  We focus on the biological problem of tracking organelles as they move
through cells. In the past, most intracellular movements were recorded
manually, however, the results are too incomplete to capture the full
complexity of organelle motions. An automated tracking algorithm promises to
provide a complete analysis of noisy microscopy data. In this paper, we adopt
statistical techniques from a Bayesian random set point of view. Instead of
considering each individual organelle, we examine a random set whose members
are the organelle states and we establish a Bayesian filtering algorithm
involving such set states. The propagated multi-object densities are
approximated using a Gaussian mixture scheme. Our algorithm is applied to
synthetic and experimental data.
