Segmentation and Tracking of Coronary Arterial Wall - Xianghua Xie (Computational and Mathematical Biomedical Engineering, 2017)
In this paper, we present a segmentation and tracking method based on hidden Markov model (HMM) to detect the outer coronary arterial wall in intravascular images. The proposed method tracks a set of hidden states representing the border location on a set of normal lines obtained from the previ- ous frame. The border observation is derived from a classification-based cost function and a shape prior model. The emission probability is defined based on two Gaussian probability distributions for the vessel border and background. The transition probability is learned by using the Baum-Welch algorithm. The optimal sequence of the hidden states is obtained by using Viterbi algorithm. The proposed method shows promising results on tracking and segmenting the arterial wall.