OSA's Digital Library

Journal of the Optical Society of America A

Journal of the Optical Society of America A


  • Editor: Franco Gori
  • Vol. 31, Iss. 4 — Apr. 1, 2014
  • pp: 677–684

Entropy-based clustering of embryonic stem cells using digital holographic microscopy

Ran Liu, Arun Anand, Dipak K. Dey, and Bahram Javidi  »View Author Affiliations

JOSA A, Vol. 31, Issue 4, pp. 677-684 (2014)

View Full Text Article

Enhanced HTML    Acrobat PDF (521 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



Embryonic stem (ES) cells are an important factor in the development of cell-based therapeutic strategies. In this work, the use of digital holographic interferometric microscopy and statistical identification for automatic discrimination of ES cells and fibroblast (FB) cells is discussed in detail. The proposed algorithm first reduces the complex data structure to lower dimensions. Then, based on asymptotic normality, model-based clustering and linear discriminant analysis are applied to the transformed data to obtain the classification between ES and FB cells. The proposed algorithm is robust because it does not depend on parametric assumptions and can be extended to the classification of other cell image data. Experimental results are presented to demonstrate the performance of the system.

© 2014 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
(100.4993) Image processing : Pattern recognition, Baysian processors

ToC Category:
Machine Vision

Original Manuscript: August 5, 2013
Revised Manuscript: December 23, 2013
Manuscript Accepted: January 16, 2014
Published: March 3, 2014

