Conditional random fields for object and background estimation in fluorescence video-microscopy

Authors:
T. Pécot;A. Chessel;S. Bardin;J. Salamero;P. Bouthemy;C. Kervrann
Affiliations:
INRIA, Centre Rennes, Bretagne Atlantique, Rennes and INRIA, Mathématiques et informatique appliquées, Jouy-en-Josas and;INRIA, Centre Rennes, Bretagne Atlantique, Rennes and “Cell and Tissue Imaging Facility”, IBISA, Institut Curie, Paris;INRIA, CNRS, Institut Curie, Paris;“Cell and Tissue Imaging Facility”, IBISA, Institut Curie, Paris and UMR 144 CNRS, Institut Curie, Paris;INRIA, Centre Rennes, Bretagne Atlantique, Rennes;INRIA, Centre Rennes, Bretagne Atlantique, Rennes and INRIA, Mathématiques et informatique appliquées
Venue:
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Year:
2009

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Abstract

This paper describes an original method to detect XFP-tagged proteins in time-lapse microscopy. Non-local measurements able to capture spatial intensity variations are incorporated within a Conditional Random Field (CRF) framework to localize the objects of interest. The minimization of the related energy is performed by a min-cut/max-flow algorithm. Furthermore, we estimate the slowly varying background at each time step. The difference between the current image and the estimated background provides new and reliable measurements for object detection. Experimental results on simulated and real data demonstrate the performance of the proposed method.