Spectral Separation of Quantum Dots within Tissue Equivalent Phantom Using Linear Unmixing Methods in Multispectral Fluorescence Reflectance Imaging

Document Type : Original Paper

Authors

1 Ardabil University of Medical Sciences, Ardabil, Iran

2 Medical Physics and Biomedical Engineering Department, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran

3 Medical Physics and Biomedical Engineering Department, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.

4 Bio Optical Imaging Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran.

Abstract

Introduction
Non-invasive Fluorescent Reflectance Imaging (FRI) is used for accessing physiological and molecular processes in biological media. The aim of this article is to separate the overlapping emission spectra of quantum dots within tissue-equivalent phantom using SVD, Jacobi SVD, and NMF methods in the FRI mode.
Materials and Methods
In this article, a tissue-like phantom and an optical setup in reflectance mode were developed. The algorithm of multispectral imaging method was then written in Matlab environment. The setup included the diode-pumped solid-state lasers at 479 nm, 533 nm, and 798 nm, achromatic telescopic, mirror, high pass and low pass filters, and EMCCD camera. The FRI images were acquired by a CCD camera using band pass filter centered at 600 nm and high pass max at 615 nm for the first region and high pass filter max at 810 nm for the second region. The SVD and Jacobi SVD algorithms were written in Matlab environment and compared with a Non-negative Matrix Factorization (NMF) and applied to the obtained images.
Results
PSNR, SNR, CNR of SVD, and NMF methods were obtained as 39 dB, 30.1 dB, and 0.7 dB, respectively. The results showed that the difference of Jacobi SVD PSNR with PSNR of NMF and modified NMF algorithm was significant (p<0.0001). The statistical results showed that the Jacobi SVD was more accurate than modified NMF.
Conclusion
In this study, the Jacobi SVD was introduced as a powerful method for obtaining the unmixed FRI images. An experimental evaluation of the algorithm will be done in the near future.

Keywords

Main Subjects

References

  1. Robinove CJ. The logic of multispectral classification and mapping of land. Remote sensing of environment. 1981;11:231-44.
  2. Zimmermann T, Rietdorf J, Pepperkok R. Spectral imaging and its applications in live cell microscopy. FEBS Lett. 2003 Jul 3;546(1):87-92.
  3. Mansfield J R ,. Levenson R M. Distinguished photons: The Maestro™ in-vivo Fluorescence Imaging System, BioTechniques Protocol Guide. 2006 (2007) :49–62.
  4. Garini Y, Young IT, McNamara G. Spectral imaging: principles and applications. Cytometry A. 2006 Aug 1;69(8):735-47.
  5. Moghaddam B, Weiss Y,. Avidan S. Spectral bounds for sparse PCA: Exact and greedy lgorithms Advances in Neural Information Processing Systems. 2006:915–922.
  6. Zheng W S., Li S., Lai J., Liao S. On constrained sparse matrix factorization,”in Computer Vision, IEEE International Conference on (ICCV). 2007 Oct:1–8.
  7. Nocedal J. Wright S J. Numerical Optimization. Springer, 1999.
  8. Han H. Nonnegative principal component analysis for mass spectral serum profiles and biomarker discovery. BMC Bioinformatics. 2010;11:S1.
  9. Dickinson ME, Bearman G, Tille S, Lansford R, Fraser SE. Multi-spectral imaging and linear unmixing add a whole new dimension to laser scanning fluorescence microscopy. Biotechniques. 2001 Dec;31(6):1272, 4-6, 8.
  10. Bouchard MB, MacLaurin SA, Dwyer PJ, Mansfield J, Levenson R, Krucker T. Technical considerations in longitudinal multispectral small animal molecular imaging. J Biomed Opt. 2007 Sep-Oct;12(5):051601.
  11. Mansfield JR, Hoyt C, Levenson RM. Visualization of microscopy-based spectral imaging data from multi-label tissue sections. Curr Protoc Mol Biol. 2008 Oct;Chapter 14:Unit 14 9.
  12. Tsurui H, Nishimura H, Hattori S, Hirose S, Okumura K, Shirai T. Seven-color fluorescence imaging of tissue samples based on Fourier spectroscopy and singular value decomposition. J Histochem Cytochem. 2000 May;48(5):653-62.
  13. Montcuquet AS, Herve L, Guyon L, Dinten JM, Mars JI, editors. Non-negative Matrix Factorization: A blind sources separation method to unmix fluorescence spectra. Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009 WHISPERS '09 First Workshop on; 2009 26-28 Aug. 2009.
  14. Montcuquet AS, Herve L, Navarro F, Dinten JM, Mars JI. Nonnegative matrix factorization: a blind spectra separation method for in vivo fluorescent optical imaging. J Biomed Opt. 2010 Sep-Oct;15(5):056009.
  15. Hejazi M, Stuker F, Vats D , Rudin M. Improving the accuracy of a solid spherical source radius and depth estimation using the diffusion equation in fluorescence reflectance mode. Biomed Eng  Online. 2010 June; 9:28.
  16. Demmel J, Kahan W. Accurate singular values of bidiagonal matrices. SIAM Journal on Scientific and Statistical Computing. 1990;11(5):873-912.
  17. Lee DD, Seung HS. Unsupervised learning by convex and conic coding. Advances in neural information processing systems. 1997:515-21.
  18. Tsurui H, Nishimura H, Hattori S, Hirose S, Okumura K, Shirai T. Seven-color fluorescence imaging of tissue samples based on Fourier spectroscopy and singular value decomposition. J Histochem Cytochem. 2000 May;48(5):653-62.
  19. Zimmermann T, Rietdorf J, Pepperkok R. Spectral imaging and its applications in live cell microscopy. FEBS Lett. 2003 Jul 3;546(1):87-92.
  20. Chaudhari AJ, Ahn S, Levenson R, Badawi RD, Cherry SR, Leahy RM. Excitation spectroscopy in multispectral optical fluorescence tomography: methodology, feasibility and computer simulation studies. Phys Med Biol. 2009 Aug 7;54(15):4687-704.
  21. Lee DD, Seung HS. Learning the parts of objects by non-negative matrix factorization. Nature. 1999 Oct 21;401(6755):788-91.
Iranian Journal of Medical Physics
Volume 9, Issue 3 - Serial Number 3
September and October 2012
Pages 177-182

Files

History

  • Receive Date: 02 October 2011
  • Revise Date: 23 January 2013
  • Accept Date: 15 March 2012

Share

How to cite

Statistics