Differentiating between stable and progressive carotid atherosclerotic plaques from in-vivo ultrasound images using texture descriptors

We describe an automatic pipeline for processing ultrasound images of the carotid artery, consisting of image type classification, carotid artery localization, segmentation, feature descriptor extraction, and plaque stability classification. The aim is to distinguish between stable (safe) and progressive (dangerous) atherosclerotic plaques from a single standard ultrasound transversal or longitudinal B-mode examination. The processing pipeline uses modern deep CNN techniques, while the descriptors are based on geometry and wavelets to characterize texture. When testing on a large dataset of 28718 images from 413 patients, we found that our automatically calculated descriptors are statistically significantly different between the two classes with a very high significance level, p < 10(-3). We have also created a random forest-based classifier to distinguish between progressive and stable plaques, although its accuracy remains low (61 similar to 62%).