quality as using IMPACT everywhere, while providing an important benefit regarding computational complexity. Some streak and shadow artifacts were still present, but all structures present in the phantom could be observed. Projection completion results in reconstructions with less obvious streak and shadow artifacts but tends to deform or erase structures lying close to or in between metallic structures. Conclusions: Metal artifact reduction with iterative reconstruction can be achieved by using complex models only locally without losing image quality. Separately updating metal regions leads to reduced streak artifacts. Structures lying close to or in between metals are often better reconstructed, compared to projection completion results, because all available information is used.
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paper_title: Iterative correction of beam hardening artifacts in CT.

paper_content:

PURPOSE ::: To reduce beam hardening artifacts in CT in case of an unknown x-ray spectrum and unknown material properties. ::: ::: ::: METHODS ::: The authors assume that the object can be segmented into a few materials with different attenuation coefficients, and parameterize the spectrum using a small number of energy bins. The corresponding unknown spectrum parameters and material attenuation values are estimated by minimizing the difference between the measured sinogram data and a simulated polychromatic sinogram. Three iterative algorithms are derived from this approach: two reconstruction algorithms IGR and IFR, and one sinogram precorrection method ISP. ::: ::: ::: RESULTS ::: The methods are applied on real x-ray data of a high and a low-contrast phantom. All three methods successfully reduce the cupping artifacts caused by the beam polychromaticity in such a way that the reconstruction of each homogeneous region is to good accuracy homogeneous, even in case the segmentation of the preliminary reconstruction image is poor. In addition, the results show that the three methods tolerate relatively large variations in uniformity within the segments. ::: ::: ::: CONCLUSIONS ::: We show that even without prior knowledge about materials or spectrum, effective beam hardening correction can be obtained.
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paper_title: Normalized metal artifact reduction (NMAR) in computed tomography

paper_content:

Purpose: While modern clinical CT scanners under normal circumstances produce high quality images, severe artifacts degrade the image quality and the diagnostic value if metal prostheses or other metal objects are present in the field of measurement. Standard methods for metal artifact reduction (MAR) replace those parts of the projection data that are affected by metal (the so-called metal trace or metal shadow) by interpolation. However, while sinogram interpolation methods efficiently remove metal artifacts, new artifacts are often introduced, as interpolation cannot completely recover the information from the metal trace. The purpose of this work is to introduce a generalized normalization technique for MAR, allowing for efficient reduction of metal artifacts while adding almost no new ones. The method presented is compared to a standard MAR method, as well as MAR using simple length normalization. ::: ::: Methods: In the first step, metal is segmented in the image domain by thresholding. A 3D forward projection identifies the metal trace in the original projections. Before interpolation, the projections are normalized based on a 3D forward projection of a prior image. This prior image is obtained, for example, by a multithreshold segmentation of the initial image. The original rawdata are divided by the projection data of the prior image and, after interpolation, denormalized again. Simulations and measurements are performed to compare normalized metal artifact reduction (NMAR) to standard MAR with linear interpolation and MAR based on simple length normalization. ::: ::: Results: Promising results for clinical spiral cone-beam data are presented in this work. Included are patients with hip prostheses, dental fillings, and spine fixation, which were scanned at pitch values ranging from 0.9 to 3.2. Image quality is improved considerably, particularly for metal implants within bone structures or in their proximity. The improvements are evaluated by comparing profiles through images and sinograms for the different methods and by inspecting ROIs. NMAR outperforms both other methods in all cases. It reduces metal artifacts to a minimum, even close to metal regions. Even for patients with dental fillings, which cause most severe artifacts, satisfactory results are obtained with NMAR. In contrast to other methods, NMAR prevents the usual blurring of structures close to metal implants if the metal artifacts are moderate. ::: ::: Conclusions: NMAR clearly outperforms the other methods for both moderate and severe artifacts. The proposed method reliably reduces metal artifacts from simulated as well as from clinical CT data. Computationally efficient and inexpensive compared to iterative methods, NMAR can be used as an additional step in any conventional sinogram inpainting-based MAR method.
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paper_title: Convergent block-iterative algorithms for image reconstruction from inconsistent data

paper_content:

It has been shown that convergence to a solution can be significantly accelerated for a number of iterative image reconstruction algorithms, including simultaneous Cimmino-type algorithms, the "expectation maximization" method for maximizing likelihood (EMML) and the simultaneous multiplicative algebraic reconstruction technique (SMART), through the use of rescaled block-iterative (BI) methods. These BI methods involve partitioning the data into disjoint subsets and using only one subset at each step of the iteration. One drawback of these methods is their failure to converge to an approximate solution in the inconsistent case, in which no image consistent with the data exists; they are always observed to produce limit cycles (LCs) of distinct images, through which the algorithm cycles. No one of these images provides a suitable solution, in general. The question that arises then is whether or not these LC vectors retain sufficient information to construct from them a suitable approximate solution; we show that they do. To demonstrate that, we employ a "feedback" technique in which the LC vectors are used to produce a new "data" vector, and the algorithm restarted. Convergence of this nested iterative scheme to an approximate solution is then proven. Preliminary work also suggests that this feedback method may be incorporated in a practical reconstruction method.
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paper_title: Accelerated Image Reconstruction using Ordered Subsets of Projection Data

paper_content:

The authors define ordered subset processing for standard algorithms (such as expectation maximization, EM) for image restoration from projections. Ordered subsets methods group projection data into an ordered sequence of subsets (or blocks). An iteration of ordered subsets EM is defined as a single pass through all the subsets, in each subset using the current estimate to initialize application of EM with that data subset. This approach is similar in concept to block-Kaczmarz methods introduced by Eggermont et al. (1981) for iterative reconstruction. Simultaneous iterative reconstruction (SIRT) and multiplicative algebraic reconstruction (MART) techniques are well known special cases. Ordered subsets EM (OS-EM) provides a restoration imposing a natural positivity condition and with close links to the EM algorithm. OS-EM is applicable in both single photon (SPECT) and positron emission tomography (PET). In simulation studies in SPECT, the OS-EM algorithm provides an order-of-magnitude acceleration over EM, with restoration quality maintained. >
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paper_title: Artifacts in CT: recognition and avoidance.

paper_content:

Artifacts can seriously degrade the quality of computed tomographic (CT) images, sometimes to the point of making them diagnostically unusable. To optimize image quality, it is necessary to understand why artifacts occur and how they can be prevented or suppressed. CT artifacts originate from a range of sources. Physics-based artifacts result from the physical processes involved in the acquisition of CT data. Patient-based artifacts are caused by such factors as patient movement or the presence of metallic materials in or on the patient. Scanner-based artifacts result from imperfections in scanner function. Helical and multisection technique artifacts are produced by the image reconstruction process. Design features incorporated into modern CT scanners minimize some types of artifacts, and some can be partially corrected by the scanner software. However, in many instances, careful patient positioning and optimum selection of scanning parameters are the most important factors in avoiding CT artifacts.
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paper_title: Evaluation of Two Iterative Techniques for Reducing Metal Artifacts in Computed Tomography

paper_content:

