NDLI: Accurate estimation of microtubule dynamics using kymographs and variable-rate particle filters

Content Provider	IEEE Xplore Digital Library
Author	Smal, I. Grigoriev, I. Akhmanova, A. Niessen, W.J. Meijering, E.
Copyright Year	2009
Description	Author affiliation: Department of Cell Biology, Erasmus MC, Rotterdam, The Netherlands (Grigoriev, I.; Akhmanova, A.) \|\| Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus MC, Rotterdam, The Netherlands (Smal, I.; Niessen, W.J.; Meijering, E.)
Abstract	Studying intracellular dynamics is of major importance for understanding healthy life at the molecular level and for developing drugs to target disease processes. One of the key technologies to enable this research is the automated tracking and motion analysis of subcellular objects in microscopy image sequences. Contrary to common frame-by-frame tracking methods, two alternative approaches have been proposed recently, based on either Bayesian estimation or space-time segmentation, which better exploit the available spatiotemporal information. In this paper, we propose to combine the power of both approaches, and develop a new probabilistic method to segment the traces of the moving objects in kymograph representations of the image data. It is based on variable-rate particle filtering and uses multiscale trend analysis for estimation of the relevant kinematic parameters using the extracted traces. Experiments on realistic synthetically generated images as well as on real biological image data demonstrate the improved potential of the new method for the analysis of microtubule dynamics in vitro.
Starting Page	1012
Ending Page	1015
File Size	627233
Page Count	4
File Format	PDF
ISBN	9781424432967
ISSN	1557170X
DOI	10.1109/IEMBS.2009.5333350
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Particle filters Target tracking Image segmentation Drugs Diseases Pharmaceutical technology Space technology Motion analysis Microscopy Image sequences
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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