Segmentation of carotid plaques in standard B-mode ultrasound is challenging due to irregular lumen shapes, noise, artifacts, and plaque echolucency. To overcome these challenges, we propose a novel carotid plaque segmentation method which exploits the benefits of simultaneously acquired B-mode ultrasound (BMUS) and contrast enhanced ultrasound (CEUS). We first estimate the anatomical motion from the BMUS image sequence, using nonrigid intensity-based image registration, and apply to BMUS and CEUS image sequences for motion compensation. We average the motion-compensated image sequences to obtain single BMUS&CEUS images with improved signal-to-noise ratio, which serve as 'epitome' images. We apply vessel detection to distinguish single and multiple branches in the CEUS epitome. The lumen-intima layer is segmented from the epitomes by a joint-histogram classification approach, followed by a 1D dynamic programming procedure. Then, the media-adventitia layer is segmented by using dual (2D) dynamic programming (DDP). As media-adventitia layer and adventitial wall are almost parallel to each other, DDP is used to find two almost parallel and smooth lines by combining their costs. For validation, the lumen-intima and media-adventitia layers of 13 carotid arteries with atherosclerotic plaques were manually segmented by two observers and compared to the automated results. The average of the two manual segmentations was considered as ground-truth. For the lumen-intima layer, average ± std. dev. root-mean-square-error was 283±123μm. The inter-observer variability was 261±128μm. For the media-adventitia layer, average root-mean-square-error was 334±170μm. The inter-observer variability was 225±130μm. The differences between automated and ground-truth contours are in the same order as those between two observers. In conclusion, we present an accurate, robust, and novel fully automated plaque segmentation method in combined BMUS&CEUS images. To the best of our knowledge, this is the first study to exploit the combination of BMUS&CEUS to solve the segmentation challenges for carotid plaques.