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Journal of the Optical Society of America A

Journal of the Optical Society of America A

| OPTICS, IMAGE SCIENCE, AND VISION

  • Editor: Franco Gori
  • Vol. 28, Iss. 6 — Jun. 1, 2011
  • pp: 1204–1210

Recognition and classification of red blood cells using digital holographic microscopy and data clustering with discriminant analysis

Ran Liu, Dipak K. Dey, Daniel Boss, Pierre Marquet, and Bahram Javidi  »View Author Affiliations


JOSA A, Vol. 28, Issue 6, pp. 1204-1210 (2011)
http://dx.doi.org/10.1364/JOSAA.28.001204


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Abstract

We propose to apply statistical clustering algorithms on a three-dimensional profile of red blood cells (RBCs) obtained through digital holographic microscopy (DHM). We show that two classes of RBCs stored for 14 and 38 days can be effectively classified. Two-dimensional intensity images of these cells are virtually the same. DHM allows for measurement of the RBCs’ biconcave profile, resulting in a discriminative dataset. Two statistical clustering algorithms are compared. A model-based clustering approach classifies the pixels of an RBC and recognizes the RBC as either new or old based. The K-means algorithm is applied to the four-dimensional feature vector extracted from the RBC profile.

© 2011 Optical Society of America

OCIS Codes
(150.0150) Machine vision : Machine vision
(150.6910) Machine vision : Three-dimensional sensing
(150.1135) Machine vision : Algorithms
(090.1995) Holography : Digital holography

ToC Category:
Machine Vision

History
Original Manuscript: January 10, 2011
Revised Manuscript: March 29, 2011
Manuscript Accepted: April 19, 2011
Published: May 24, 2011

Virtual Issues
Vol. 6, Iss. 7 Virtual Journal for Biomedical Optics

Citation
Ran Liu, Dipak K. Dey, Daniel Boss, Pierre Marquet, and Bahram Javidi, "Recognition and classification of red blood cells using digital holographic microscopy and data clustering with discriminant analysis," J. Opt. Soc. Am. A 28, 1204-1210 (2011)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-28-6-1204


