We use cookies to ensure that we give you the best experience on our website. By continuing to browse this repository, you give consent for essential cookies to be used. You can read more about our Privacy and Cookie Policy.

Durham Research Online
You are in:

Materials-based 3D segmentation of unknown objects from dual-energy computed tomography imagery in baggage security screening.

Mouton, A. and Breckon, T.P. (2015) 'Materials-based 3D segmentation of unknown objects from dual-energy computed tomography imagery in baggage security screening.', Pattern recognition., 48 (6). pp. 1961-1978.


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.

Item Type:Article
Additional Information:
Keywords:Segmentation, Dual-energy computed tomography, Random forests, Baggage-CT imagery.
Full text:(AM) Accepted Manuscript
Available under License - Creative Commons Attribution Non-commercial No Derivatives.
Download PDF
Publisher Web site:
Publisher statement:© 2015 This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Date accepted:13 January 2015
Date deposited:05 October 2015
Date of first online publication:19 January 2015
Date first made open access:No date available

Save or Share this output

Look up in GoogleScholar