Classification of Carotid
Artery Abnormalities in Ultrasound Images using an Artificial Neural Classifier
Dhanalakshmi Samiappan1 and Venkatesh Chakrapani2
1Department
of Electronics and Communication Engineering, SRM University, India.
2Department of Electronics and Communication Engineering, Sengunthar Engineering College, India.
Abstract: This work presents a
computer-aided system for the identification of plaques and atherosclerosis of
carotid abnormalities and the individuals at risk of stroke. Intima Media
Thickness (IMT) of carotid artery is the standard biomarker of subclinical
atherosclerosis and plaques. Conventional IMT measurement by expert sonologist
is time consuming, associated with subjectivity and the process becomes
difficult when the number of patients is very large. This paper proposes a
standard protocol to diagnose patients efficiently and the process is made
extremely fast. In this paper, the decision making ability of an artificial learning machine is
investigated in carotid ultrasound artery image classification. Architecture with multilayer Back Propagation Network (BPN) using
Levenberg-Marquardt training with good generalization capabilities and extremely fast learning
capacity that overcomes the local minima problem of
generalized BPN has been proposed. Carotid images are preprocessed, normalized
and segmented to extract eighteen different feature sets and given as input to
Artificial Neural Network (ANN). The selected features are found to be the good
choice of feature vectors and have the ability to discriminate between normal
and abnormal image. The proposed system is robust to any ultrasound image
artifact. ANN classifier is evaluated using 361 ultrasound images. The
efficiency is measured by validating the outputs of this decision support
system with that of medical experts. This system improves the classification
rate, reaching the diagnostic yield of 89.43%. The simulation results depicts
that ANN achieves good classification accuracies with less implementation
complexity when compared with manual operation.
Keywords: Artificial
neural network, multilayer back propagation network, ultrasound carotid artery, carotid intima-media thickness, subclinical
atherosclerosis, decision assist system.
Received November 8, 2013;
accepted January 16, 2014