Automatic Segmentation of Lumen Intima Layer in Transverse Mode Ultrasound Images

We propose an elliptical active disc technique for the segmentation of common carotid artery lumen intima layer from transverse mode ultrasound images. The segmentation and subsequent outlining problem is posed as one of optimization of a local energy function with respect to the five degrees-of-freedom that characterize the elliptical active disc. Gradient descent technique is used to find the minimum of the energy function with respect to the five parameters that describe the disc. In addition, we use Green's theorem to optimize the computation of the partial derivatives. For automatic initialization of the active disc, we use the normalized crosscorrelation technique. We report results of experimental validation on SPLab, Brno university database, which contains 971 transverse mode ultrasound images of the carotid artery. We achieve accurate carotid artery lumen intima detection in 97.63% of cases. In addition, for lumen intima layer segmentation we achieve an average Dice index of 94.83%.