Abstract
Recently, we have proposed a blind source separation algorithm to separate dyes in multiply labeled fluorescence microscopy images. Applying the algorithm, we are able to successfully extract the dye distributions from the images. It thereby solves an often challenging problem since the recorded emission spectra of fluorescent dyes are environment and instrument specific. The separation algorithm is based on nonnegative matrix factorization in a Poisson noise model and works well on many samples. In some cases, however, additional cost function terms such as sparseness enhancement are necessary to arrive at a satisfactory decomposition.
In this contribution we analyze the algorithm on two very well controlled real data sets. In the first case, known sources are artificially mixed in varying mixing conditions. In the second case, fluorescent beads are used to generate well behaved mixing situations. In both cases we can successfully extract the original sources. We discuss how the separation is influenced by the weight of the additional cost function terms, thereby illustrating that BSS can be be vastly improved by invoking qualitative knowledge about the nature of the sources.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Araki, S., Mukai, R., Makino, S., Nishikawa, T., Saruwatari, H.: The fundamental limitation of frequency domain blind source separation for convolutive mixtures of speech. IEEE Transactions on Speech and Audio Processing 11(2), 109–116 (2003)
Delorme, A., Makeig, S.: Eeglab: an open source toolbox for analysis of single-trial eeg dynamics. Journal of Neuroscience Methods 134, 9–21 (2004)
Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 40, 788–791 (1999)
Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: Proc. NIPS 2000, vol. 13, pp. 556–562. MIT Press, Cambridge (2001)
McKeown, M.J., Makeig, S., Brown, G.G., Jung, T.P., Bell, A.J., Kindermann, S.S., Sejnowski, T.: Analysis of fMRI data by blind separation into independent spatial components. Human Brain Mapping 6, 160–188 (1998)
Neher, R.A., Mitkovski, M., Kirchhoff, F., Neher, E., Theis, F.J., Zeug, A.: Blind source separation techniques for the decomposition of multiply labeled fluorescence images. Biophysical Journal (accepted, 2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Theis, F.J., Neher, R., Zeug, A. (2009). Blind Decomposition of Spectral Imaging Microscopy: A Study on Artificial and Real Test Data. In: Adali, T., Jutten, C., Romano, J.M.T., Barros, A.K. (eds) Independent Component Analysis and Signal Separation. ICA 2009. Lecture Notes in Computer Science, vol 5441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00599-2_69
Download citation
DOI: https://doi.org/10.1007/978-3-642-00599-2_69
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00598-5
Online ISBN: 978-3-642-00599-2
eBook Packages: Computer ScienceComputer Science (R0)