Paper Info
Title
Tracking virus particles in fluorescence microscopy images using two-step multi-frame association
Abstract
Automatic fluorescent particle tracking is an essential task to study the dynamics of a large number of biological structures at a sub-cellular level. We have developed a two-step multi-frame association finding algorithm which is based on a temporally semi-global formulation as well as combines a spatially global and a spatially local approach. Using this multi-frame association finding algorithm we have developed a probabilistic tracking approach based on the Kalman filter. We have successfully applied the approach to synthetic as well as real microscopy image sequences of ALV virus particles and quantified the performance. We found that the proposed approach outperforms previous approaches.
Year
DOI
Venue
2012
10.1109/ISBI.2012.6235635
Biomedical Imaging
Keywords
Field
DocType
Kalman filters,biomedical optical imaging,cellular biophysics,fluorescence,image sequences,medical image processing,microorganisms,optical microscopy,probability,ALV virus particles,Kalman filter,automatic fluorescent particle tracking,avian leukosis virus particles,biological structures,fluorescence microscopy imaging,multiframe association finding algorithm,probabilistic tracking approach,real microscopy image sequences,subcellular level,temporally semiglobal formulation,two-step multiframe association,two-step multiframe association finding algorithm,virus particle tracking,Kalman filter,Virus particle tracking,multi-frame association
Fluorescence microscope,Computer vision,Cellular biophysics,Pattern recognition,Computer science,Signal-to-noise ratio,Kalman filter,Artificial intelligence,Microscopy,Probabilistic logic,Particle
Conference
ISSN
ISBN
Citations 
1945-7928
978-1-4577-1857-1
1
PageRank 
References 
Authors
0.36
0
5
Name
Order
Citations
PageRank
Astha Jaiswal161.85
William J. Godinez29110.98
Roland Eils364470.09
Maik J. Lehmann421.07
Karl Rohr534048.69