Multidetector CT facilitates reduced metal artifacts in images from simulated and clinical scans and has the potential to improve diagnostic accuracy.
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paper_title: METAL ARTIFACT REDUCTION FOR CT-BASED LUGGAGE SCREENING

paper_content:

In aviation security, checked luggage is screened by computed tomography (CT) scanning, followed by automatic target recognition from the CT images. Metal objects in the bags cause image artifacts that degrade object representation, leading to increased false alarms. We develop a new method, which isolates and reduces artifacts in an intermediate image, based on a numerical optimization that de-emphasizes metal and has a novel constraint for beam hardening and scatter. Results on test bags showed excellent artifact reduction, even for multiple metal objects.
---
paper_title: A distance driven method for metal artefact reduction in computed tomography

paper_content:

This paper presents an extension to a recent intensity-limiting sino-gram completion-based Metal Artefact Reduction (MAR) algorithm for Computed Tomography (CT) images containing multiple metal objects. A novel weighting scheme is introduced, whereby the intensities of the MAR-corrected pixels are modified based on their spatial locations relative to the metal objects. Pixels falling within the straight-line regions connecting multiple metal objects are subjected to less intensive intensity-limiting, thereby compensating for the characteristic dark bands occurring in these regions. Extensive experimentation is performed on a state-of-the-art numerical simulation, a clinical CT data set and a baggage security CT data set. Comprehensive performance analysis, using reference and reference-free error metrics, Bland-Altman plots and visual comparisons, demonstrate an improvement in the restoration of the underestimated intensities occurring in the regions connecting multiple metal objects.
---
paper_title: Multiscale nonlinear decomposition: the sieve decomposition theorem

paper_content:

Sieves decompose one dimensional bounded functions (d/sub m/)/sub m=1//sup R/ that represent the information in a manner that is analogous to the pyramid of wavelets obtained by linear decomposition. Sieves based on sequences of increasing scale open-closings with flat structuring elements (M and N filters) map f to {d} and the recomposition, consisting of adding up all the granule functions, maps {d} to f. Experiments show that a more general property exists such that {d/spl circ/} maps to f/spl circ/ and back to {d/spl circ/} where the granule functions {d/spl circ/} are obtained from {d} by applying any operator /spl alpha/ consisting of changing the amplitudes of some granules, including zero, without changing their signs. In other words, the set of granule function vectors produced by the decomposition is closed under the operation /spl alpha/. An analytical proof of this property is presented. This property means that filters are useful in the context of feature recognition and, in addition, opens the way for an analysis of the noise resistance of sieves.
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paper_title: Symmetric region growing

paper_content:

The goal of image segmentation is to partition a digital image into disjoint regions of interest. Of the many proposed image segmentation methods, region growing has been one of the most popular. Research on region growing, however, has focused primarily on the design of feature measures and on growing and merging criteria. Most of these methods have an inherent dependence on the order in which the points and regions are examined. This weakness implies that a desired segmented result is sensitive to the selection of the initial growing points. We define a set of theoretical criteria for a subclass of region-growing algorithms that are insensitive to the selection of the initial growing points. This class of algorithms, referred to as symmetric region growing (SymRG), leads to a single-pass region-growing approach applicable to any dimensionality of images. Furthermore, they lead to region-growing algorithms that are both memory- and computation-efficient. Finally, by-products of this general paradigm are algorithms for fast connected-component labeling and cavity deletion. The paper gives theoretical results and 3-D image examples.
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paper_title: The Segmentation of Images via Scale-Space Trees

paper_content:

A useful representation of an image would be an object tree in which nodes represent objects, or parts of objects, and which includes at least one node that, together with its children, represents each object: a grandmothernode. It is shown that scale-trees, obtained from greyscale images, approximate such a tree. It is then shown how they may be modified using other attributes to more closely become object trees. The result is a data structure that provides “handles” for every element of the image that can be used for manipulating the image. This segmentation has potential for object recognition.
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paper_title: METAL ARTIFACT REDUCTION FOR CT-BASED LUGGAGE SCREENING

paper_content:

In aviation security, checked luggage is screened by computed tomography (CT) scanning, followed by automatic target recognition from the CT images. Metal objects in the bags cause image artifacts that degrade object representation, leading to increased false alarms. We develop a new method, which isolates and reduces artifacts in an intermediate image, based on a numerical optimization that de-emphasizes metal and has a novel constraint for beam hardening and scatter. Results on test bags showed excellent artifact reduction, even for multiple metal objects.
---
paper_title: Fast, Quality, Segmentation of Large Volumes -- Isoperimetric Distance Trees

paper_content:

For many medical segmentation tasks, the contrast along most of the boundary of the target object is high, allowing simple thresholding or region growing approaches to provide nearly sufficient solutions for the task. However, the regions recovered by these techniques frequently leak through bottlenecks in which the contrast is low or non-existent. We propose a new approach based on a novel speed-up of the isoperimetric algorithm [1] that can solve the problem of leaks through a bottleneck. The speed enhancement converts the isoperimetric segmentation algorithm to a fast, linear-time computation by using a tree representation as the underlying graph instead of a standard lattice structure. In this paper, we show how to create an appropriate tree substrate for the segmentation problem and how to use this structure to perform a linear-time computation of the isoperimetric algorithm. This approach is shown to overcome common problems with watershed-based techniques and to provide fast, high-quality results on large datasets.
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paper_title: Investigating existing medical CT segmentation techniques within automated baggage and package inspection

paper_content:

3D Computed Tomography (CT) image segmentation is already well established tool in medical research and in routine daily clinical practice. However, such techniques have not been used in the context of 3D CT image segmentation for baggage and package security screening using CT imagery. CT systems are increasingly used in airports for security baggage examination. We propose in this contribution an investigation of the current 3D CT medical image segmentation methods for use in this new domain. Experimental results of 3D segmentation on real CT baggage security imagery using a range of techniques are presented and discussed.
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paper_title: Algorithms in C

paper_content:

Algorithms in C is a comprehensive repository of algorithms, complete with code. If you're in a pinch and need to code something up fast, this book is the place to look. Starting with basic data structures, Algorithms in C covers an enormous scope of information, with extensive treatment of searching and advanced data structures, sorting, string processing, computational geometry, graph problems, and mathematical algorithms. Although the manual often neglects to provide rigorous analysis, the text surrounding the algorithms provides clear and relevant insight into why the algorithms work.
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paper_title: Finding stable salient contours

paper_content:

Methods for generating maximally stable extremal regions are generalized to make intensity trees. Such trees may be computed quickly, but they are large so there is a need to select useful nodes within the tree. Methods for simplifying the tree are developed and it is shown that standard confidence tests may be applied to regions identified as parent and child nodes in the tree. These tests provide a principled way to edit the tree and hence control its size. One of the algorithms for simplifying trees is able to reduce the tree size by at least 90% while retaining important nodes. Furthermore the tree can be parsed to identify salient contours which are presented as generalisations of maximally stable extremal regions.
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paper_title: A Parametric Level Set Approach to Simultaneous Object Identification and Background Reconstruction for Dual Energy Computed Tomography

paper_content:

Dual energy computerized tomography has gained great interest because of its ability to characterize the chemical composition of a material rather than simply providing relative attenuation images as in conventional tomography. The purpose of this paper is to introduce a novel polychromatic dual energy processing algorithm with an emphasis on detection and characterization of piecewise constant objects embedded in an unknown, cluttered background. Physical properties of the objects, specifically the Compton scattering and photoelectric absorption coefficients, are assumed to be known with some level of uncertainty. Our approach is based on a level-set representation of the characteristic function of the object and encompasses a number of regularization techniques for addressing both the prior information we have concerning the physical properties of the object as well as fundamental, physics-based limitations associated with our ability to jointly recover the Compton scattering and photoelectric absorption properties of the scene. In the absence of an object with appropriate physical properties, our approach returns a null characteristic function and thus can be viewed as simultaneously solving the detection and characterization problems. Unlike the vast majority of methods which define the level set function non-parametrically, i.e., as a dense set of pixel values), we define our level set parametrically via radial basis functions (RBF's) and employ a Gauss-Newton type algorithm for cost minimization. Numerical results show that the algorithm successfully detects objects of interest, finds their shape and location, and gives a adequate reconstruction of the background.
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paper_title: Dual energy CT: preliminary observations and potential clinical applications in the abdomen

paper_content:

Dual energy CT (DECT) is a new technique that allows differentiation of materials and tissues based on CT density values derived from two synchronous CT acquisitions at different tube potentials. With the introduction of a new dual source CT system, this technique can now be used routinely in abdominal imaging. Potential clinical applications include evaluation of renal masses, liver lesions, urinary calculi, small bowel, pancreas, and adrenal glands. In CT angiography of abdominal aortic aneurysms, dual energy CT techniques can be used to remove bones from the datasets, and virtual unenhanced images allow differentiation of contrast agent from calcifying thrombus in patients with endovascular stents. This review describes potential applications, practical guidelines, and limitations of dual energy CT in the abdomen.
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paper_title: A Parametric Level Set Approach to Simultaneous Object Identification and Background Reconstruction for Dual Energy Computed Tomography

paper_content:

Dual energy computerized tomography has gained great interest because of its ability to characterize the chemical composition of a material rather than simply providing relative attenuation images as in conventional tomography. The purpose of this paper is to introduce a novel polychromatic dual energy processing algorithm with an emphasis on detection and characterization of piecewise constant objects embedded in an unknown, cluttered background. Physical properties of the objects, specifically the Compton scattering and photoelectric absorption coefficients, are assumed to be known with some level of uncertainty. Our approach is based on a level-set representation of the characteristic function of the object and encompasses a number of regularization techniques for addressing both the prior information we have concerning the physical properties of the object as well as fundamental, physics-based limitations associated with our ability to jointly recover the Compton scattering and photoelectric absorption properties of the scene. In the absence of an object with appropriate physical properties, our approach returns a null characteristic function and thus can be viewed as simultaneously solving the detection and characterization problems. Unlike the vast majority of methods which define the level set function non-parametrically, i.e., as a dense set of pixel values), we define our level set parametrically via radial basis functions (RBF's) and employ a Gauss-Newton type algorithm for cost minimization. Numerical results show that the algorithm successfully detects objects of interest, finds their shape and location, and gives a adequate reconstruction of the background.
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paper_title: Compton scatter effects in CT reconstructions.

paper_content:

Compton scatter of x-ray quanta is the dominant attenuation mechanism in medical diagnostic imaging. In this paper, it is shown that the scatter-to-primary ratio determines the nature and intensity of scatter artifacts in computed tomography (CT) reconstructions and that this ratio, while significantly lower than in radiographic and fluoroscopic examinations, can still be significant in CT. It is found that high spatial-frequency artifacts can arise even though the detected scatter intensity has little or no high-frequency modulation. Reconstructions from x-ray data are presented which show "cupping" as well as dark streaks connecting high-attenuation regions. Correction of the data from measurements of scatter eliminates these artifacts. It is, moreover, observed that the intensity of the artifacts is often diminished when a beam-shaping attenuator is employed. Calculations of scatter intensity are developed from a model which includes single-event and two-event scatter. This analysis is in good agreement with measurements on round water phantoms. Extension to other detector geometries shows, not unexpectedly, that detectors with poorer collimation yield larger scatter artifacts.
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paper_title: Split Xenon Detector for Tomochemistry in Computed Tomography

paper_content:

The design of a split high-pressure xenon detector array for tomochemistry in computed tomography (CT) is described. Each detector produces a signal from the front primarily due to low energy photons and a signal from the back primarily due to high energy photons. Two methods are described whereby these signals are used to determine the photoelectric and Compton coefficients. From these, the electron density and average atomic number can be determined for each pixel in the image. These methods were tested by computer simulations of scans of a simple phantom, and the resulting Compton and photoelectric images are presented and compared with a conventional CT image. It was found that electron density and atomic number can be determined to an accuracy of better than 4%. The sensitivity to noise was studied, and it was found that the standard deviation of the mean of a 5 X 5 pixel region in the conventional image is about a factor of 3 lower than in the same region in the Compton image and about a factor of 40 lower than in the photoelectric image.
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paper_title: A method for selective tissue and bone visualization using dual energy scanned projection radiography

paper_content:

Information contained in the x-ray energy spectrum can be used to produce selective radiographic images of bone or soft tissue. A method has been devised to separate bone and soft tissue based upon differences in photoelectric absorption and Compton scattering using an appropriate combination of images obtained with radiographic exposures at 70 KVP and 140 KVP. Since photoelectric absorption is highly dependent upon atomic number, high atomic number materials such as calcium can be easily separated from water density substances. Using a prototype system for line-scanned radiography, selective subtraction of bone or soft-tissue has been implemented. Because this method uses a conventional broad-spectrum x-ray source, it was necessary to develop a nonlinear polynomial approximation to estimate tissue and bone thickness. The model was verified with phantom studies using water and aluminum. The application of this dual-energy bone and soft-tissue separation to chest radiography is demonstrated. This method allows accurate estimation of tissue and bone thickness and should find application to chest radiography for improved lesion detection and for bone mineral assessment.
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paper_title: A fast dual‐energy computational method using isotransmission lines and table lookup

paper_content:

