Direct combination of multi-scale detection and multi-frame association for tracking of virus particles in microscopy image data

The importance of automatic particle tracking for analyzing microscopy image data to discover hidden knowledge of complex biological systems has motivated the development of many tracking approaches. We have developed a new tracking approach that exploits information from multiple image scales and multiple time points, and directly combines the information in the optimization procedure. Our approach allows selecting an appropriate scale of a particle by using temporal information. Many-to-one and one-to-many associations are supported to deal with occlusion and deocclusion of particles. We have successfully applied our approach to real fluorescence microscopy image sequences displaying avian leukosis virus particles and quantified the performance.