Segmentation of carotid artery in ultrasound images

Abstract

Segmentation of carotid artery lumen in 2D and 3D ultrasonography is an important step in evaluating arterial disease severity and finding vulnerable artherosclerotic plaques susceptible to rupture causing stroke. Because of the complexity of anatomical structures, noise as well as the requirement of accurate segmentation, interactions are necessary between observers and computer segmentation process. We describe a segmentation algorithm based on a discrete dynamic model approach with only one seed point to guide the initialization of the deformable model for each lumen cross-section. With one seed, the initial contour of the deformable model is generated using the entropy map of the original image and mathematical morphology operations. The deformable model is driven to fit the lumen contour by an internal force and an external force that are calculated respectively with geometrical properties of deformed contour and with the image gray level features. We also introduce a set of metrics based on a contour probability distribution function for evaluating the accuracy and variability of the interactive segmentation algorithm. These metrics provide a complete performance evaluation of an interactive segmentation algorithm and a means for comparing different algorithm settings. Seven images of the common, internal and external carotid arteries were chosen to test the segmentation algorithm. The average position error and average variability of the boundary segmentation result are 0.2 mm and 0.25 mm.