Tracking virus particles in fluorescence microscopy images using two-step multi-frame association

Automatic fluorescent particle tracking is an essential task to study the dynamics of a large number of biological structures at a sub-cellular level. We have developed a two-step multi-frame association finding algorithm which is based on a temporally semi-global formulation as well as combines a spatially global and a spatially local approach. Using this multi-frame association finding algorithm we have developed a probabilistic tracking approach based on the Kalman filter. We have successfully applied the approach to synthetic as well as real microscopy image sequences of ALV virus particles and quantified the performance. We found that the proposed approach outperforms previous approaches.