Patch-Based Markov Models for Event Detection in Fluorescence Bioimaging

  • Thierry Pécot
  • Charles Kervrann
  • Sabine Bardin
  • Bruno Goud
  • Jean Salamero
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5242)


The study of protein dynamics is essential for understanding the multi-molecular complexes at subcellular levels. Fluorescent Protein (XFP)-tagging and time-lapse fluorescence microscopy enable to observe molecular dynamics and interactions in live cells, unraveling the live states of the matter. Original image analysis methods are then required to process challenging 2D or 3D image sequences. Recently, tracking methods that estimate the whole trajectories of moving objects have been successfully developed. In this paper, we address rather the detection of meaningful events in spatio-temporal fluorescence image sequences, such as apparent stable “stocking areas” involved in membrane transport. We propose an original patch-based Markov modeling to detect spatial irregularities in fluorescence images with low false alarm rates. This approach has been developed for real image sequences of cells expressing XFP-tagged Rab proteins, known to regulate membrane trafficking.


Markov Random Field False Alarm Probability Markov Random Field Model Real Image Sequence Gibbs Model 
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.

Supplementary material (3 mb)
Electronic Supplementary Material (3,075 KB)


  1. 1.
    Echard, A., Opdam, F., de Leeuw, H., Jollivet, F., Savelkoul, P., Hendriks, W., Voorberg, J., Goud, B., Fransen, J.: Alternative splicing of the human rab6a gene generates two close but functionally different isoforms. Molecular biology of the cell 11, 3819–3833 (2000)CrossRefGoogle Scholar
  2. 2.
    Anderson, C., Georgiou, G., Morrison, I., Stevenson, G., Cherry, R.: Tracking of cell surface receptors by fluorescence digital imaging microscopy using a charged-coupled device camera. low-density lipoprotein and influenza virus receptor mobility at 4 degrees c. Journal of Cell Science 101, 415–425 (1992)Google Scholar
  3. 3.
    Sbalzarini, I., Koumoutsakos, P.: Feature point tracking and trajectory analysis for video imaging in cell biology. Journal of Structural Biology 151, 182–195 (2005)CrossRefGoogle Scholar
  4. 4.
    Smal, I., Draegestein, K., Galjart, N., Niessen, W., Meijering, E.: Rao-blackwellized marginal particle filtering for multiple object tracking in molecular bioimaging. In: IPMI, pp. 110–121 (2007)Google Scholar
  5. 5.
    Genovesio, A., Liedl, T., Emiliani, V., Parak, W., Coppey-Moisan, M., Olivo-Marin, J.C.: Multiple particle tracking in 3D+t microscopy: Method and application to the tracking of endocytosed quantum dots. IEEE Trans. on IP 15(5), 1062–1070 (2006)Google Scholar
  6. 6.
    Thomann, D., Dorn, J., Sorger, P., Danuser, G.: Automatic fluorescent tag localization ii: improvement in super-resolution by relative tracking. Journal of Microscopy 211(3), 230–248 (2003)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Racine, V., Salamero, J., Fraisier, V., Trubuil, A., Sibarita, J.-B.: Visualization and quantification of vesicle trafficking on a three-dimensional cytoskeleton network in living cells. Journal of Microscopy 225, 213–227 (2007)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Besag, J.: Spatial interaction and the statistical analysis of lattice systems. Journal Royal Statistical Society 36, 192–236 (1974)MathSciNetzbMATHGoogle Scholar
  9. 9.
    Buades, A., Coll, B., Morel, J.: A review of image denoising algorithms, with a new one. Multiscale Modeling and Simulation 4(2), 490–530 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Roth, S., Black, M.J.: Fields of experts: A framework for learning image priors. In: Proc. of IEEE CVPR 2005, San Diego, USA, vol. 2, pp. 860–867 (June 2005)Google Scholar
  11. 11.
    Awate, S.P., Whitaker, R.T.: Unsupervised, information-theoretic, adaptive image filtering for image restoration. IEEE Trans. Pattern Anal. Mach. Intell. 28(3), 364–376 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Thierry Pécot
    • 1
    • 2
  • Charles Kervrann
    • 1
    • 2
  • Sabine Bardin
    • 3
  • Bruno Goud
    • 3
  • Jean Salamero
    • 3
    • 4
  1. 1.INRIA Rennes - Bretagne AtlantiqueRennes
  2. 2.INRA, UR341 Mathématiques et informatique appliquéesJouy-en-JosasFrance
  3. 3.UMR 144 CNRS - Institut CurieParisFrance
  4. 4.“Cell and Tissue Imaging Facility” IBISA, Institut CurieParisFrance

Personalised recommendations