[Dynamic contour tracking of medical images based on improved particle filter]

In the research of medical image processing, motion estimation and tracking relating to the region of interest has been given considerable attention. For improving the quality of the noisy or cluttered medical images, the particle filter (PF) based on the non-linear and non-Gaussian Bayesian State Estimation is a better as well as a technically challenging solution. As the algorithm of particle weights, especially the importance density function, often severely affects the performance of the PF, we propose in this paper a better algorithm for its improvement; in addition, to ensure better tracking of the dynamic contour with the PF, we proposed a new algorithm for the likelihood and prior probability density. Objective theoretical evaluation and substantial comparative experiments suggest that this method can be a good solution for accurate dynamic contour tracking.