Abstract
A minicomputer based system has been developed for studying carotid artery blood flow data obtained for a combined B-mode, pulsed Doppler ultrasound scanner. The goals of this work are to devise and improve techniques for estimating the extent of atherosclerosis at the carotid artery bifurcation. Features are automatically extracted from spectrum analyzed Doppler blood flow data. Five statistical pattern recognition algorithms are compared, with cross validation being used to improve the estimate of classification accuracy. A data collection protocol has been devised in which four sites are studied along each carotid arterial system. Classification of unknowns is done using a hierarchy of three decisions.
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