Particle Tracking in Noisy Microscopy Images: the Multiple Hypothesis Tracking Approach
Nicolas Chenouard, Quantitative Image Analysis Group Institut Pasteur, Paris

Seminar • 20 July 2009 • BM 4.233

Abstract
Multiple hypothesis tracking (MHT) is a preferred technique for solving the data association problem in modern multiple targets tracking systems. However in bioimaging applications, its use has long been thought impossible due to the prohibitive cost induced by the high number of objects that need to be tracked and the poor quality of images. We have proposed a new MHT formulation in which target perceivability is modeled whereby early track termination and false measurements exclusion reduce the problem complexity and improve the results robustness to clutter. Moreover, we have proposed a MHT implementation that is fast by exploiting the tree structure of the potential tracks. We have applied the method to the analysis of several sets of real microscopy images containing thousands of biological targets. By doing so we prove the benefits of the approach when tracking in very noisy environments such as low-light level fluorescent microscopy images.