A Multiple Model Probability Hypothesis Density Tracker for Time-Lapse Cell Microscopy Sequences

  • Seyed Hamid Rezatofighi
  • Stephen Gould
  • Ba-Ngu Vo
  • Katarina Mele
  • William E. Hughes
  • Richard Hartley
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7917)


Quantitative analysis of the dynamics of tiny cellular and subcellular structures in time-lapse cell microscopy sequences requires the development of a reliable multi-target tracking method capable of tracking numerous similar targets in the presence of high levels of noise, high target density, maneuvering motion patterns and intricate interactions. The linear Gaussian jump Markov system probability hypothesis density (LGJMS-PHD) filter is a recent Bayesian tracking filter that is well-suited for this task. However, the existing recursion equations for this filter do not consider a state-dependent transition probability matrix. As required in many biological applications, we propose a new closed-form recursion that incorporates this assumption and introduce a general framework for particle tracking using the proposed filter. We apply our scheme to multi-target tracking in total internal reflection fluorescence microscopy (TIRFM) sequences and evaluate the performance of our filter against the existing LGJMS-PHD and IMM-JPDA filters.


Gaussian Mixture Model Particle Tracking Jump Markov System Multiple Object Tracking Probabilistic Hypothesis Density 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Rezatofighi, S.H., Gould, S., Hartley, R., Mele, K., Hughes, W.E.: Application of the IMM-JPDA filter to multiple target tracking in total internal reflection fluorescence microscopy images. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part I. LNCS, vol. 7510, pp. 357–364. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  2. 2.
    Yang, L., Qiu, Z., Greenaway, A., Lu, W.: A new framework for particle detection in low-SNR fluorescence live-cell images and its application for improved particle tracking. IEEE Trans. Biomed. Eng. 59(7), 2040–2050 (2012)CrossRefGoogle Scholar
  3. 3.
    Feng, L., Xu, Y., Yang, Y., Zheng, X.: Multiple dense particle tracking in fluorescence microscopy images based on multidimensional assignment. J. Struct. Biol. 173(2), 219–228 (2011)CrossRefGoogle Scholar
  4. 4.
    Yuan, L., Zheng, Y.F., Zhu, J., Wang, L., Brown, A.: Object tracking with particle filtering in fluorescence microscopy images: Application to the motion of neurofilaments in axons. IEEE Trans. Med. Imag. 31(1), 117–130 (2012)CrossRefGoogle Scholar
  5. 5.
    Smal, I., Meijering, E., Draegestein, K., Galjart, N., Grigoriev, I., Akhmanova, A., Van Royen, M., Houtsmuller, A., Niessen, W.: Multiple object tracking in molecular bioimaging by rao-blackwellized marginal particle filtering. Med. Image Anal. 12(6), 764–777 (2008)CrossRefGoogle Scholar
  6. 6.
    Smal, I., Draegestein, K., Galjart, N., Niessen, W., Meijering, E.: Particle filtering for multiple object tracking in dynamic fluorescence microscopy images: Application to microtubule growth analysis. IEEE Trans. Med. Imag. 27(6) (2008)Google Scholar
  7. 7.
    Wood, T., Yates, C., Wilkinson, D., Rosser, G.: Simplified multitarget tracking using the PHD filter for microscopic video data. IEEE Trans. Circ. Syst. Vid. 22(5), 702–713 (2012)CrossRefGoogle Scholar
  8. 8.
    Juang, R., Levchenko, A., Burlina, P.: Tracking cell motion using GM-PHD. In: Proc. ISBI, pp. 1154–1157 (2009)Google Scholar
  9. 9.
    Vo, B.N., Singh, S., Doucet, A.: Sequential monte carlo methods for multitarget filtering with random finite sets. IEEE Trans. Aerosp. Electron. Syst. 41(4) (2005)Google Scholar
  10. 10.
    Mahler, R.: Multitarget bayes filtering via first-order multitarget moments. IEEE Trans. Aerosp. Electron. Syst. 39(4), 1152–1178 (2003)CrossRefGoogle Scholar
  11. 11.
    Maggio, E., Taj, M., Cavallaro, A.: Efficient multitarget visual tracking using random finite sets. IEEE Trans. Circ. Syst. Vid. 18(8), 1016–1027 (2008)CrossRefGoogle Scholar
  12. 12.
    Pasha, S., Vo, B.N., Tuan, H., Ma, W.: A gaussian mixture PHD filter for jump markov system models. IEEE Trans. Aerosp. Electron. Syst. 45(3), 919–936 (2009)CrossRefGoogle Scholar
  13. 13.
    Vo, B.N., Ma, W.: The gaussian mixture probability hypothesis density filter. IEEE Trans. Signal Process. 54(11), 4091–4104 (2006)CrossRefGoogle Scholar
  14. 14.
    Keller, P., Pampaloni, F., Lattanzi, G., Stelzer, E.: Three-dimensional microtubule behavior in xenopus egg extracts reveals four dynamic states and state-dependent elastic properties. Biophys. J. 95(3), 1474–1486 (2008)CrossRefGoogle Scholar
  15. 15.
    Cohen, A., Gomes, F., Roysam, B., Cayouette, M.: Computational prediction of neural progenitor cell fates. Nature Methods 7(3), 213–218 (2010)CrossRefGoogle Scholar
  16. 16.
    Panta, K., Clark, D., Vo, B.N.: Data association and track management for the gaussian mixture probability hypothesis density filter. IEEE Trans. Aerosp. Electron. Syst. 45(3), 1003–1016 (2009)CrossRefGoogle Scholar
  17. 17.
    Burchfield, J., Lopez, J., Mele, K., Vallotton, P., Hughes, W.: Exocytotic vesicle behaviour assessed by total internal reflection fluorescence microscopy. Traffic 11, 429–439 (2010)CrossRefGoogle Scholar
  18. 18.
    Rezatofighi, S.H., Hartley, R., Hughes, W.: A new approach for spot detection in total internal reflection fluorescence microscopy. In: Proc. ISBI, pp. 860–863 (2012)Google Scholar
  19. 19.
    Rezatofighi, S.H., Pitkeathly, W., Gould, S., Hartley, R., Mele, K., Hughes, W., Burchfield, J.: A framework for generating realistic synthetic sequences of total internal reflection fluorescence microscopy images. In: Proc. ISBI (2013)Google Scholar
  20. 20.
    Ristic, B., Vo, B.N., Clark, D., Vo, B.T.: A metric for performance evaluation of multi-target tracking algorithms. IEEE Trans. Signal Process. 59(7) (2011)Google Scholar
  21. 21.
    Vo, B.T., Vo, B.N., Cantoni, A.: Analytic implementations of the cardinalized probability hypothesis density filter. IEEE Trans. Signal Process. 55(7) (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Seyed Hamid Rezatofighi
    • 1
    • 2
  • Stephen Gould
    • 1
  • Ba-Ngu Vo
    • 3
  • Katarina Mele
    • 2
  • William E. Hughes
    • 4
    • 5
  • Richard Hartley
    • 1
    • 6
  1. 1.College of Engineering & Computer Sci.Australian National UniversityAustralia
  2. 2.CSIRO Math., Informatics & StatisticsQuantitative Imaging GroupAustralia
  3. 3.Department of Electrical and Computer EngineeringCurtin UniversityAustralia
  4. 4.The Garvan Institute of Medical ResearchAustralia
  5. 5.Department of MedicineSt. Vincent’s HospitalAustralia
  6. 6.National ICT (NICTA)Australia

Personalised recommendations