Multi feature texture analysis for the classification of carotid plaques

Abstract

The objective of this work was to develop a computer aided system which will facilitate the automated characterization of carotid plaques recorded from high resolution ultrasound images for the identification of individuals with asymptomatic carotid stenosis at risk of stroke. The plaques were classified into (i) symptomatic or (ii) asymptomatic. Ten different texture feature sets were extracted from the segmented plaque image using the following algorithms: fast order statistics, spatial gray level dependence matrices, gray level difference statistics, neighborhood gray tone difference matrix, statistical feature matrix, Laws texture energy measures, fractal dimension texture analysis, Fourier power spectrum and shape parameters. Although the statistics of all features extracted for the two classes indicated a high degree of overlap, a classification of the plaques was possible using the unsupervised self-organizing feature map (SOFM) classifier and combining techniques. The classification results of the different feature sets were combined using (i) majority voting and (ii) weighted averaging based on a confidence measure derived from the SOFM. Combining the classification results of the ten different feature sets improved significantly the classification results obtained by the individual feature sets, reaching an average diagnostic yield of 75%.