![](bg8.png)
Figure 7: Results obtained by applying the NT-based ap-
proach on the sequence of Fig. 1. The arrows represent the
estimated OD pairs, and the corresponding colored num-
bers at the top right represent traffic proportions. Left: all
the central regions are imposed to be origin regions; right:
the central region is imposed to be an origin region.
sequence suits the biologicalknowledge about the OD
regions for the Rab6 trafficking. In our experiments,
the proportions of vesicles for the OD pairs given by
the NT procedure represent new tools for biologists.
It can be applied to understand other trafficking prob-
lems where many objects are moving. Actually, the
main limit is related to image partition yet, which can
be arbitrary. Indeed, although the expert defines the
centers of Voronoi cells with biological knowledge,
the segmentation remains very crude for representing
the regions of interest. Actually, the MIP map is the
only tool available to define these regions, but is not
enough accurate. For future work, it will be necessary
to apply the NT-based approach on more relevant re-
gions. A possible way is to extract the microtubule
network and consider it as a graph for applying the
NT procedure. Moreover, it is established that the flu-
orescence decreases with time, which is neglected in
our modeling since we exploit the difference of fluo-
rescence between two time steps. However, it is well
known that the vesicles diffuse also in the cytosol.
This could be considered in future work by introduc-
ing this phenomenon in the estimation process of the
data to improve the results.
REFERENCES
Anderson, C., Georgiou, G., Morrison, I., Stevenson, G.,
and Cherry, R. (1992). Tracking of cell surface re-
ceptors 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.
Barber, C., Dobkin, D., and Huhdanpaa, H. T. (1996). The
quickhull algorithm for convex hulls. ACM transac-
tions on mathematical software, 22(4):469–483.
Bechar, I. and Trubuil, A. (2006). A bidimensional sig-
nal processing approach to vesicle trafficking analy-
sis in 3d+t fluorescence videomicroscopy. Proc. of
the MICCAI Workshop on Microscopic Image Analy-
sis and Application to Biology.
Boulanger, J., Kervrann, C., and Bouthemy, P. (2006).
Estimation of dynamic background for fluorescence
video-microscopy. In Proc. of ICIP’2006, Atlanta.
Boyd, J. E., Meloche, J., and Vardi, Y. (1999). Statisti-
cal tracking in video traffic surveillance. In Proc. of
ICCV’99, volume 1, pages 163–168.
Candes, E. and Tao, T. (2007). The dantzig selector: statis-
tical estimation when p is much larger than n. Annals
of statistics, to appear.
Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., and
Diot, C. (2002). Traffic matrix estimation: existing
techniques and new directions. In SIGCOMM ’02:
Proc. Conf. on Applications, Technologies, Architec-
tures, and Protocols for Computer Communications,
pages 161–174, New York, USA.
P´ecot, T., Boulanger, J., Kervrann, C., and Bouthemy, P.
(2007). Network tomography for trafficking simula-
tion and analysis in fluorescence microscopy imaging.
In Proc. of IEEE ISBI’2007, pages 268–271, Arling-
ton.
Racine, V., Hertzog, A., Jouaneau, J., Salamero, J.,
Kervrann, C., and Sibarita, J. (2006). Mutliple target
tracking of 3d fluorescent objects based on simulated
annealing. In Proc. of IEEE ISBI’2006.
Santini, S. (2000). Analysis of traffic flow in urban areas
using web cameras. In Fifth IEEE Workshop on Appli-
cations of Computer Vision, volume 596, pages 140–
145.
Sbalzarini, I. and Koumoutsakos, P. (2005). Feature point
tracking and trajectory analysis for video imaging in
cell biology. Journal of Structural Biology, 151:182–
195.
Sibarita, J., Racine, V., and Salamero, J. (2006). Quantifi-
cation of membrane trafficking on a 3d cytoskeleton
network in living cells. In Proc. of IEEE ISBI’2006.
Smal, I., Niessen, W., and Meijering, E. (2007). Advanced
particle filtering for multiple object tracking in dy-
namic fluorescence microscopy images. In Proc. of
IEEE ISBI’2007, pages 1048–1051, Arlington.
Thomann, D., Dorn, J., Sorger, P., and Danuser, G. (2003).
Automatic fluorescent tag localization ii: improve-
ment in super-resolution by relative tracking. Journal
of Microscopy, 211(3):230–248.
Tibshirani, R. (1996). Regression shrinkage and selection
via the lasso. Journal of the Royal Statistical Society,
58:267–288.
Vardi, Y. (1996). Network tomography: Estimation
of source-destination traffic intensities from link
data. Journal of the American Statistical Association,
91(433):365–377.
NETWORK TOMOGRAPHY-BASED TRACKING FOR INTRACELLULAR TRAFFIC ANALYSIS IN
FLUORESCENCE MICROSCOPY IMAGING
161