Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.510176
Title: View synthesis for depth from motion 3D X-ray imaging
Author: Liu, Yong
Awarding Body: Nottingham Trent University
Current Institution: Nottingham Trent University
Date of Award: 2009
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Abstract:
The depth from motion or kinetic depth X-ray imaging (KDEX) technique is designed to enhance the luggage screening at airport checkpoints. The technique requires multiple views of the luggage to be obtained from an arrangement of linear X-ray detector arrays. This research investigated a solution to the unique problems defined when considering the possibility of replacing some of the X-ray sensor views with synthetic images. If sufficiently high quality synthetic images can be generated then intermediary X-ray sensors can be removed to minimise the hardware requirements and improve the commercial viability of the KDEX technique. Existing image synthesis algorithms are developed for visible light images. Due to fundamental differences between visible light and X-ray images, those algorithms are not directly applicable to the X-ray scenario. The conditions imposed by the X-ray images have instigated the original research and novel algorithm development and experimentation that form the body of this work. A voting based dual criteria multiple X-ray images synthesis algorithm (V-DMX) is proposed to exploit the potential of two matching criteria and information contained in a sequence of images. The V-DMX algorithm is divided into four stages. The first stage is to aggregate matching cost among input images. Subsequently, a novel voting approach is developed for electing the 'best' disparity prior to generation of synthetic pixels. A void filling routine is applied to complete the synthetic image generation. A series of experiments, using real acquired images, investigated the fidelity of the synthesised images resulting from application of the V-DMX algorithm as a function of several parameters: number of input images, matching criterion, method of handling multiple images and X-ray beam separation. The performance measure is based on counting the number of pixel errors in the synthetic images relative to the ground truth images. The V-DMX employs the widely adopted sum of squared differences (SSD) criterion and a novel criterion, which is derived from the laminography technique, termed laminography intensity (LamI). SSD is shown experimentally to have poor performance when the image contains repeating features, discontinuities and overlapping regions. While the overall performance of the LamI is found to be weaker than SSD, LamI consistently outperformed SSD in discontinuity and overlapping regions. This has spurred the use of LamI as a complement to SSD. Integration of the two criteria has demonstrably produced better results than using solely either of the criteria. Limitations of the algorithm are assessed by increasing the angular separation between X-ray beams used to produce the perspective X-ray images. The resultant image fidelity degraded as the angular separation increases. This result was expected because the increase in angular separation meant a concomitant increase in images' dissimilarity and disparity window. Empirical evidence demonstrated that synthetic images may be satisfactorily produced by processing images produced by X-ray beams separated by angular increments up to 6º. This result is based on comparing the algorithm performance for four beam separations, which are 4°, 6°, 8° and 10°. This finding reveals that, for example, a 32-view X-ray scanner with 1° beam separation may be scaled down to a 7-view system with at least the same angular coverage. The encouraging result has formed a basis for further research to extend the current algorithmic approach to the use of dual-energy X-ray data. The practical performance of the algorithm will be evaluated by conducting human factors investigation in collaboration with the US Department of Homeland Security.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.510176  DOI: Not available
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