Assessment of stroke by analysing carotid plaque morphology

Abstract

Stroke is the third leading cause of death in the Western world and a major cause of disability in adults. The objective of this work was to investigate morphological feature extraction techniques and the use of automatic classifiers; in order to develop a computer aided system that will facilitate the automated characterization of carotid plaques for the identification of individuals with asymptomatic carotid stenosis at risk of stroke. Through this chapter we summarize the recent advances in ultrasonic plaque characterization and evaluate the efficacy of computer aided diagnosis based on neural and statistical classifiers using as input morphological features. Several classifiers like the K-Nearest Neighbour(KNN) the Probabilistic Neural Network(PNN) and the Support Vector Machine(SVM) were evaluated resulting to a diagnostic accuracy up to 73.7%.