Active contours tracking of medical images based on the generalized fuzzy particle filter

In the field of medical image visual tracking, Contour-based tracking methods have been proved to be a powerful tool for boundary delineation. During contour evolution, the particle filter (PF) is used to track the feature points by enforcing spatio-temporal local constraints to handle the observation noise. To improved the capability of PF and optimize its importance ratios (IR), the generalized fuzzy particle filter (GFPF) is presented in this paper. By comparing with the UPF which is a good method in object tracking, the GFPF shows great advantage. Another, with regard to likelihood estimation (LE), a special model of LE is constituted for contour estimation. By theoretic evaluation and sufficient contrast experiments, it is clear that the GFPF is a better measure for contours tracking and provide a novel resource for computing the IR of PF.