Machine vision image quality measurement in cardiac x-ray imaging

Stephen M. Kengyelics; Amber Gislason-Lee; Claire Keeble; Derek Magee; Andrew G. Davies

doi:10.1117/12.2083208

16 March 2015 Machine vision image quality measurement in cardiac x-ray imaging

Stephen M. Kengyelics, Amber Gislason-Lee, Claire Keeble, Derek Magee, Andrew G. Davies

Proceedings Volume 9399, Image Processing: Algorithms and Systems XIII; 93990J (2015) https://doi.org/10.1117/12.2083208
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States

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 fitting 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.

Citation Download Citation

Stephen M. Kengyelics, Amber Gislason-Lee, Claire Keeble, Derek Magee, and Andrew G. Davies "Machine vision image quality measurement in cardiac x-ray imaging", Proc. SPIE 9399, Image Processing: Algorithms and Systems XIII, 93990J (16 March 2015); https://doi.org/10.1117/12.2083208

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KEYWORDS

Arteries

X-ray imaging

Machine vision

Iodine

Algorithm development

Imaging systems

X-rays

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