Rao-blackwellized marginal particle filtering for multiple object tracking in molecular bioimaging

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4584 LNCS, Issue , 2007, Pages 110-121

  Smal, Ihor ^a   Draegestein, Katharina ^a   Galjart, Niels ^a   Niessen, Wiro ^a   Meijering, Erik ^a  

^a ERASMUS MEDICAL CENTER   (Netherlands)

Author keywords

Bayesian estimation; Fluorescence microscopy; Microtubule dynamics; Multiple object tracking; Particle filtering; Rao Blackwellization

Indexed keywords

BAYESIAN NETWORKS; DATA PROCESSING; FLUORESCENCE MICROSCOPY; IMAGING SYSTEMS; PHOTOBLEACHING;

BAYESIAN ESTIMATION; MARGINAL PARTICLE FILTERING; MICROTUBULE DYNAMICS; MOLECULAR BIOIMAGING; MULTIPLE OBJECT TRACKING; NOISY IMAGE DATA; RAO-BLACKWELLIZATION;

OBJECT RECOGNITION;

PROTEIN;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BAYES THEOREM; BIOLOGICAL MODEL; COMPUTER ASSISTED DIAGNOSIS; CONFOCAL MICROSCOPY; EVALUATION; FLUORESCENCE MICROSCOPY; IMAGE ENHANCEMENT; METHODOLOGY; MICROTUBULE; MOLECULAR BIOLOGY; PARTICLE SIZE; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL; ULTRASTRUCTURE;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MICROSCOPY, CONFOCAL; MICROSCOPY, FLUORESCENCE; MICROTUBULES; MODELS, BIOLOGICAL; MODELS, STATISTICAL; MOLECULAR BIOLOGY; PARTICLE SIZE; PATTERN RECOGNITION, AUTOMATED; PROTEINS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 34548409171     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-73273-0_10     Document Type: Conference Paper

References (16)

1. Meijering, E., Smal, I., Danuser, G.: Tracking in molecular bioimaging. IEEE Signal Process. Mag. 23(3), 46-53 (2006)
2. Gerlich, D., Mattes, J., Eils, R.: Quantitative motion analysis and visualization of cellular structures. Methods 29(1), 3-13 (2003)
3. Thomann, D., Rines, D.R., Sorger, P.K., Danuser, G.: Automatic fluorescent tag detection in 3D with super-resolution: Application to the analysis of chromosome movement. J. Microsc. 208(1), 49-64 (2002)
4. Cheezum, M.K., Walker, W.F., Guilford, W.H.: Quantitative comparison of algorithms for tracking single fluorescent particles. Biophys. J. 81(4), 2378-2388 (2001)
5. Sage, D., Neumann, F.R., Hediger, F., Gasser, S.M., Unser, M.: Automatic tracking of individual fluorescence particles: Application to the study of chromosome dynamics. IEEE Trans. Image Process. 14(9), 1372-1383 (2005)
6. Doucet, A., de Freitas, N., Gordon, N.: Sequential Monte Carlo Methods in Practice. Springer-Verlag, Berlin (2001)
7. Arulampalam, S.M., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Process. 50(2)174-188 (2002)
8. Smal, I., Niessen, W., Meijering, E.: Advanced particle filtering for multiple object tracking in dynamic fluorescence microscopy images. In: Proceedings of the IEEE International Symposium on Biomedical Imaging (April 2007)
9. Isard, M., Blake, A.: CONDENSATION - Conditional density propagation for visual tracking. Int. J. Comput. Vis. 29(1), 5-28 (1998)
10. Doucet, A., Godsill, S., Andrieu, C.: On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing 10, 197-208 (2000)
11. 2 Monte Carlo: The marginal particle filter. In: Proceedings of the 21th Annual Conference on Uncertainty in Artificial Intelligence (UAI-05) 2005, pp. 308-331. AUAI Press (2005)
12. Vermaak, J., Doucet, A., Pérez, P.: Maintaining multi-modality through mixture tracking. In: Proceedings of the 9th IEEE International Conference on Computer Vision 2003, pp. 1110-1116 (2003)
13. Stepanova, T., Slemmer, J., Hoogenraad, C.C., Lansbergen, G., Dortland, B., De Zeeuw, C.I., Grosveld, F., van Cappellen, G., Akhmanova, A., Galjart, N.: Visualization of microtubule growth in cultured neurons via the use of EB3-GFP (end-binding protein 3-green fluorescent protein). J. Neurosci. 23(7), 2655-2664 (2003)
14. Li, X.R., Jilkov, V.P.: Survey of maneuvering target tracking. Part I: Dynamic models. IEEE Trans. Aerosp. Electron. Syst. 39(4), 1333-1364 (2003)
15. Song, L., Hennink, E.J., Young, I.T., Tanke, H.J.: Photobleaching kinetics of fluorescein in quantitative fluorescence microscopy. Biophys. J. 68(6), 2588-2600 (1995)
16. Khan, Z., Balch, T., Dellaert, F.: MCMC-Based particle filtering for tracking a variable number of interacting targets. IEEE Trans. Pattern Anal. Machine Intell. 27(11), 1805-1819 (2005)