Split and Merge Data Association Filter for Dense Multi-target Tracking
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Multiple hypothesis tracking in microscopy images
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Some assignment problems arising from multiple target tracking
Mathematical and Computer Modelling: An International Journal
IEEE Transactions on Image Processing
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In biological studies, it is often required to track thousands of small particles in microscopic images to analyze underlying mechanisms of cellular and subcellular processes which may lead to better understanding of some disease processes. In this paper, we present an automatic particle tracking method and apply it for analyzing an essential subcellular process, namely clathrin mediated endocytosis using total internal reflection microscopy. Particles are detected by using image filters and subsequently Gaussian mixture models are fitted to achieve sub-pixel resolution. A multiple hypothesis based framework is designed to solve data association problems and handle splitting/merging events. The tracking method is demonstrated on synthetic data under different scenarios and applied to real data. We also show that, by equipping with a cell detection module, the method can be extended straightforwardly for segmenting cell images taken by two-photon excitation microscopy.