Abstract: Contrast-enhanced ultrasound (CEUS) is of great value for the diagnosis and treatment of vascular diseases. Extraction of carotid arterial contours is important for the measurement of morphological and elastic properties of arteries. Since manually tracing of arterial contours is time-consuming, subjective, and unrepeatable, computer-aided methods are required. However, speckle noise in the CEUS images causes poor robustness and difficult initialization in traditional computer-aided image segmentation methods. This paper integrates multi-scale fuzzy C-means clustering with particle swarm optimization to extract coarse boundaries of carotid arteries. Then boundaries are used as initial contours of the directional gradient vector flow (DGVF) model, and deform them until convergence to get final refined contours. Experimental results on 48 CEUS images from 14 patients show that the proposed method is superior to the traditional method, and can automatically and accurately extract boundaries of carotid arteries in CEUS images.

Key words: contrast-enhanced ultrasound (CEUS), directional gradient vector flow (DGVF) model, fuzzy C-means (FCM) clustering, multi-scale analysis, vascular contours