An efficient method has been developed to speed up the computational time required for tissue separation after the dual‐energy scan in computerized tomography and digital radiography. This method consists of two steps. First, an isotransmission line method is used to replace the iterative algorithm for solving the two nonlinear equations, which is computationally noisy and sensitive. Second, two tables, one for the aluminum component and the other for the plastic component, are derived from calibration data in order to replace calculation by a table lookup. Results from this method are compared with the conventional method.
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paper_title: Dual energy computed tomography for explosive detection

paper_content:

Single energy computed tomography (CT) scanners use measurements of densities to detect explosives in luggage. It is desirable to apply dual energy techniques to these CT scanners to obtain atomic number measurements to reduce false alarm rates. However, the direct application of existing dual energy techniques has practical problems, such as, approximation errors and lack of boundary constraints in dual energy decomposition, image artifacts, and x-ray spectral drifts. In this paper, we present methods to reduce these problems. The methods include constrained dual energy decomposition, adaptive scatter correction, nonlinear filtering of decomposed projections, and real-time image-based correction for x-ray spectral drifts. We demonstrate the effectiveness of the methods using simulated data and real data obtained from a commercial dual energy CT scanner.
---
paper_title: Christensen's physics of diagnostic radiology

paper_content:

The fourth edition of this text provides a clear understanding of the physics principles essential to getting maximum diagnostic value from the full range of current and emerging imaging technologies. Updated material is added in areas such as x-ray generators (solid-state devices), xerography (liquid toner), CT scanners (fast-imaging technology) and ultrasound (color Doppler).
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paper_title: Evaluation of a prototype dual-energy computed tomographic apparatus. I. Phantom studies

paper_content:

We report the evaluation of a prototype dual-energy implementation using rapid kVp switching on a clinical computed tomographic scanner. The method employs prereconstruction basis material decomposition of the dual-energy projection data. Each dual-energy scan can be processed into conventional single-kVp images, basis material density images, and monoenergetic images. Phantom studies were carried out to qualitatively and quantitatively evaluate and validate the approach.
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paper_title: A Parametric Level Set Approach to Simultaneous Object Identification and Background Reconstruction for Dual Energy Computed Tomography

paper_content:

Dual energy computerized tomography has gained great interest because of its ability to characterize the chemical composition of a material rather than simply providing relative attenuation images as in conventional tomography. The purpose of this paper is to introduce a novel polychromatic dual energy processing algorithm with an emphasis on detection and characterization of piecewise constant objects embedded in an unknown, cluttered background. Physical properties of the objects, specifically the Compton scattering and photoelectric absorption coefficients, are assumed to be known with some level of uncertainty. Our approach is based on a level-set representation of the characteristic function of the object and encompasses a number of regularization techniques for addressing both the prior information we have concerning the physical properties of the object as well as fundamental, physics-based limitations associated with our ability to jointly recover the Compton scattering and photoelectric absorption properties of the scene. In the absence of an object with appropriate physical properties, our approach returns a null characteristic function and thus can be viewed as simultaneously solving the detection and characterization problems. Unlike the vast majority of methods which define the level set function non-parametrically, i.e., as a dense set of pixel values), we define our level set parametrically via radial basis functions (RBF's) and employ a Gauss-Newton type algorithm for cost minimization. Numerical results show that the algorithm successfully detects objects of interest, finds their shape and location, and gives a adequate reconstruction of the background.
---
paper_title: A Parametric Level Set Approach to Simultaneous Object Identification and Background Reconstruction for Dual Energy Computed Tomography

paper_content:

Dual energy computerized tomography has gained great interest because of its ability to characterize the chemical composition of a material rather than simply providing relative attenuation images as in conventional tomography. The purpose of this paper is to introduce a novel polychromatic dual energy processing algorithm with an emphasis on detection and characterization of piecewise constant objects embedded in an unknown, cluttered background. Physical properties of the objects, specifically the Compton scattering and photoelectric absorption coefficients, are assumed to be known with some level of uncertainty. Our approach is based on a level-set representation of the characteristic function of the object and encompasses a number of regularization techniques for addressing both the prior information we have concerning the physical properties of the object as well as fundamental, physics-based limitations associated with our ability to jointly recover the Compton scattering and photoelectric absorption properties of the scene. In the absence of an object with appropriate physical properties, our approach returns a null characteristic function and thus can be viewed as simultaneously solving the detection and characterization problems. Unlike the vast majority of methods which define the level set function non-parametrically, i.e., as a dense set of pixel values), we define our level set parametrically via radial basis functions (RBF's) and employ a Gauss-Newton type algorithm for cost minimization. Numerical results show that the algorithm successfully detects objects of interest, finds their shape and location, and gives a adequate reconstruction of the background.
---
paper_title: Explosives Detection By Dual-Energy Computed Tomography (CT)

paper_content:

Through the use of dual-energy CT, it is possible to obtain a two-dimensional mapping of average electron density and average atomic number within the imaged plane. Preliminary results obtained with medical scanners indicate that these data may provide a reliable criterion for differentiating between explosives and innocent materials contained within a package or suitcase without resorting to imaging, pattern recognition, or operator interpretation. The results of a preliminary systems study indicate that a practical system based on this principle of detection is feasible. Currently, there is a program for the development of a breadboard dual-energy Cm explosives detection system tailored to meet the performance characteristics reauired for a checkpoint operational scenario.
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paper_title: Classification-aware dimensionality reduction methods for explosives detection using multi-energy x-ray computed tomography

paper_content:

Multi-Energy X-ray Computed Tomography (MECT) is a non-destructive scanning technology in which multiple energyselective ::: measurements of the X-ray attenuation can be obtained. This provides more information about the chemical ::: composition of the scanned materials than single-energy technologies and potential for more reliable detection of explosives. ::: We study the problem of discriminating between explosives and non-explosives using low-dimensional features ::: extracted from the high-dimensional attenuation versus energy curves of materials. We study various linear dimensionality ::: reduction methods and demonstrate that the detection performance can be improved by using more than two features and ::: when using features different than the standard photoelectric and Compton coefficients. This suggests the potential for ::: improved detection performance relative to conventional dual-energy X-ray systems.
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paper_title: Aspects of image recognition in Vivid Technologies' dual-energy x-ray system for explosives detection

paper_content:

