Two-filter probabilistic data association for tracking of virus particles in fluorescence microscopy images

Tracking subcellular structures displayed as small spots in fluorescence microscopy images is important to determine quantitative information of biological processes. We have developed an approach for tracking multiple fluorescent particles based on two-filter smoothing and probabilistic data association. Compared to previous work, our approach exploits information from past and future time points, integrates multiple measurements, and combines Kalman filtering and particle filtering. We evaluated our approach based on data from the ISBI Particle Tracking Challenge and found that it yields state-of-the-art results for low signal-to-noise ratios. We also applied our method to live cell fluorescence microscopy image sequences of HIV-1 particles and HCV proteins. It turned out that the new approach generally outperforms existing methods.