AUTOMATIC SEGMENTATION OF COMMON CAROTID ARTERY IN TRANSVERSE MODE ULTRASOUND IMAGES

We consider the problem of carotid artery segmentation and develop an automated outlining technique based on the active disc formalism that we recently introduced. The outlining problem is posed as one of optimization of a locally defined contrast function with respect to the affine transformation parameters that characterize the active disc. It turns out that standard techniques based on gradient-descent minimization can be used to carry out the optimization, although more sophisticated optimizers could also be deployed. For the initialization, we use a matched filter with a template size chosen based on an estimate of the average size of the carotid artery. We report results of experimental validation on Brno university's signal processing (SP) lab database, which contains 971 transverse mode ultrasound images of the carotid artery. The images in the database are manually annotated using a circle with center and radius explicitly specified in pixels, which serves as the reference. The circular annotation is also a good match with the active disc template considered in this paper. The proposed method results in an average detection accuracy of 95.5% and an average Dice similarity measure of 87.36% and takes only a few seconds of processing time per image. Comparisons with other state-of-the-art techniques are also reported.