Contrast-enhanced ultrasound image segmentation of atherosclerotic plaques using spatial-temporal analysis and snakes

It is valuable to segment carotid atherosclerotic plaques in contrast-enhanced ultrasound (CEUS) images. Traditional methods for plaque segmentation neglect the temporal information in video images, and it limits the accuracy of the segmentation. We propose an algorithm for accurate segmentation of plaques in CEUS images using spatial-temporal analysis and snakes. First, the spatial correlation of time intensity curves is used to capture detailed information from spatial-temporal neighborhoods of plaques and detect initial contours of the plaques. Then the speckle reducing anisotropic diffusion is adopted to modify the edge map of the gradient vector flow snake, and the initial contours are deformed using the modified snake and converged to refined contours. The proposed method was evaluated via 21 in vivo images, and it outperformed the boundary vector field snake by 0.06 mm, 2.0%, 0.04 mm and 5.3%, in terms of the mean distance error, relative mean distance error, mean signed distance error, and relative difference degree, respectively. The results revealed that the proposed method can accurately detect plaques and delineate their contours.