Abstract
Modern live cell fluorescence microscopy imaging systems, used abundantly for studying intra-cellular processes in vivo, generate vast amounts of noisy image data that cannot be processed efficiently and accurately by means of manual or current computerized techniques. We propose an improved tracking method, built within a Bayesian probabilistic framework, which better exploits temporal information and prior knowledge. Experiments on simulated and real fluorescence microscopy image data acquired for microtubule dynamics studies show that the technique is more robust to noise, photobleaching, and object interaction than common tracking methods and yields results that are in good agreement with expert cell biologists.
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References
Meijering, E., Smal, I., Danuser, G.: Tracking in molecular bioimaging. IEEE Signal Process. Mag. 23(3), 46–53 (2006)
Gerlich, D., Mattes, J., Eils, R.: Quantitative motion analysis and visualization of cellular structures. Methods 29(1), 3–13 (2003)
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)
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)
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, 1372–1383 (2005)
Doucet, A., de Freitas, N., Gordon, N.: Sequential Monte Carlo Methods in Practice. Springer-Verlag, Berlin (2001)
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, 174–188 (2002)
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, IEEE Computer Society Press, Los Alamitos (2007)
Isard, M., Blake, A.: CONDENSATION – Conditional density propagation for visual tracking. Int. J. Comput. Vis. 29, 5–28 (1998)
Doucet, A., Godsill, S., Andrieu, C.: On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing 10, 197–208 (2000)
Klaas, M., de Freitas, N., Doucet, A.: Toward practical N 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)
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)
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)
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)
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)
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, 1805–1819 (2005)
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Smal, I., Draegestein, K., Galjart, N., Niessen, W., Meijering, E. (2007). Rao-Blackwellized Marginal Particle Filtering for Multiple Object Tracking in Molecular Bioimaging. In: Karssemeijer, N., Lelieveldt, B. (eds) Information Processing in Medical Imaging. IPMI 2007. Lecture Notes in Computer Science, vol 4584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73273-0_10
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DOI: https://doi.org/10.1007/978-3-540-73273-0_10
Publisher Name: Springer, Berlin, Heidelberg
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