A Multitarget Tracking Method for Estimating Carotid Artery Wall Motion from Ultrasound Sequences

Abstract

Analyzing the motion of the wall of the common carotid artery (CCA) yields effective indicators for atherosclerosis. In this work, we explore the use of multitarget tracking techniques for estimating the time-varying CCA radius from an ultrasound video sequence. We employ the joint integrated probabilistic data association (JIPDA) filter to track a set of “feature points” (FPs) located around the CCA wall cross section. Subsequently, we estimate the time-varying CCA radius via a non-linear least-squares method and a Kalman filter. The application of the JIPDA filter is enabled by a linearized state-space model describing the quasi-periodic movement of the FPs and the measurement extraction process. Simulation results using the Field II ultrasound simulation program show that the proposed multitarget tracking method can outperform a state-of-the-art method.