The Vivid Rapid Explosives Detection Systems is a true dual energy x-ray machine employing precision x-ray data acquisition in combination with unique algorithms and massive computation capability. Data from the system's 960 detectors is digitally stored and processed by powerful supermicro-computers organized as an expandable array of parallel processors. The algorithms operate on the dual energy attenuation image data to recognize and define objects in the milieu of the baggage contents. Each object is then systematically examined for a match to a specific effective atomic number, density, and mass threshold. Material properties are determined by comparing the relative attenuations of the 75 kVp and 150 kVp beams and electronically separating the object from its local background. Other heuristic algorithms search for specific configurations and provide additional information. The machine automatically detects explosive materials and identifies bomb components in luggage with high specificity and throughput, X-ray dose is comparable to that of current airport x-ray machines. The machine is also configured to find heroin, cocaine, and US currency by selecting appropriate settings on-site. Since January 1992, production units have been operationally deployed at U.S. and European airports for improved screening of checked baggage.
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paper_title: A learning-based approach to explosives detection using Multi-Energy X-Ray Computed Tomography

paper_content:

In this paper we consider the task of classifying materials into explosives and non-explosives according to features obtainable from Multi-Energy X-ray Computed Tomography (MECT) measurements. The discriminative ability of MECT derives from its sensitivity to the attenuation versus energy curves of materials. Thus we focus on the fundamental information available in these curves and features extracted from them. We study the dimensionality and span of these curves for a set of explosive and non-explosive compounds and show that their space is larger than two-dimensional, as is typically assumed. In addition, we build support vector machine classifiers with different feature sets and find superior classification performance when using more than two features and when using features different than the standard photoelectric and Compton coefficients. These results suggest the potential for improved detection performance relative to conventional dual-energy X-ray systems.
---
paper_title: Dual energy computed tomography for explosive detection

paper_content:

Single energy computed tomography (CT) scanners use measurements of densities to detect explosives in luggage. It is desirable to apply dual energy techniques to these CT scanners to obtain atomic number measurements to reduce false alarm rates. However, the direct application of existing dual energy techniques has practical problems, such as, approximation errors and lack of boundary constraints in dual energy decomposition, image artifacts, and x-ray spectral drifts. In this paper, we present methods to reduce these problems. The methods include constrained dual energy decomposition, adaptive scatter correction, nonlinear filtering of decomposed projections, and real-time image-based correction for x-ray spectral drifts. We demonstrate the effectiveness of the methods using simulated data and real data obtained from a commercial dual energy CT scanner.
---
paper_title: Investigating existing medical CT segmentation techniques within automated baggage and package inspection

paper_content:

3D Computed Tomography (CT) image segmentation is already well established tool in medical research and in routine daily clinical practice. However, such techniques have not been used in the context of 3D CT image segmentation for baggage and package security screening using CT imagery. CT systems are increasingly used in airports for security baggage examination. We propose in this contribution an investigation of the current 3D CT medical image segmentation methods for use in this new domain. Experimental results of 3D segmentation on real CT baggage security imagery using a range of techniques are presented and discussed.
---
paper_title: Shape retrieval using 3d zernike descriptors

paper_content:

Abstract We advocate the usage of 3D Zernike invariants as descriptors for 3D shape retrieval. The basis polynomials of this representation facilitate computation of invariants under rotation, translation and scaling. Some theoretical results have already been summarized in the past from the aspect of pattern recognition and shape analysis. We provide practical analysis of these invariants along with algorithms and computational details. Furthermore, we give a detailed discussion on influence of the algorithm parameters like the conversion into a volumetric function, number of utilized coefficients, etc. As is revealed by our study, the 3D Zernike descriptors are natural extensions of recently introduced spherical harmonics based descriptors. We conduct a comparison of 3D Zernike descriptors against these regarding computational aspects and shape retrieval performance using several quality measures and based on experiments on the Princeton Shape Benchmark.
---
paper_title: A distance driven method for metal artefact reduction in computed tomography

paper_content:

This paper presents an extension to a recent intensity-limiting sino-gram completion-based Metal Artefact Reduction (MAR) algorithm for Computed Tomography (CT) images containing multiple metal objects. A novel weighting scheme is introduced, whereby the intensities of the MAR-corrected pixels are modified based on their spatial locations relative to the metal objects. Pixels falling within the straight-line regions connecting multiple metal objects are subjected to less intensive intensity-limiting, thereby compensating for the characteristic dark bands occurring in these regions. Extensive experimentation is performed on a state-of-the-art numerical simulation, a clinical CT data set and a baggage security CT data set. Comprehensive performance analysis, using reference and reference-free error metrics, Bland-Altman plots and visual comparisons, demonstrate an improvement in the restoration of the underestimated intensities occurring in the regions connecting multiple metal objects.
---
paper_title: Sampling strategies for bag-of-features image classification

paper_content:

Bag-of-features representations have recently become popular for content based image classification owing to their simplicity and good performance. They evolved from texton methods in texture analysis. The basic idea is to treat images as loose collections of independent patches, sampling a representative set of patches from the image, evaluating a visual descriptor vector for each patch independently, and using the resulting distribution of samples in descriptor space as a characterization of the image. The four main implementation choices are thus how to sample patches, how to describe them, how to characterize the resulting distributions and how to classify images based on the result. We concentrate on the first issue, showing experimentally that for a representative selection of commonly used test databases and for moderate to large numbers of samples, random sampling gives equal or better classifiers than the sophisticated multiscale interest operators that are in common use. Although interest operators work well for small numbers of samples, the single most important factor governing performance is the number of patches sampled from the test image and ultimately interest operators can not, provide enough patches to compete. We also study the influence of other factors including codebook size and creation method, histogram normalization method and minimum scale for feature extraction.
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paper_title: Decision Forests For Computer Vision And Medical Image Analysis

paper_content:

Thank you very much for downloading decision forests for computer vision and medical image analysis. As you may know, people have search hundreds times for their favorite readings like this decision forests for computer vision and medical image analysis, but end up in harmful downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they cope with some malicious bugs inside their computer.
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paper_title: Object classification in 3D baggage security computed tomography imagery using visual codebooks

paper_content:

We investigate the performance of a Bag of (Visual) Words (BoW) object classification model as an approach for automated threat object detection within 3D Computed Tomography (CT) imagery from a baggage security context. This poses a novel and unique challenge for rigid object classification within complex and cluttered volumetric imagery. Within this context it extends the BoW model to 3D transmission imagery (X-ray CT) from its conventional application in 2D reflectance (photographic) imagery. We explore combinations of four 3D feature descriptors (Density Histogram (DH), Density Gradient Histogram (DGH), Scale Invariant Feature Transform (SIFT) and Rotation Invariant Feature Transform (RIFT)), three codebook assignment methodologies (hard, kernel and uncertainty) and seven codebook sizes. Optimal performance is achieved using the DH and DGH descriptors in conjunction with an uncertainty assignment methodology. Successful detection rates in excess of 97% for handguns and 89% for bottles and false-positive rates of approximately 2-3% are achieved. We demonstrate that the underlying imaging modality and the irrelevance of illumination and scale invariance within the transmission imagery context considered here result in the favourable performance of simpler density histogram descriptors (DH, DGH) over 3D extensions of the well-established SIFT and RIFT feature descriptor approaches. HighlightsNovel investigation of BoW model for object classification in 3D baggage CT scans.Four descriptor types and three codebook assignment methodologies compared.Simple density-based descriptors outperform more complex descriptors.Optimal true and false positive rates for classification of handguns and bottles.Low resolution, noise and artefacts limit performance.
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paper_title: A sparse texture representation using affine-invariant regions

paper_content:

