Multi-feature texture analysis for the classification of carotid plaques

We develop a computer aided system which will facilitate the automated characterisation 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: symptomatic or asymptomatic. Ten different texture feature sets were extracted from the segmented plaque image. 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 the majority voting and 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%