A fully automated method for carotid plaques segmentation in ultrasound images based on motion estimation and level-set

Ultrasound (US) guided carotid atherosclerosis diagnosis is based on the evaluation of the stenosis degree due to the presence of carotid plaques (CPs) and on CPs composition study. Accurate and automated CPs segmentation is essential to enable these evaluations. In this work, we present a fully automated CPs segmentation method based on level-set with an innovative initialization procedure that makes the approach completely user-independent by exploiting the carotid walls motion analysis. Performance were tested on 10 US image sequences of carotid artery and compared with manual contouring from an expert physician. Results show the effectiveness of our method at performing accurate CPs segmentation in US images without requiring any user intervention.