An experimental survey of metal artefact reduction in computed tomography

Mouton, André; Megherbi, Najla; Slambrouck, Katrien Van; Breckon, Toby P.

doi:10.3233/xst-130372

Cited by 64 publications

(61 citation statements)

References 87 publications

(259 reference statements)

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“…The proposed techniques were evaluated on the classification of two target objects (handguns and bottles) in cluttered 3D baggage-CT imagery obtained on a CT-80DR dualenergy baggage scanner manufactured by Reveal Imaging Inc., which produces volumes with low anisotropic resolutions (1.56x1.61x5mm). We considered only the high-energy (nominal tube voltage of 160kVp), filtered-back projection [25] reconstructed CT images. Target objects were scanned in random poses to obtain rotational invariance and were manually isolated prior to feature extraction ( Figure 2).…”

Section: Resultsmentioning

confidence: 99%

3D object classification in baggage computed tomography imagery using randomised clustering forests

Mouton

Breckon

Flitton

et al. 2014

2014 IEEE International Conference on Image Processing (ICIP)

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We investigate the feasibility of a codebook approach for the automated classification of threats in pre-segmented 3D baggage Computed Tomography (CT) security imagery. We compare the performance of five codebook models, using various combinations of sampling strategies, feature encoding techniques and classifiers, to the current state-of-the-art 3D visual cortex approach [1]. We demonstrate an improvement over the state-of-the-art both in terms of accuracy as well as processing time using a codebook constructed via randomised clustering forests [2], a dense feature sampling strategy and an SVM classifier. Correct classification rates in excess of 98% and false positive rates of less than 1%, in conjunction with a reduction of several orders of magnitude in processing time, make the proposed approach an attractive option for the automated classification of threats in security screening settings.

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Section: Resultsmentioning

confidence: 99%

3D object classification in baggage computed tomography imagery using randomised clustering forests

Mouton

Breckon

Flitton

et al. 2014

2014 IEEE International Conference on Image Processing (ICIP)

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“…Since the data used in our experiments were captured by a medical CT machine which suffers less from the metal artefacts, there is a risk of performance drop when the segmentation algorithm is applied to baggage data with significant artefacts. A potential solution to addressing this issue is to apply the metal artefact reduction algorithms [30,29] as a pre-process, though this could be marginal and yet require considerable additional computation [25].…”

Section: Discussionmentioning

confidence: 99%

An approach for adaptive automatic threat recognition within 3D computed tomography images for baggage security screening

Wang

Ismail

Breckon

2020

XST

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BACKGROUND: The screening of baggage using X-ray scanners is now routine in aviation security with automatic threat detection approaches, based on 3D X-ray computed tomography (CT) images, known as Automatic Threat Recognition (ATR) within the aviation security industry. These current strategies use pre-defined threat material signatures in contrast to adaptability towards new and emerging threat signatures. To address this issue, the concept of adaptive automatic threat recognition (AATR) was proposed in previous work.OBJECTIVE: In this paper, we present a solution to AATR based on such X-ray CT baggage scan imagery. This aims to address the issues of rapidly evolving threat signatures within the screening requirements. Ideally, the detection algorithms deployed within the security scanners should be readily adaptable to different situations with varying requirements of threat characteristics (e.g., threat material, physical properties of objects).METHODS: We tackle this issue using a novel adaptive machine learning methodology with our solution consisting of a multi-scale 3D CT image segmentation algorithm, a multiclass support vector machine (SVM) classifier for object material recognition and a strategy to enable the adaptability of our approach. Experiments are conducted on both open and sequestered 3D CT baggage image datasets specifically collected for the AATR study.RESULTS: Our proposed approach performs well on both recognition and adaptation. Overall our approach can achieve the probability of detection around 90% with a probability of false alarm below 20%. Figure 1: Threat objects in 2D X-ray images [2]. Left: threat object signatures of specific appearance (e.g., firearms) are easy to identify. Right: threat materials are more challenging to recognize in cluttered X-ray images.

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“…CT images that are used to construct 3D skull models can contain radiation artifacts caused by metallic dental implants that are very difficult to remove. Thus, 3D skull models segmented and constructed from CT images often contain metal artifacts (Figure ).…”

Section: Methodsmentioning

confidence: 99%

Flip‐avoiding interpolating surface registration for skull reconstruction

Xie

Leow

Lee

et al. 2018

Robotics Computer Surgery

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Skull reconstruction is an important and challenging task in craniofacial surgery planning, forensic investigation and anthropological studies. Existing methods typically reconstruct approximating surfaces that regard corresponding points on the target skull as soft constraints, thus incurring non-zero error even for non-defective parts and high overall reconstruction error. This paper proposes a novel geometric reconstruction method that non-rigidly registers an interpolating reference surface that regards corresponding target points as hard constraints, thus achieving low reconstruction error. To overcome the shortcoming of interpolating a surface, a flip-avoiding method is used to detect and exclude conflicting hard constraints that would otherwise cause surface patches to flip and self-intersect. Comprehensive test results show that our method is more accurate and robust than existing skull reconstruction methods. By incorporating symmetry constraints, it can produce more symmetric and normal results than other methods in reconstructing defective skulls with a large number of defects. It is robust against severe outliers such as radiation artifacts in computed tomography due to dental implants. In addition, test results also show that our method outperforms thin-plate spline for model resampling, which enables the active shape model to yield more accurate reconstruction results. As the reconstruction accuracy of defective parts varies with the use of different reference models, we also study the implication of reference model selection for skull reconstruction.

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An experimental survey of metal artefact reduction in computed tomography

Cited by 64 publications

References 87 publications

3D object classification in baggage computed tomography imagery using randomised clustering forests

3D object classification in baggage computed tomography imagery using randomised clustering forests

An approach for adaptive automatic threat recognition within 3D computed tomography images for baggage security screening

Flip‐avoiding interpolating surface registration for skull reconstruction

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