A DECISION SYSTEM FOR PLAQUE DETECTION IN THE CAROTID ARTERY

Abstract

Carotid Doppler ultrasound and imaging are focused on the visualization and measurement of blood flow providing critical diagnostic information noninvasively about fluid dynamics and abnormalities. Ultrasound imaging is a complicated interplay between physical principles and signal processing methods. In this work a two-step methodology to predict carotid plaque disruption is reported. An automatic technique based on row wise pixel intensity distribution alleviates the laborious and time consuming manual evaluation and classification of the carotid artery intima-media thickness. Selected image-based parameters, extracted from pixel intensity information and associated with risk score of carotid atherosclerotic plaques, were introduced in an artificial neural network model the feasibility of possible development of neurological complications due to plaque disruption.