Patch-Based Markov Models for Event Detection in Fluorescence Bioimaging

Authors:
Thierry Pécot;Charles Kervrann;Sabine Bardin;Bruno Goud;Jean Salamero
Affiliations:
INRIA Rennes - Bretagne Atlantique, Rennes F-35042 and INRA, UR341 Mathématiques et informatique appliquées, Jouy-en-Josas, F-78352;INRIA Rennes - Bretagne Atlantique, Rennes F-35042 and INRA, UR341 Mathématiques et informatique appliquées, Jouy-en-Josas, F-78352;UMR 144 CNRS - Institut Curie, Paris, F-75248;UMR 144 CNRS - Institut Curie, Paris, F-75248;UMR 144 CNRS - Institut Curie, Paris, F-75248 and "Cell and Tissue Imaging Facility" IBISA, Institut Curie, Paris, F-75248
Venue:
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Year:
2008

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Abstract

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.