Pattern Spectrum and Multiscale Shape Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks
Neural network PC tools: a practical guide
Neural network PC tools: a practical guide
Making large-scale support vector machine learning practical
Advances in kernel methods
Data mining: concepts and techniques
Data mining: concepts and techniques
An Introduction to Nonlinear Image Processing
An Introduction to Nonlinear Image Processing
IEEE Transactions on Information Technology in Biomedicine
Mining bridging rules between conceptual clusters
Applied Intelligence
Symptomatic vs. Asymptomatic Plaque Classification in Carotid Ultrasound
Journal of Medical Systems
A multi-threshold segmentation approach based on Artificial Bee Colony optimization
Applied Intelligence
Understanding symptomatology of atherosclerotic plaque by image-based tissue characterization
Computer Methods and Programs in Biomedicine
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The aim of this study was to investigate the usefulness of multilevel binary and gray scale morphological analysis in the assessment of atherosclerotic carotid plaques. Ultrasound images were recorded from 137 asymptomatic and 137 symptomatic plaques (Stroke, Transient Ischaemic Attack (TIA), Amaurosis Fugax (AF)). We carefully develop the clinical motivation behind our approach. We do this by relating the proposed L-images, M-images and H-images in terms of the clinically established hypoechoic, isoechoic and hyperechoic classification.Normalized pattern spectra were computed for both a structural, multilevel binary morphological model, and a direct gray scale morphology model. From the plots of the average pattern spectra, it is clear that we have significant differences between the symptomatic and asymptomatic spectra. Here, we note that the morphological measurements appear to be in agreement with the clinical assertion that symptomatic plaques tend to have large lipid cores while the asymptomatic plaques tend to have small lipid cores.The derived pattern spectra were used as classification features with two different classifiers, the Probabilistic Neural Network (PNN) and the Support Vector Machine (SVM). Both classifiers were used for classifying the pattern spectra into either a symptomatic or an asymptomatic class. The highest percentage of correct classifications score was 73.7% for multilevel binary morphological image analysis and 66.8% for gray scale morphological analysis. Both were achieved using the SVM classifier. Among all features, the L-image pattern spectra, that also measure the distributions of the lipid core components (and some non-lipid components) gave the best classification results.