Open Access Open Access  Restricted Access Subscription or Fee Access

Blind Source Separation Based on ICA Algorithm Applied to Multispectral Fluorescence Imaging


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/irecos.v11i3.8643

Abstract


Immunofluorescence is one of the most used techniques in optical fluorescence microscopy and has substantial applications in biology and pathology. It aims to detect and localize one or more proteins thanks to the use of specific dyes. One of the major issues with immunofluorescence is the intrinsic fluorescence, called auto-fluorescence present in some biological specimen. Several approaches, based on blind source deconvolution, are developed to deal with this problem and to isolate the extrinsic fluorescence from the intrinsic one. In this paper, we present two Independent Component Algorithms based on second order and higher order statistics. Experimental results have revealed that the blind identification algorithm based on second-order statistics is more adaptedly fit to fluorescence sources un-mixing problem than algorithms based on higher-order statistics.
Copyright © 2016 Praise Worthy Prize - All rights reserved.

Keywords


Blind Source Separation; Fluorescent Multispectral Images; LMM Model; Second Order Statistics; Higher Order Statistics

Full Text:

PDF


References


G. P. C. Drummen. Fluorescent Probes and Fluorescence (Microscopy) Techniques-Illuminating Biological and Biomedical Research, Molecules, Vol.17,2012, pp. 14067-14090.
http://dx.doi.org/10.3390/molecules171214067

M. J. Ahrens and A. T. Dudley. Chemical pre-treatment of growth plate cartilage increases immunofluorescence sensitivity, J. Histochem. Cytochem, Vol. 59, 2011, pp. 408-418.
http://dx.doi.org/10.1369/0022155411400869

F. Humpert, I. Yahiatène, M. Lummer, M. Sauer, T. Huser. Quantifying molecular colocalization in live cell fluorescence microscopy, J Biophotonics, 2013, pp. 1-9.
http://dx.doi.org/10.1002/jbio.201300146

J. C. Waters. Accuracy and precision in quantitative fluorescence microscopy, Journal of Cell Biology, Vol. 185,2009, pp. 1135-1148.
http://dx.doi.org/10.1083/jcb.200903097

I. Odell and D.Cook, Immunofluorescence Techniques, Journal of Investigative Dermatology Vol.133, 2013, pp. 1-4.
http://dx.doi.org/10.1038/jid.2012.455

M. Monici, Cell and tissue autofluorescence research and diagnostic applications,Biotechnol Ann Rev, Vol.11,2005, pp. 227-256.
http://dx.doi.org/10.1016/s1387-2656(05)11007-2

C. Vandelest, E. Versteeg, J. Veerkamp, and T. Vankuppevelt, Elimination of autofluorescence in immunofluorescence microscopy with digital image processing,J. Histochem. Cytochem,Vol.43, 1995, pp.727–730.
http://dx.doi.org/10.1177/43.7.7608528

D. Wood, G. Feke, D. Vizard, and R. Papineni, Refining epifluorescence imaging and analysis with automated multiple-band flat-field correction,Nat. Methods, Vol.5, 2008, pp. i–ii.

H. Xu and B.W. Rice, In vivo fluorescence imaging with a multivariate curve resolution spectral unmixing technique, J. Biomed. Opt. Vol.14, 2009, pp: 064011-1- 064011-9.
http://dx.doi.org/10.1117/1.3258838

T. Pengo, A. Muñoz-Barrutia, I. Zudaire, C. Ortiz-de-Solorzano, Efficient Blind Spectral Unmixing of Fluorescently Labeled Samples Using Multi-Layer Non-Negative Matrix Factorization, PLoS, Vol.8,2013, pp.1-11.
http://dx.doi.org/10.1371/journal.pone.0078504

F. Woolfe, M. Gerdes, M. Bello, X. Tao, and A.Can, Autofluorescence Removal by Non-Negative Matrix Factorization, IEEE Transactions on image processing, Vol. 20, 2011; pp.1085-1093.
http://dx.doi.org/10.1109/tip.2010.2079810

H.Pu, G.Zhang, W.He, F.Liu, H.Guang, Y.Zhang, J. Bai, J.Luo. Resolving fluorophores by unmixing multispectral fluorescence tomography with independent component analysis, Phys Med Bio,Vol. 17, 2014.
http://dx.doi.org/10.1088/0031-9155/59/17/5025

Lakowicz, J.R., Principles of Fluorescence Spectroscopy, Third Edition, Springer, 2006.
http://dx.doi.org/10.1117/1.2904580

J.Mitra ,R. Jolivot , F.Marzani, P.Vabres, Source separation on hyperspectral cube applied to dermatology, SPIE. SPIE Medical Imaging meeting, 2010, pp.762431-1, 762431-11.
http://dx.doi.org/10.1117/12.844044

A.S. Montcuquet, L. Hervé, F. Navarro, J.M. Dinten, and J.I. Mars, Nonnegative matrix factorization: a blind spectra separation method for in vivo fluorescent optical imaging, J. Biomed. Opt., Vol. 15, 2010, pp. 1–14.
http://dx.doi.org/10.1117/1.3491796

L. Tong, R.Liu, V.Soon, and Y.Huang, Indeterminacy and identifiability of blind identification, IEEE Transactions on Circuits and Systems, Vol. 38, 1991, pp. 499–509.
http://dx.doi.org/10.1109/31.76486

Hyv¨arinen, A., Karhunen, J., and Oja, E., Independent Component Analysis, JohnWiley, New York, 2001.

L. Tong, R. Liu, V. Soon, and Y. Huang, Indeterminacy and identifiability of blind identification, IEEE Transactions on Circuits and Systems, vol. 38, pp. 499–509, 1991.
http://dx.doi.org/10.1109/31.76486

J.-F. Cardoso, Iterative techniques for blind source separation using only fourth-order cumulants, in Proc. EUSIPCO, Brussels, Belgium, 1992, pp. 739–742.

C.-J. Lin. Projected Gradient Methods for Non-negative Matrix Factorization,Neural Computation, Vol. 19, 2007.
http://dx.doi.org/10.1162/neco.2007.19.10.2756

J. Maclaren, O. Speck, D. Stucht, P. Schulze, J. Hennig& M. Zaitsev. Navigator Accuracy Requirements for Prospective Motion Correction,Magnetic Resonance in Medicine, Vol. 63, 2010, pp.162_170.
http://dx.doi.org/10.1002/mrm.22191

P. O. Hoyer. Non-negative matrix factorization with sparseness constraints,Journal of Machine Learning Research, Vol. 5, 2004, pp.1457_1469.

A. .Elhafid, D. Nuzillard, M.F. Devaux, N. Petrochilos, and F. Belloir, Extraction des signatures de composéspursconstituant la coucheexterne du grain d'orge à partird'images de fluorescence, Actes of the 20eCollogueGRETSI on signal and image processing, 2005.

R. Ruiz , R. Vázquez , R. Ranta , L. Leija, Analysis of 5 source separation algorithms on simulated EEG signals, Conference on Computing, Information Technology and Biomedical, 2008, Mexico. pp.CDROM.

L. B. Almeida. MISEP-Linear and NonLinear ICA Based on Mutual Information, Journal of Machine Learning Research, Vol. 4,2003, pp. 1297-1318.


Refbacks

  • There are currently no refbacks.



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize