A Multiple Hypothesis Based Method for Particle Tracking and Its Extension for Cell Segmentation

  • Liang Liang
  • Hongying Shen
  • Panteleimon Rompolas
  • Valentina Greco
  • Pietro De Camilli
  • James S. Duncan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7917)


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.


Gaussian Mixture Model Multiple Hypothesis Clathrin Mediate Endocytosis Cell Segmentation Particle Tracking Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Liang Liang
    • 1
  • Hongying Shen
    • 1
  • Panteleimon Rompolas
    • 1
  • Valentina Greco
    • 1
  • Pietro De Camilli
    • 1
  • James S. Duncan
    • 1
  1. 1.Yale UniversityNew HavenUSA

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