Enhanced particle-filtering framework for vessel segmentation and tracking.

BACKGROUND AND OBJECTIVES: A robust vessel segmentation and tracking method based on a particle-filtering framework is proposed to cope with increasing demand for a method that can detect and track vessel anomalies.

METHODS: We apply the level set method to segment the vessel boundary and a particle filter to track the position and shape variations in the vessel boundary between two adjacent slices. To enhance the segmentation and tracking performances, the importance density of the particle filter is localized by estimating the translation of an object's boundary. In addition, to minimize problems related to degeneracy and sample impoverishment in the particle filter, a newly proposed weighting policy is investigated.

RESULTS: Compared to conventional methods, the proposed algorithm demonstrates better segmentation and tracking performances. Moreover, the stringent weighting policy we proposed demonstrates a tendency of suppressing degeneracy and sample impoverishment, and higher tracking accuracy can be obtained.

CONCLUSIONS: The proposed method is expected to be applied to highly valuable applications for more accurate three-dimensional vessel tracking and rendering.