Contour tracking of left ventricle based on generalized fuzzy particle filter

Summary form only given. For medical image sequences, the method of contour-based tracking proved to be a powerful tool for boundary delineation. During contour evolution, the particle filter (PF) can be used to track the feature points by enforcing spatio-temporal local constraints to handle the observation noise. To optimize the importance ratios (IR) of PF and improve its capability, a new approach of generalized fuzzy particle filter (GFPF) is presented. Compared with the unscented particle filter (UPF) that is currently a good method for object tracking, GFPF shows more advantages, including lower particle degeneracy, higher precision and so on. In addition, a likelihood estimation model is constructed to provide the observation data for GFPF. By theoretic analysis and contrast experiments, it is clear that GFPF is a good method for left ventricle tracking.