On artefact reduction, segmentation and classification of 3D computed tomography imagery in baggage security screening

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 efficient 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.