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References

  1. A. J. Berger, T. W. Koo, I. Itzkan, G. Horowitz, and M. S. Feld, “Multicomponent blood analysis by near-infrared Raman spectroscopy,” Appl. Opt. 38, 2916–2926 (1999). [CrossRef]
  2. A. R. Moradi, M. K. Ali, M. Daneshpanah, A. Anand, and B. Javidi, “Detection of calcium-induced morphological changes of living cells using optical traps,” IEEE Photonics J. 2, 775–783 (2010). [CrossRef]
  3. J. Laurie, D. Wyncoll, and C. Harrison, “New versus old blood—the debate continues,” Crit. Care 14, 130–131 (2010). [CrossRef] [PubMed]
  4. C. G. Koch, L. Li, D. I. Sessler, P. Figueroa, G. A. Hoeltge, T. Mihaljevic, E. H. Blackstone, “Duration of red-cell storage and complications after cardiac surgery,” N. Engl. J. Med. 358, 1229–1239 (2008). [CrossRef] [PubMed]
  5. I. Moon, M. Daneshpanah, B. Javidi, and A. Stern, “Automated three dimensional identification and tracking of micro/nano biological organisms by computational holographic microscopy,” Proc. IEEE 97, 990–1010 (2009). [CrossRef]
  6. B. Javidi, I. Moon, and S. Yeom, “Real-time 3D sensing and identification of microorganisms,” Opt. Photon. News Magazine 17, 16–21 (2006). [CrossRef]
  7. P. Marquet, B. Rappaz, E. Cuche, T. Colomb, Y. Emery, C. Depeursinge, and P. Magistretti, “Digital holography microscopy a non-invasive quantitative contrast imaging technique allowing visualization of living cells,” Opt. Lett. 30468–470(2005). [CrossRef] [PubMed]
  8. B. Javidi, I. Moon, S. Yeom, and E. Carapezza, “Three-dimensional imaging and recognition of microorganism using single-exposure on-line (SEOL) digital holography,” Opt. Express 13, 4492–4506 (2005). [CrossRef] [PubMed]
  9. T. Kreis, Handbook of Holographic Interferometry (Wiley, 2005).
  10. F. Dubois, L. Joannes, and J.-C. Legros, “Improved three-dimensional imaging with digital holography microscope using a partial spatial coherent source,” Appl. Opt. 38, 7085–7094(1999). [CrossRef]
  11. T. Nomura, S. Murata, E. Nitanai, and T. Numata, “Phase-shifting digital holography with a phase difference between orthogonal polarizations,” Appl. Opt. 45, 4873–4877 (2006). [CrossRef] [PubMed]
  12. Y. Frauel, T. Naughton, O. Matoba, E. Tahajuerce, and B. Javidi, “Three dimensional imaging and display using computational holographic imaging,” Proc. IEEE 94, 636–654 (2006). [CrossRef]
  13. Y. Zhang, G. Pedrini, W. Osten, and H. J. Tiziani, “Reconstruction of in-line digital holograms from two intensity measurements,” Opt. Lett. 29, 1787–1789 (2004). [CrossRef] [PubMed]
  14. P. Ferraro, S. De Nicola, G. Coppola, A. Finizio, D. Alfieri, and G. Pierattini, “Controlling image size as a function of distance and wavelength in Fresnel-transform reconstruction of digital holograms,” Opt. Lett. 29, 854–856 (2004). [CrossRef] [PubMed]
  15. L. Martinez and B. Javidi, “Synthetic aperture single-exposure on-axis digital holography,” Opt. Express 16, 161–169 (2008). [CrossRef]
  16. I. Moon and B. Javidi, “3D identification of stem cells by computational holographic imaging,” J. R. Soc. Interface 4, 305–313 (2007). [CrossRef] [PubMed]
  17. E. Cuche, P. Marquet, and C. Depeursinge, “Simultaneous amplitude and quantitative phase contrast microscopy by numerical reconstruction of Fresnel off-axis holograms,” Appl. Opt. 38, 6994–7001 (1999). [CrossRef]
  18. T. Colomb, E. Cuche, F. Charrière, J. Kühn, N. Aspert, P. Marquet, and C. Depeursinge, “Automatic procedure for aberration compensation in digital holographic microscopy and application to specimen shape compensation,” Appl. Opt. 45, 851–863 (2006). [CrossRef] [PubMed]
  19. B. Rappaz, A. Barbul, Y. Emery, R. Korenstein, C. Depeursinge, P. J. Magistretti, and P. Marquet, “Comparative study of human erythrocytes by digital holographic microscopy, confocal microscopy and coulter counter,” Cytometry Part A 73, 895–903(2008). [CrossRef]
  20. C. Fraley and A. E. Raftery, “Model-based clustering, discriminant analysis and density estimation,” J. Am. Stat. Assoc. 97, 611–631 (2002). [CrossRef]
  21. A. P. Dempster, N. M. Laird, and D. B. Rubin, “Maximum likelihood from incomplete data via the EM algorithm,” J. Royal Statistical Soc. B 39, 1–38 (1977).
  22. J. D. Banfield and A. E. Raftery, “Model-based Gaussian and non-Gaussian clustering,” Biometrics 49, 803–821 (1993). [CrossRef]
  23. S. Borah and M. K. Ghose, “Performance analysis of AIM-K-means & K-means in quality cluster generation,” J. Comp. 1, 175–178 (2009).
  24. J. B. MacQueen, “Some methods for classification and analysis of multivariate observations,” in Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability(University of California, 1967), pp. 281–297.
  25. C. Elkan, “Using the triangle inequality to accelerate k-means,” in Proceedings of the Twentieth International Conference on Machine Learning (ICML), 2003), pp. 147–153.
  26. G. Frahling and C. Sohler, “A fast k-means implementation using coresets,” in Proceedings of the Twenty-Second Annual Symposium on Computational Geometry (SoCG) (Association for Computing Machinery, 2006), pp. 135–143.
  27. J. A. Hartigan and M. A. Wong, “Algorithm AS 136: A K-means clustering algorithm,” J. Royal Statistical Soc. C 28, 100–108(1979). [CrossRef]
  28. T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y. Wu, “An efficient k-means clustering algorithm: Analysis and implementation,” IEEE Trans. Pattern Anal. Machine Intell. 24, 881–892 (2002). [CrossRef]
  29. S. Ghosh and D. K. Dey, “Clustering: a pervasive data analytic technique,” Multivariate Statistical Methods, A.Sengupta, ed., Macmillan Advanced Research Series (Macmillan, 2009), pp. 171–201.
  30. S. Ghosh and D. K. Dey, “Model based penalized clustering for multivariate data,” in Advances in Multivariate Statistical Methods, A.Sengupta, ed. (World Scientific, 2009), pp. 53–72. [CrossRef]

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