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Materials-Based 3D Segmentation of Unknown Objects from Dual-Energy Computed Tomography Imagery in Baggage Security Screening

Mouton, A.; Breckon, T.P.

Materials-Based 3D Segmentation of Unknown Objects from Dual-Energy Computed Tomography Imagery in Baggage Security Screening Thumbnail


Authors

A. Mouton



Abstract

We present a novel technique for the 3D segmentation of unknown objects from cluttered dual-energy Computed Tomography (CT) data obtained in the baggage security-screening domain. Initial materials-based coarse segmentations, generated using the Dual-Energy Index (DEI), are refined by partitioning at automatically detected regions. Partitioning is guided by a novel random forest based quality metric, trained to recognise high-quality, single-object segments. A second novel segmentation quality measure is presented for quantifying the quality of full segmentations based on the random forest metric of the constituent parts and the error in the number of objects segmented. In a comparative evaluation between the proposed approach and three state-of-the-art volumetric segmentation techniques designed for single-energy CT data (two region-growing [1] and [2] and one graph-based [3]) our method is shown to outperform both region-growing methods in terms of segmentation quality and speed. Although the graph-based approach generates more accurate partitions, it is characterised by high processing times and is significantly outperformed by the proposed method in this regard. The observations made in this study indicate that the proposed segmentation technique is well-suited to the baggage security-screening domain, where the demand for computational efficiency is paramount to maximise throughput.

Citation

Mouton, A., & Breckon, T. (2015). Materials-Based 3D Segmentation of Unknown Objects from Dual-Energy Computed Tomography Imagery in Baggage Security Screening. Pattern Recognition, 48(6), 1961-1978. https://doi.org/10.1016/j.patcog.2015.01.010

Journal Article Type Article
Acceptance Date Jan 13, 2015
Online Publication Date Jan 19, 2015
Publication Date Jun 1, 2015
Deposit Date Oct 4, 2015
Publicly Available Date Oct 5, 2015
Journal Pattern Recognition
Print ISSN 0031-3203
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 48
Issue 6
Pages 1961-1978
DOI https://doi.org/10.1016/j.patcog.2015.01.010
Keywords Segmentation, Dual-energy computed tomography, Random forests, Baggage-CT imagery.
Public URL https://durham-repository.worktribe.com/output/1430477
Related Public URLs http://community.dur.ac.uk/toby.breckon/publications/papers/mouton15segmentation.pdf

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