This paper introduces a texture representation suitable for recognizing images of textured surfaces under a wide range of transformations, including viewpoint changes and nonrigid deformations. At the feature extraction stage, a sparse set of affine-invariant local patches is extracted from the image. This spatial selection process permits the computation of characteristic scale and neighborhood shape for every texture element. The proposed texture representation is evaluated in retrieval and classification tasks using the entire Brodatz database and a collection of photographs of textured surfaces taken from different viewpoints.
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paper_title: COSMOS-a representation scheme for free-form surfaces

paper_content:

We address the problem of representing and recognizing arbitrarily curved 3D rigid objects when: the objects may vary in shape and complexity, and no restrictive assumptions are made about the types of surfaces on the object. We propose a new and general surface representation scheme for recognizing objects with free form (sculpted) surfaces from range data. In this scheme, an object is described concisely in terms of maximal surface patches of constant shape index. These maximal patches are mapped onto the unit sphere via their orientations, and aggregated via shape spectral functions. Properties such as surface area, curvedness and connectivity that capture local and global information are also built into the representation. The scheme yields not only a meaningful and rich surface description useful for the recoverability of the object, but also a set of powerful indexing primitives for object matching. We demonstrate the generality and the effectiveness of our scheme using real range images of complex objects. We also present results on the categorization of object views based on a novel shape spectral matching technique. >
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paper_title: A sparse texture representation using local affine regions

paper_content:

This paper introduces a texture representation suitable for recognizing images of textured surfaces under a wide range of transformations, including viewpoint changes and nonrigid deformations. At the feature extraction stage, a sparse set of affine Harris and Laplacian regions is found in the image. Each of these regions can be thought of as a texture element having a characteristic elliptic shape and a distinctive appearance pattern. This pattern is captured in an affine-invariant fashion via a process of shape normalization followed by the computation of two novel descriptors, the spin image and the RIFT descriptor. When affine invariance is not required, the original elliptical shape servee as an additional discriminative feature for texture recognition. The proposed approach is evaluated in retrieval and classification tasks using the entire Brodatz database and a publicly available collection of 1,000 photographs of textured surfaces taken from different viewpoints.
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paper_title: A 3D extension to cortex like mechanisms for 3D object class recognition

paper_content:

We introduce a novel 3D extension to the hierarchical visual cortex model used for prior work in 2D object recognition. Prior work on the use of the visual cortex standard model for the explicit task of object class recognition has solely concentrated on 2D imagery. In this paper we discuss the explicit 3D extension of each layer in this visual cortex model hierarchy for use in object recognition in 3D volumetric imagery. We apply this extended methodology to the automatic detection of a class of threat items in Computed Tomography (CT) security baggage imagery. The CT imagery suffers from poor resolution and a large number of artefacts generated through the presence of metallic objects. In our examination of recognition performance we make a comparison to a codebook approach derived from a 3D SIFT descriptor and demonstrate that the visual cortex method out-performs in this imagery. Recognition rates in excess of 95% with minimal false positive rates are demonstrated in the detection of a range of threat items.
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paper_title: A User's Guide to Support Vector Machines

paper_content:

The Support Vector Machine (SVM) is a widely used classifier in bioinformatics. Obtaining the best results with SVMs requires an understanding of their workings and the various ways a user can influence their accuracy. We provide the user with a basic understanding of the theory behind SVMs and focus on their use in practice. We describe the effect of the SVM parameters on the resulting classifier, how to select good values for those parameters, data normalization, factors that affect training time, and software for training SVMs.
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paper_title: Neural network classification: a Bayesian interpretation.

paper_content:

The relationship between minimizing a mean squared error and finding the optimal Bayesian classifier is reviewed. This provides a theoretical interpretation for the process by which neural networks are used in classification. A number of confidence measures are proposed to evaluate the performance of the neural network classifier within a statistical framework.
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paper_title: The Nature of Statistical Learning Theory

paper_content:

Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?.
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paper_title: Video Google: a text retrieval approach to object matching in videos

paper_content:

We describe an approach to object and scene retrieval which searches for and localizes all the occurrences of a user outlined object in a video. The object is represented by a set of viewpoint invariant region descriptors so that recognition can proceed successfully despite changes in viewpoint, illumination and partial occlusion. The temporal continuity of the video within a shot is used to track the regions in order to reject unstable regions and reduce the effects of noise in the descriptors. The analogy with text retrieval is in the implementation where matches on descriptors are pre-computed (using vector quantization), and inverted file systems and document rankings are used. The result is that retrieved is immediate, returning a ranked list of key frames/shots in the manner of Google. The method is illustrated for matching in two full length feature films.
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paper_title: A classifier based approach for the detection of potential threats in CT based Baggage Screening

paper_content:

Recent years have seen increased use of Computed Tomography (CT) based Unaccompanied Baggage and Package Screening (UBPS) systems for luggage examination to ensure air travel security. In this paper we present a research work on developing a system for automatic detection of potential threat items in cluttered 3D CT imagery originating from UBPS systems by combining 3D medical image segmentation techniques with 3D shape classification and retrieval methods.
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paper_title: An experimental survey of metal artefact reduction in computed tomography

paper_content:

We present a survey of techniques for the reduction of streaking artefacts caused by metallic objects in X-ray Computed Tomography (CT) images. A comprehensive review of the existing state-of-theart Metal Artefact Reduction (MAR) techniques, drawn predominantly from the medical CT literature, is supported by an experimental comparison of twelve MAR techniques. The experimentation is grounded in an evaluation based on a standard scientific comparison protocol for MAR methods, using a software generated medical phantom image as well as a clinical CT scan. The experimentation is extended by considering novel applications of CT imagery consisting of metal objects in non-tissue surroundings acquired from the aviation security screening domain. We address the shortage of thorough performance analyses in the existing MAR literature by conducting a qualitative as well as quantitative comparative evaluation of the selected techniques. We find that the difficulty in generating accurate priors to be the predominant factor limiting the effectiveness of the state-of-the-art medical MAR techniques when applied to non-medical CT imagery. This study thus extends previous works by: comparing several state-of-the-art MAR techniques; considering both medical and non-medical applications and performing a thorough performance analysis, considering both image quality as well as computational demands.
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paper_title: 3D threat image projection

paper_content:

