Bayesian tracking for fluorescence microscopic imaging

Fluorescence microscopy is a powerful imaging tool for studying molecular dynamics in living cells. For quantitative motion analysis of subcellular structures robust and accurate detection and tracking techniques are necessary sequential Monte Carlo methods, also known as particle filters (PF), have become a tremendously popular tool to perform tracking in many fields. We propose a PF-based approach for quantitative analysis of subcellular dynamics. This approach utilizes all spatiotemporal information, which is an important advantage over existing methods that separate the object detection and object linking stage. The tracking technique has been evaluated using simulated but highly realistic image sequences, for which ground truth was available, showing that the method is more robust to noise than existing tracking techniques. In addition, evaluation experiments were conducted with real fluorescence microscopy image data acquired for specific biological studies