Browsing by Author "Mouton, Andre"

Now showing 1 - 3 of 3
  • Thumbnail Image
    ItemOpen Access
    An experimental survey of metal artefact reduction in computed tomography
    (Elsevier Science B.V., Amsterdam, 2013-11-01T00:00:00Z) Mouton, Andre; Megherbi Bouallagui, Najla; Van Slambrouck, Katrien; Nuyts, Johan; Breckon, Toby P.
    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- the-art Metal Artefact Reduction (MAR) techniques, drawn almost exclusively from the medical CT literature, is supported by an experimental comparison grounded in an evaluation based on a standard scienti c comparison protocol for MAR methods using a software generated medical phan- tom image. This experimental comparison is further extended by considering novel applications of CT imagery consisting of isolated metal objects with no surrounding tissue, as is encountered in typical engineering and security screening CT applications. We nd that the performance of twelve state-of-the-art MAR techniques to be fairly consistent across the two domains and demonstrate the feasibility of a reference-free quantitative performance measure. The literature review and experi- mentation demonstrate several trends. In particular, the major limitations of state-of-the-art MAR techniques are a dependence on prior knowledge, a sensitivity to input parameters and a shortage of comprehensive performance analyses. 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 comprehensive quantitative analysis, taking into account image quality as well as computational requirements.
  • Thumbnail Image
    ItemOpen Access
    A novel intensity limiting approach to Metal Artefact Reduction in 3D CT baggage imagery
    (IEEE, 2013-02-21) Mouton, Andre; Megherbi, Najla; Flitton, Greg T.; Bizot, Suzanne; Breckon, Toby P.
    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.
  • Thumbnail Image
    ItemOpen Access
    On artefact reduction, segmentation and classification of 3D computed tomography imagery in baggage security screening
    (Cranfield University, 2014-03) Mouton, Andre; Breckon, Toby P.; Armitage, Carol
    This work considers novel image-processing and computer-vision techniques to advance the automated analysis of low-resolution, complex 3D volumetric Computed Tomography (CT) imagery obtained in the aviation-security-screening domain. Novel research is conducted in three key areas: image quality improvement, segmentation and classification. A sinogram-completion Metal Artefact Reduction (MAR) technique is presented. The presence of multiple metal objects in the scanning Field of View (FoV) is accounted for via a distance-driven weighting scheme. The technique is shown to perform comparably to the state-of-the-art medical MAR techniques in a quantitative and qualitative comparative evaluation. A materials-based technique is proposed for the segmentation of unknown objects from low-resolution, cluttered volumetric baggage-CT data. Initial coarse segmentations, generated using dual-energy techniques, are refined by partitioning at automatically-detected regions. Partitioning is guided by a novel random-forestbased quality metric (trained to recognise high-quality, single-object segments). A second segmentation-quality measure is presented for quantifying the quality of full segmentations. In a comparative evaluation, the proposed method is shown to produce similar-quality segmentations to the state-of-the-art at reduced processing times. A codebook model constructed using an Extremely Randomised Clustering (ERC) forest for feature encoding, a dense-feature-sampling strategy and a Support Vector Machine (SVM) classifier is presented. The model is shown to offer improvements in accuracy over the state-of-the-art 3D visual-cortex model at reduced processing times, particularly in the presence of noise and artefacts. The overall contribution of this work is a novel, fully-automated and effcient framework for the classification of objects in cluttered 3D baggage-CT imagery. It extends the current state-of-the-art by improving classification performance in the presence of noise and artefacts; by automating the previously-manual isolation of objects and by decreasing processing times by several orders of magnitude.