Virtual Issues
Vol. 9, Iss. 6 Virtual Journal for Biomedical Optics

Ran Liu, Arun Anand, Dipak K. Dey, and Bahram Javidi, "Entropy-based clustering of embryonic stem cells using digital holographic microscopy," J. Opt. Soc. Am. A 31, 677-684 (2014)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. D. Huangfu, K. Osafune, R. Maehr, W. Guo, A. Eijkelenboom, S. Chen, W. Muhlestein, and D. A. Melton, “Induction of pluripotent stem cells from primary human fibroblasts with only Oct4 and Sox2,” Nat. Biotechnol. 26, 1269–1275 (2008). [CrossRef]
  2. D. C. Colter, I. Sekiya, and D. J. Prockop, “Identification of a subpopulation of rapidly self renewing and multi potential adult stem cells in colonies of human marrow stromal cells,” Proc. Natl. Acad. Sci. USA 98, 7841–7845 (2001). [CrossRef]
  3. M. Pudlasa, D. A. C. Berrio, M. Votteler, S. Koch, S. Thude, H. Walles, and K. Schenke-Laylanda, “Non-contact discrimination of human bone marrow-derived mesenchymal stem cells and fibroblasts using Raman spectroscopy,” Med. Laser Appl. 26, 119–125 (2011). [CrossRef]
  4. D. B. Murphy, Fundamentals of Light Microscopy and Electronic Imaging (Wiley-Liss, 2001).
  5. U. Schnars and W. Jueptner, Digital Holography: Digital Hologram Recording, Numerical Reconstruction and Related Techniques (Springer, 2005).
  6. U. Schnars and W. Jueptner, “Digital recording and numerical reconstruction of holograms,” Meas. Sci. Technol. 13, R85–R101 (2002). [CrossRef]
  7. T. Kreis, Handbook of Holographic Interferometry (Wiley, 2005).
  8. J. W. Goodman and R. W. Lawrence, “Digital image formation from electronically detected holograms,” Appl. Phys. Lett. 11, 77–79 (1967). [CrossRef]
  9. T. Zhang and I. Yamaguchi, “Three-dimensional microscopy with phase-shifting digital holography,” Opt. Lett. 23, 1221–1223 (1998). [CrossRef]
  10. E. Cuche, F. Bevilacqua, and C. Depeursinge, “Digital holography for quantitative phase-contrast imaging,” Opt. Lett. 24, 291–294 (1999). [CrossRef]
  11. P. Marquet, B. Rappaz, P. J. Magistretti, E. Cuche, Y. Emery, T. Colomb, and C. Depeursinge, “Digital holographic microscopy: a noninvasive contrast imaging technique allowing quantitative visualization of living cells with subwavelength axial accuracy,” Opt. Lett. 30, 468–470 (2005). [CrossRef]
  12. I. K. Moon, M. Daneshpanah, A. Stern, and B. Javidi, “Automated three-dimensional identification and tracking of micro/nano biological organisms by computational holographic microscopy,” Proc. IEEE 97, 990–1010 (2009). [CrossRef]
  13. Y. Frauel, T. Naughton, O. Matoba, E. Tahajuerce, and B. Javidi, “Three dimensional imaging and processing using computational holographic imaging,” Proc. IEEE 94, 636–653 (2006). [CrossRef]
  14. A. Anand, V. Chhaniwal, and B. Javidi, “Real-time digital holographic microscopy for phase contrast 3D imaging of dynamic phenomena,” J. Display Technol. 6, 500–505 (2010). [CrossRef]
  15. 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]
  16. P. Ferraro, S. Grilli, D. Alfieri, S. De Nicola, A. Finizio, G. Pierattini, B. Javidi, G. Coppola, and V. Striano, “Extended focused image in microscopy by digital holography,” Opt. Express 13, 6738–6749 (2005). [CrossRef]
  17. 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]
  18. G. Pedrini and H. J. Tiziani, “Short-coherence digital microscopy by use of a lensless holographic imaging system,” Appl. Opt. 41, 4489–4496 (2002). [CrossRef]
  19. B. Javidi and E. Tajahuerce, “Three dimensional object recognition using digital holography,” Opt. Lett. 25, 610–612 (2000). [CrossRef]
  20. A. Faridian, D. Hopp, G. Pedrini, and W. Osten, “Nanoscale imaging using deep ultraviolet digital holography,” Opt. Express 18, 14159–14164 (2010). [CrossRef]
  21. U. Gopinathan, G. Pedrini, and W. Osten, “Coherence effects in digital in-line holographic microscopy,” J. Opt. Soc. Am. A 25, 2459–2466 (2008). [CrossRef]
  22. A. Anand and B. Javidi, “Three dimensional microscopy with single beam wavefront sensing and reconstruction from volume speckle fields,” Opt. Lett. 35, 766–768 (2010). [CrossRef]
  23. D. Shin, M. Daneshpanah, A. Anand, and B. Javidi, “Optofluidic system for three dimensional sensing and identification of micro-organisms with digital holographic microscopy,” Opt. Lett. 35, 4066–4068 (2010). [CrossRef]
  24. A. Anand, V. K. Chhaniwal, and B. Javidi, “Imaging embryonic stem cell dynamics using quantitative 3D digital holographic microscopy,” IEEE Photon. J. 3, 546–554 (2011). [CrossRef]
  25. I. Moon and B. Javidi, “Three dimensional identification of stem cells computational holographic imaging,” J. R. Soc. Interface 4, 305–313 (2007). [CrossRef]
  26. A. El Mallahi, C. Minetti, and F. Dubois, “Automated three-dimensional detection and classification of living organisms using digital holographic microscopy with partial spatial coherent source: application to the monitoring of drinking-water resources,” Appl. Opt. 52, A68–A80 (2013). [CrossRef]
  27. J. W. Goodman, Introduction to Fourier Optics (McGraw-Hill, 1996).
  28. R. J. Serfling, Approximate Theorems of Mathematical Statistics (Wiley, 1980).
  29. T. M. Cover and J. A. Thomas, Elements of Information Theory (Wiley, 1991).
  30. H. Joe, “Estimation of entropy and other functionals of a multivariate density,” Ann. Inst. Stat. Math. 41, 683–697 (1989). [CrossRef]
  31. P. Hall and S. C. Morton, “On the estimation of entropy,” Ann. Inst. Stat. Math. 45, 69–88 (1993). [CrossRef]
  32. E. Parzen, “On estimation of a probability density function and mode,” Ann. Math. Stat. 33, 1065–1076 (1962). [CrossRef]
  33. A. K. Gangopadhyay, R. Disario, and D. K. Dey, “A nonparametric approach to k-sample inference based on entropy,” J. Nonparametr. Stat. 8, 237–252 (1997). [CrossRef]
  34. C. Fraley and A. E. Raftery, “Model-based clustering, discriminant analysis and density estimation,” J. Am. Stat. Assoc. 97, 611–631 (2002). [CrossRef]
  35. A. P. Dempster, N. M. Laird, and D. B. Rubin, “Maximum likelihood from incomplete data via the EM algorithm,” J. R. Stat. Soc. Ser. B. Methodol. 39, 1–38 (1977).
  36. G. Schwarz, “Estimating the dimension of a model,” Ann. Stat. 6, 461–464 (1978). [CrossRef]
  37. J. D. Banfield and A. E. Raftery, “Model-based Gaussian and non-Gaussian clustering,” Biometrics 49, 803–821 (1993). [CrossRef]
  38. R. E. Kass and A. E. Raftery, “Bayes factors,” J. Am. Stat. Assoc. 90, 773–795 (1995). [CrossRef]
  39. R. A. Fisher, “The use of multiple measurements in taxonomic problems,” Ann. Eugen. 7, 179–188 (1936). [CrossRef]
  40. W. N. Venables and B. D. Ripley, Modern Applied Statistics with S (Springer, 2002).
  41. T. Duong, “ks: Kernel smoothing. R package version 1.8.3.,” 2011, http://CRAN.R-project.org/package=ks .
  42. R Development Core Team, “R: A Language and Environment for Statistical Computing,” Vienna, Austria. {ISBN} 3-900051-07-0, R. Foundation for Statistical Computing, 2011, http://www.R-project.org/ .
  43. C. Fraley and A. E. Raftery, “MCLUST version 3 for R: normal mixture modeling and model-based clustering,” (Department of Statistics, University of Washington, 2006, revised 2009).

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited