Tracking multiple particles in fluorescence microscopy images via probabilistic data association

Tracking subcellular structures displayed as `particles´ in fluorescence microscopy images yields quantitative descriptions of the underlying dynamical processes. We have developed an approach for tracking multiple fluorescent particles. Our approach includes a localization scheme using probabilistic data association that combines a top-down strategy driven by the Kalman filter and a bottom-up strategy using standard localization algorithms for fluorescent particles. The combined scheme yields multiple positions that are incorporated to the filter via a combined innovation. To track objects in close proximity, we introduce a support map that adjusts the association probabilities. By using the combined localization scheme in conjunction with the Kalman filter we integrate localization and position estimation. The approach has been successfully applied to synthetic images as well as to real microscopy image sequences and the performance has been quantified.