A sequential Bayesian based method for tracking and strain palpography estimation of arteries in intravascular ultrasound images
Nicolas Widynski, Jonathan Porée, Marie‐Hélène Roy Cardinal, Jacques Ohayon, Guy M. Cloutier, Damien Garcia · 2014
This paper investigates the task of tracking and strain estimation of arteries in intravascular ultrasound images. A tracking method is proposed to extract the inner and the outer contours of the vessel wall (lumen/intima-media and intima-media/adventitia interfaces, respectively), and the deformations along them. This estimation is carried out by a non parametric sequential Bayesian method. The Bayesian modeling holds three ingredients: the prior, which is given by a manually defined segmentation of the contours on the first image; the transition, which is assumed to follow a Markovian random walk; and the likelihood, which is a distance between patches distributed along the contours. The underlying Bayesian posterior distribution is approximated using a sequential Monte Carlo approach. Experiments on three PVA-C phantoms present direct readings of the deformations along the lumen/intima-media contour.