Kengyelics, SM, Gislason-Lee, AJ, Keeble, C et al. (2 more authors) (2015) Machine vision image quality measurement in cardiac x-ray imaging. In: Proceedings of SPIE 9399, Image Processing: Algorithms and Systems XIII. SPIE Electronic Imaging, 08-13 Feb 2015, San Francisco, California, USA. Society of Photo-optical Instrumentation Engineers (SPIE) ISBN 9781628414899
Abstract
The purpose of this work is to report on a machine vision approach for the automated measurement of x-ray image contrast of coronary arteries filled with iodine contrast media during interventional cardiac procedures. A machine vision algorithm was developed that creates a binary mask of the principal vessels of the coronary artery tree by thresholding a standard deviation map of the direction image of the cardiac scene derived using a Frangi filter. Using the mask, average contrast is calculated by tting a Gaussian model to the greyscale profile orthogonal to the vessel centre line at a number of points along the vessel. The algorithm was applied to sections of single image frames from 30 left and 30 right coronary artery image sequences from different patients. Manual measurements of average contrast were also performed on the same images. A Bland-Altman analysis indicates good agreement between the two methods with 95% confidence intervals -0.046 to +0.048 with a mean bias of 0.001. The machine vision algorithm has the potential of providing real-time context sensitive information so that radiographic imaging control parameters could be adjusted on the basis of clinically relevant image content.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2015, Society of Photo-Optical Instrumentation Engineers (SPIE). This is an author produced version of a paper published in Proceedings of SPIE 9399, Image Processing: Algorithms and Systems XIII. |
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 Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) The University of Leeds > Faculty of Medicine and Health (Leeds) |
Funding Information: | Funder Grant number EU - European Union 269104 Innovate UKfka Technology Strategy Board (TSB) 600735/296104-1 EU - European Union 600735/296104-1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 01 Oct 2015 12:50 |
Last Modified: | 17 Jan 2018 02:47 |
Published Version: | http://dx.doi.org/10.1117/12.2083208 |
Status: | Published |
Publisher: | Society of Photo-optical Instrumentation Engineers (SPIE) |
Identification Number: | 10.1117/12.2083208 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:84809 |