Kengyelics, SM, Gislason-Lee, AJ, Keeble, C orcid.org/0000-0003-1633-8842 et al. (2 more authors) (2015) Context sensitive cardiac x-ray imaging: a machine vision approach to x-ray dose control. Journal of Electronic Imaging, 24 (5). ARTN 051002. ISSN 1017-9909
Abstract
Modern cardiac x-ray imaging systems regulate their radiation output based on the thickness of the patient to maintain an acceptable signal at the input of the x-ray detector. This approach does not account for the context of the examination or the content of the image displayed. We have developed a machine vision algorithm that detects iodine-filled blood vessels and fits an idealized vessel model with the key parameters of contrast, diameter, and linear attenuation coefficient. The spatio-temporal distribution of the linear attenuation coefficient samples, when appropriately arranged, can be described by a simple linear relationship, despite the complexity of scene information. The algorithm was tested on static anthropomorphic chest phantom images under different radiographic factors and 60 dynamic clinical image sequences. It was found to be robust and sensitive to changes in vessel contrast resulting from variations in system parameters. The machine vision algorithm has the potential of extracting real-time context sensitive information that may be used for augmenting existing dose control strategies.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | Kengyelics, SM, Gislason-Lee, AJ, Keeble, C , Magee, DR and Davies, AG (2015) Context sensitive cardiac x-ray imaging: a machine vision approach to x-ray dose control. Journal of Electronic Imaging, 24 (5). ARTN 051002. http://doi.org/10.1117/1.JEI.24.5.051002 (c) 2015 SPIE and IS&T. One print or electronic copy may be made for personal use only. Systematic electronic or print reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. |
Keywords: | cardiac; x-ray; contrast; machine vision |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Artificial Intelligence & Biological Systems (Leeds) The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Genetics, Health and Therapeutics (LIGHT) > Division of Epidemiology & Biostatistics (Leeds) |
Funding Information: | Funder Grant number ENIAC JU UNSPECIFIED |
Depositing User: | Symplectic Publications |
Date Deposited: | 30 Sep 2016 14:35 |
Last Modified: | 17 Jan 2018 07:19 |
Published Version: | http://doi.org/10.1117/1.JEI.24.5.051002 |
Status: | Published |
Publisher: | Society of Photo-optical Instrumentation Engineers (SPIE) |
Identification Number: | 10.1117/1.JEI.24.5.051002 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:97512 |