Identifying fusion events in fluorescence microscopy images

Authors:
W. J. Godinez;M. Lampe;S. Wörz;R. Eils;B. Müller;K. Rohr
Affiliations:
University of Heidelberg, BIOQUANT, IPMB, and DKFZ Heidelberg, Dept. Bioinformatics and Functional Genomics, Biomedical Computer Vision Group, Heidelberg, Germany;University of Heidelberg, Dept. of Virology, Heidelberg, Germany;University of Heidelberg, BIOQUANT, IPMB, and DKFZ Heidelberg, Dept. Bioinformatics and Functional Genomics, Biomedical Computer Vision Group, Heidelberg, Germany;University of Heidelberg, BIOQUANT, IPMB, and DKFZ Heidelberg, Dept. Bioinformatics and Functional Genomics, Biomedical Computer Vision Group, Heidelberg, Germany;University of Heidelberg, Dept. of Virology, Heidelberg, Germany;University of Heidelberg, BIOQUANT, IPMB, and DKFZ Heidelberg, Dept. Bioinformatics and Functional Genomics, Biomedical Computer Vision Group, Heidelberg, Germany
Venue:
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Year:
2009

Citing 4
Cited 0

Shape-and-Behavior Encoded Tracking of Bee Dances

IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning and Inferring Motion Patterns using Parametric Segmental Switching Linear Dynamic Systems

International Journal of Computer Vision
Efficient Annotation of Vesicle Dynamics Video Microscopy

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple particle tracking in 3-D+t microscopy: method and application to the tracking of endocytosed quantum dots

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

We are investigating the dynamical relationships exhibited by virus particles via fluorescence time-lapse microscopy. To obtain a quantitative description of each particle over time, these objects are tracked. To derive an explicit characterization of each particle as well as to identify interesting transient behaviors, the intensity over time of each particle needs to be analyzed. We have developed an approach based on hybrid stochastic systems for identifying behaviors of interest. We employ a hybrid particle filter for estimating the behavior of individual particles. The approach has been successfully applied to particles tracked in synthetic image sequences as well as in real image sequences displaying HIV-1 particles.