Automated Explosive Detection Systems utilizing Computed Tomography perform a series X-ray scans of passenger bags being checked in at the airport, and produce various 2-D projection images and 3-D volumetric images of the bag. The determination as to whether the passenger bag contains an explosive and needs to be searched manually is performed through trained Transportation Security Administration screeners following an approved protocol. In order to keep the screeners vigilant with regards to screening quality, the Transportation Security Administration has mandated the use of Threat Image Projection on 2-D projection X-ray screening equipment used at all US airports. These algorithms insert visual artificial threats into images of the normal passenger bags in order to test the screeners with regards to their screening efficiency and their screening quality at determining threats. This technology for 2-D X-ray system is proven and is widespread amongst multiple manufacturers of X-ray projection systems. Until now, Threat Image Projection has been unsuccessful at being introduced into 3-D Automated Explosive Detection Systems for numerous reasons. The failure of these prior attempts are mainly due to imaging queues that the screeners ::: pickup on, and therefore make it easy for the screeners to discern the presence of the threat image and thus defeating the intended purpose. This paper presents a novel approach for 3-D Threat Image Projection for 3-D Automated Explosive Detection Systems. ::: The method presented here is a projection based approach where both the threat object and the bag remain in projection sinogram space. Novel approaches have been developed for projection based object segmentation, projection based streak reduction used for threat object isolation along with scan orientation independence and projection based streak generation for an overall realistic 3-D image. ::: The algorithms are prototyped in MatLab and C++ and demonstrate non discernible 3-D threat image insertion into various luggage, and non discernable streak patterns for 3-D images when compared to actual scanned images.
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paper_title: The Radon Transform And Some Of Its Applications

paper_content:

the radon transform and some of its applications is available in our book collection an online access to it is set as public so you can get it instantly. Our digital library spans in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the the radon transform and some of its applications is universally compatible with any devices to read.
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paper_title: Use of threat image projection (TIP) to enhance security performance

paper_content:

Threat Image Projection (TIP) is a software system that is used at airports to project images of threat items amongst the passenger baggage being screened by X-ray. The use of TIP is becoming more widespread and is increasingly being included as part of security regulation. This is due to its purported benefits of improved attention and vigilance, and increased exposure to threat items that are linked to improvements in threat detection performance. Further, the data collected by the TIP system can be used to assess individual performance, provide feedback to screeners, and tailor training to specific performance weaknesses; which can generate further performance improvements. However, TIP will only be successful in enhancing security performance if it is used and managed effectively.
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paper_title: Using threat image projection data for assessing individual screener performance.

paper_content:

Threat image projection (TIP) is a technology of current x-ray machines that allows exposing screeners to artificial but realistic x-ray images during the routine baggage x-ray screening operation. If a screener does not detect a TIP within a specified amount of time, a feedback message appears indicating that a projected image was missed. Feedback messages are also shown when a TIP image is detected or in the case of a non-TIP alarm, i.e. when the screener indicated that there was threat but in fact no TIP was shown. TIP data is an interesting source for quality control, risk analysis and assessment of individual screener performance. In two studies we examined the conditions for using TIP data for the latter purpose. Our results strongly suggest using aggregated data in order to have a large enough data sample as the basis for statistical analysis. Second, an appropriate TIP library containing a large number of threat items, which are representative for the prohibited items to be detected is recommended. Furthermore, consideration should be given to image-based factors such as general threat item difficulty, viewpoint difficulty, superposition and bag complexity. Different methods to cope with these issues are discussed in order to achieve reliable, valid and standardized measurements of individual screener performance using TIP.
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paper_title: Fully automatic 3D Threat Image Projection: Application to densely cluttered 3D Computed Tomography baggage images

paper_content:

In this paper, we describe a Threat Image Projection (TIP) method designed for 3D Computed Tomography (CT) screening systems. The novel methodology automatically determines a valid 3D location in the passenger 3D CT baggage image into which a fictional threat 3D image can be inserted without violating the bag content. According to the scan orientation, the passenger bag content and the material of the inserted threat appropriate CT artefacts are generated using a Radon transform in order to make the insertion realistic. Densely cluttered 3D CT baggage images are used to validate our method. Experimental results confirm that our method is able to reliably insert threat items in challenging 3D images without providing any perceptible visual cue to human screeners.
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paper_title: CT baggage image enhancement using a combination of alpha-weighted mean separation and histogram equalization

paper_content:

Baggage scanning systems are used for detecting the presence of explosives and other prohibited items in baggage at ::: security checkpoints in airports. However, the CT baggage images contain projection noise and are of low resolution. ::: This paper introduces a new enhancement algorithm combining alpha-weighted mean separation and histogram ::: equalization to enhance the CT baggage images while removing the background projection noise. A new enhancement ::: measure is introduced for quantitative assessment of image enhancement. Simulations and a comparative analysis are ::: given to demonstrate the new algorithm's performance.
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paper_title: A distance driven method for metal artefact reduction in computed tomography

paper_content:

This paper presents an extension to a recent intensity-limiting sino-gram completion-based Metal Artefact Reduction (MAR) algorithm for Computed Tomography (CT) images containing multiple metal objects. A novel weighting scheme is introduced, whereby the intensities of the MAR-corrected pixels are modified based on their spatial locations relative to the metal objects. Pixels falling within the straight-line regions connecting multiple metal objects are subjected to less intensive intensity-limiting, thereby compensating for the characteristic dark bands occurring in these regions. Extensive experimentation is performed on a state-of-the-art numerical simulation, a clinical CT data set and a baggage security CT data set. Comprehensive performance analysis, using reference and reference-free error metrics, Bland-Altman plots and visual comparisons, demonstrate an improvement in the restoration of the underestimated intensities occurring in the regions connecting multiple metal objects.
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paper_title: Classification-aware dimensionality reduction methods for explosives detection using multi-energy x-ray computed tomography

paper_content:

Multi-Energy X-ray Computed Tomography (MECT) is a non-destructive scanning technology in which multiple energyselective ::: measurements of the X-ray attenuation can be obtained. This provides more information about the chemical ::: composition of the scanned materials than single-energy technologies and potential for more reliable detection of explosives. ::: We study the problem of discriminating between explosives and non-explosives using low-dimensional features ::: extracted from the high-dimensional attenuation versus energy curves of materials. We study various linear dimensionality ::: reduction methods and demonstrate that the detection performance can be improved by using more than two features and ::: when using features different than the standard photoelectric and Compton coefficients. This suggests the potential for ::: improved detection performance relative to conventional dual-energy X-ray systems.
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paper_title: A learning-based approach to explosives detection using Multi-Energy X-Ray Computed Tomography

paper_content:

In this paper we consider the task of classifying materials into explosives and non-explosives according to features obtainable from Multi-Energy X-ray Computed Tomography (MECT) measurements. The discriminative ability of MECT derives from its sensitivity to the attenuation versus energy curves of materials. Thus we focus on the fundamental information available in these curves and features extracted from them. We study the dimensionality and span of these curves for a set of explosive and non-explosive compounds and show that their space is larger than two-dimensional, as is typically assumed. In addition, we build support vector machine classifiers with different feature sets and find superior classification performance when using more than two features and when using features different than the standard photoelectric and Compton coefficients. These results suggest the potential for improved detection performance relative to conventional dual-energy X-ray systems.
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paper_title: METAL ARTIFACT REDUCTION FOR CT-BASED LUGGAGE SCREENING

paper_content:

In aviation security, checked luggage is screened by computed tomography (CT) scanning, followed by automatic target recognition from the CT images. Metal objects in the bags cause image artifacts that degrade object representation, leading to increased false alarms. We develop a new method, which isolates and reduces artifacts in an intermediate image, based on a numerical optimization that de-emphasizes metal and has a novel constraint for beam hardening and scatter. Results on test bags showed excellent artifact reduction, even for multiple metal objects.
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paper_title: A 3D extension to cortex like mechanisms for 3D object class recognition

paper_content:

We introduce a novel 3D extension to the hierarchical visual cortex model used for prior work in 2D object recognition. Prior work on the use of the visual cortex standard model for the explicit task of object class recognition has solely concentrated on 2D imagery. In this paper we discuss the explicit 3D extension of each layer in this visual cortex model hierarchy for use in object recognition in 3D volumetric imagery. We apply this extended methodology to the automatic detection of a class of threat items in Computed Tomography (CT) security baggage imagery. The CT imagery suffers from poor resolution and a large number of artefacts generated through the presence of metallic objects. In our examination of recognition performance we make a comparison to a codebook approach derived from a 3D SIFT descriptor and demonstrate that the visual cortex method out-performs in this imagery. Recognition rates in excess of 95% with minimal false positive rates are demonstrated in the detection of a range of threat items.
---
paper_title: Dual energy computed tomography for explosive detection

paper_content:

Single energy computed tomography (CT) scanners use measurements of densities to detect explosives in luggage. It is desirable to apply dual energy techniques to these CT scanners to obtain atomic number measurements to reduce false alarm rates. However, the direct application of existing dual energy techniques has practical problems, such as, approximation errors and lack of boundary constraints in dual energy decomposition, image artifacts, and x-ray spectral drifts. In this paper, we present methods to reduce these problems. The methods include constrained dual energy decomposition, adaptive scatter correction, nonlinear filtering of decomposed projections, and real-time image-based correction for x-ray spectral drifts. We demonstrate the effectiveness of the methods using simulated data and real data obtained from a commercial dual energy CT scanner.
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paper_title: Fully automatic 3D Threat Image Projection: Application to densely cluttered 3D Computed Tomography baggage images

paper_content:

In this paper, we describe a Threat Image Projection (TIP) method designed for 3D Computed Tomography (CT) screening systems. The novel methodology automatically determines a valid 3D location in the passenger 3D CT baggage image into which a fictional threat 3D image can be inserted without violating the bag content. According to the scan orientation, the passenger bag content and the material of the inserted threat appropriate CT artefacts are generated using a Radon transform in order to make the insertion realistic. Densely cluttered 3D CT baggage images are used to validate our method. Experimental results confirm that our method is able to reliably insert threat items in challenging 3D images without providing any perceptible visual cue to human screeners.
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paper_title: Automatic segmentation of unknown objects, with application to baggage security

paper_content:

Computed tomography (CT) is used widely to image patients for medical diagnosis and to scan baggage for threatening materials. Automated reading of these images can be used to reduce the costs of a human operator, extract quantitative information from the images or support the judgements of a human operator. Object quantification requires an image segmentation to make measurements about object size, material composition and morphology. Medical applications mostly require the segmentation of prespecified objects, such as specific organs or lesions, which allows the use of customized algorithms that take advantage of training data to provide orientation and anatomical context of the segmentation targets. In contrast, baggage screening requires the segmentation algorithm to provide segmentation of an unspecified number of objects with enormous variability in size, shape, appearance and spatial context. Furthermore, security systems demand 3D segmentation algorithms that can quickly and reliably detect threats. To address this problem, we present a segmentation algorithm for 3D CT images that makes no assumptions on the number of objects in the image or on the composition of these objects. The algorithm features a new Automatic QUality Measure (AQUA) model that measures the segmentation confidence for any single object (from any segmentation method) and uses this confidence measure to both control splitting and to optimize the segmentation parameters at runtime for each dataset. The algorithm is tested on 27 bags that were packed with a large variety of different objects.
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Title: A Review of Automated Image Understanding Within 3D Baggage Computed Tomography Security Screening

Section 1: Introduction
Description 1: Provide an overview of the importance of baggage screening in aviation security, the adoption of 3D X-ray CT in this domain, and the motivation for automated image understanding to improve efficiency and reduce manual inspections.

Section 2: Denoising
Description 2: Discuss the challenges and techniques of denoising complex volumetric baggage-CT imagery, highlighting key methods and their efficacy.

Section 3: Metal Artefact Reduction (MAR)
Description 3: Review the origins, impact, and methods for reducing metal artefacts in CT imagery, including simulation studies and the evaluation of existing techniques.

Section 4: Dual-Energy Computed Tomography (DECT)
Description 4: Explore the application of DECT in baggage screening, including data acquisition techniques, the challenges of dual-energy decomposition, and recent advancements in this field.

Section 5: Post-Reconstruction Techniques
Description 5: Examine post-reconstruction DECT techniques and their application in the medical imaging domain, and discuss their recent adoption in baggage-CT imagery.

Section 6: Pre-Reconstruction Techniques
Description 6: Describe pre-reconstruction DECT methods, including basis material decomposition and its challenges and benefits when applied to baggage-screening systems.

Section 7: Iterative-Reconstruction Techniques
Description 7: Detail iterative-reconstruction techniques for DECT and their advantages in overcoming artefacts and improving image quality, with particular focus on aviation security applications.

Section 8: Segmentation
Description 8: Review segmentation techniques for 3D objects in low-resolution, cluttered baggage-CT imagery, outlining the existing methods and their limitations.

Section 9: Classification
Description 9: Discuss the machine learning-based approaches for the automated classification of 3D objects in baggage-CT data, including feature-based models and their performance.

Section 10: Threat-Image Projection (TIP)
Description 10: Investigate TIP techniques for the automated insertion of realistic threat items into baggage imagery for operator training and performance monitoring.

Section 11: Conclusions
Description 11: Summarize the current state of automated image understanding in baggage-CT security screening, identify gaps between research and commercial application, and suggest directions for future research.

Section 12: Tables
Description 12: Provide tables summarizing methodologies, data types, evaluation measures, and conclusions for denoising and MAR (Table 1), 3D object segmentation (Table 3), and 3D object classification (Table 4).