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Measuring Spatiotemporal Dependencies in Bivariate Temporal Random Sets with Applications to Cell Biology
September 2008 (vol. 30 no. 9)
pp. 1659-1671
ASCII Text
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Ester Díaz, Rafael Sebastian, Guillermo Ayala, María Elena Díaz, Roberto Zoncu, Derek Toomre, Stéphane Gasman, "Measuring Spatiotemporal Dependencies in Bivariate Temporal Random Sets with Applications to Cell Biology," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 9, pp. 1659-1671, September, 2008.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2007.70821,
author = {Ester Díaz and Rafael Sebastian and Guillermo Ayala and María Elena Díaz and Roberto Zoncu and Derek Toomre and Stéphane Gasman},
title = {Measuring Spatiotemporal Dependencies in Bivariate Temporal Random Sets with Applications to Cell Biology},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {30},
number = {9},
issn = {0162-8828},
year = {2008},
pages = {1659-1671},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2007.70821},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Measuring Spatiotemporal Dependencies in Bivariate Temporal Random Sets with Applications to Cell Biology
IS - 9
SN - 0162-8828
SP1659
EP1671
EPD - 1659-1671
A1 - Ester Díaz,
A1 - Rafael Sebastian,
A1 - Guillermo Ayala,
A1 - María Elena Díaz,
A1 - Roberto Zoncu,
A1 - Derek Toomre,
A1 - Stéphane Gasman,
PY - 2008
KW - Pattern analysis
KW - Stochastic processes
KW - Image models
KW - Video analysis
KW - Applications
KW - Biology and genetics
VL - 30
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Analyzing spatio-temporal dependencies between different types of events is highly relevant to numerous biological phenomena (e.g. signalling and trafficking) especially as advances in probes and microscopy have facilitated imaging of dynamic processes in living cells. For many types of events, the segmented areas can overlap spatially and temporally forming random clumps. In this paper, we model binary image sequences of two different event types as a realization of a bivariate temporal random set and propose a non-parametric approach to quantify spatial and spatio-temporal interrelations using the pair-correlation, cross-covariance and the Ripley ${\mathbb K}$ functions. Based on these summary statistics we propose a randomization procedure to test independence between event types by applying random toroidal shifts and Monte Carlo tests. A simulation study assessed the performance of the proposed estimators and showed that these statistics capture the spatio-temporal dependencies accurately. The estimation of the spatio-temporal interval of interactions was also obtained. The method was successfully applied to analyze the interdependencies of several endocytic proteins using image sequences of living cells and validated the procedure as a new way to automatically quantify dependencies between proteins in a formal and robust manner.

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Index Terms:
Pattern analysis, Stochastic processes, Image models, Video analysis, Applications, Biology and genetics
Citation:
Ester Díaz, Rafael Sebastian, Guillermo Ayala, María Elena Díaz, Roberto Zoncu, Derek Toomre, Stéphane Gasman, "Measuring Spatiotemporal Dependencies in Bivariate Temporal Random Sets with Applications to Cell Biology," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 9, pp. 1659-1671, Sept. 2008, doi:10.1109/TPAMI.2007.70821
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