An automated technique for carotid far wall classification using grayscale features and wall thickness variability

Purpose To test a computer‐aided diagnostic method for differentiating symptomatic from asymptomatic carotid B‐mode ultrasonographic images. Methods Our system (called Atheromatic) automatically computed the intima‐media thickness ( IMT ) of the carotid far wall using AtheroEdge, calculated nonlinear features based on higher order spectra, and used these features and IMT and IMT variability ( IMTV poly ) to associate each image to a feature vector that was then labeled as symptomatic or asymptomatic ( Sym / Asym ) by a multiclassifiers system. We tested this method on a database of 118 carotid artery images from 37 symptomatic and 22 asymptomatic patients Results The highest accuracy (99.1%) was obtained by the support vector machine classifier using seven features. These features, relevant to discriminate Sym/Asym , included IMT and IMTV poly , along with the bispectral entropies of the distal wall image at 77°, 78°, and 79° angles. Conclusions Classification in Sym/Asym of the far carotid wall is feasible and accurate and could be useful for the early detection of atherosclerosis and to identify patients with higher cardiovascular risk. © 2014 Wiley Periodicals, Inc. J Clin Ultrasound 43:302–311, 2015