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Journal of Electronic Imaging

Context sensitive cardiac x-ray imaging: a machine vision approach to x-ray dose control
Author(s): Stephen M. Kengyelics; Amber J. Gislason-Lee; Claire Keeble; Derek R. Magee; Andrew G. Davies
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Paper 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.

Paper Details

Date Published: 21 September 2015
PDF: 9 pages
J. Electron. Imag. 24(5) 051002 doi: 10.1117/1.JEI.24.5.051002
Published in: Journal of Electronic Imaging Volume 24, Issue 5
Show Author Affiliations
Stephen M. Kengyelics, Univ. of Leeds (United Kingdom)
Amber J. Gislason-Lee, Univ. of Leeds (United Kingdom)
Claire Keeble, Univ. of Leeds (United Kingdom)
Derek R. Magee, Univ. of Leeds (United Kingdom)
Andrew G. Davies, Univ. of Leeds (United Kingdom)

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