Browsing Health Studies by Subject "Machine vision"
Now showing items 1-3 of 3
Context sensitive cardiac x-ray imaging: a machine vision approach to x-ray dose controlModern 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.
Machine vision image quality measurement in cardiac x-ray imagingThe purpose of this work is to report on a machine vision approach for the automated measurement of x-ray image contrast of coronary arteries lled 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 lter. Using the mask, average contrast is calculated by tting a Gaussian model to the greyscale pro le 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 di erent 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% con dence 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.
Noise estimation in cardiac x-ray imaging: a machine vision approachWe propose a method to automatically parameterize noise in cardiac x-ray image sequences. The aim was to provide context-sensitive imaging information for use in regulating dose control feedback systems that relates to the experience of human observers. The algorithm locates and measures noise contained in areas of approximately equal signal level. A single noise metric is derived from the dominant noise components based on their magnitude and spatial location in relation to clinically relevant structures. The output of the algorithm was compared to noise and clinical acceptability ratings from 28 observers viewing 40 different cardiac x-ray imaging sequences. Results show good agreement and that the algorithm has the potential to augment existing control strategies to deliver x-ray dose to the patient on an individual basis.