Three-dimensional counting of morphologically normal human red blood cells via digital holographic microscopy

Journal of Biomedical Optics

Volumn 20, Issue 1, 2015, Pages

  Yi, Faliu ^a   Moon, Inkyu ^a   Lee, Yeon H ^b  

^a Chosun University   (South Korea)

^b Sungkyunkwan University   (South Korea)

Author keywords

cell analysis; digital holography; statistical analysis; three dimensional image processing

Indexed keywords

ALGORITHMS; BLOOD; CELLS; COMPUTATION THEORY; COMPUTER GENERATED HOLOGRAPHY; CYTOLOGY; DECISION THEORY; HEMOGLOBIN; HOLOGRAMS; HOLOGRAPHY; IMAGE PROCESSING; IMAGE RECONSTRUCTION; IMAGE SEGMENTATION; MICROSCOPIC EXAMINATION; PHASE MEASUREMENT; SPEECH RECOGNITION; STATISTICAL METHODS;

BAYESIAN DECISION THEORY; CELL ANALYSIS; DIGITAL HOLOGRAPHIC MICROSCOPY; DIGITAL HOLOGRAPHY; GAUSSIAN MIXTURE DENSITIES; THREE-DIMENSIONAL IMAGE PROCESSING; THREEDIMENSIONAL (3-D); WATERSHED TRANSFORM ALGORITHM;

PRINCIPAL COMPONENT ANALYSIS;

ALGORITHM; BAYES THEOREM; CLASSIFICATION; CYTOLOGY; ERYTHROCYTE; HOLOGRAPHY; HUMAN; IMAGE CYTOMETRY; MICROSCOPY; PROCEDURES;

ALGORITHMS; BAYES THEOREM; ERYTHROCYTES; HOLOGRAPHY; HUMANS; IMAGE CYTOMETRY; MICROSCOPY;

EID: 84922496782     PISSN: 10833668     EISSN: 15602281     Source Type: Journal    
DOI: 10.1117/1.JBO.20.1.016005     Document Type: Article

References (35)

1. B. Javidi, F. Okano, and J. Sun, 3D Imaging, Visualization, and Display Technologies, Springer, New York (2008).
2. W. Osten, T. Baumbach, and W. Juptner, "Comparative digital holography," Opt. Lett. 27, 1764-1766 (2002).
3. Y. Frauel et al., "Three dimensional imaging and display using computational holographic imaging," Proc. IEEE 94, 636-654 (2006).
4. S. Benton, Holographic Imaging, Wiley-Interscience, New York (2008).
5. I. Moon et al., "Automated three dimensional identification and tracking of micro/nano biological organisms by computational holographic microscopy," Proc. IEEE 97, 990-1010 (2009).
6. B. Javidi, I. Moon, and S. Yeom, "Real-time 3D sensing and identification of microorganisms," Opt. Photon. News Mag. 17, 16-21 (2006).
7. P. Langehanenberg et al., "Autofocusing in digital holographic phase contrast microscopy on pure phase objects for live cell imaging," Appl. Opt. 47, D176-D182 (2008).
8. J. Sheng, E. Malkiel, and J. Katz, "Digital holographic microscopy for measuring three-dimensional particle distributions and motions," Appl. Opt. 45, 3893-3901 (2006).
9. I. Moon and B. Javidi, "Shape tolerant three-dimensional recognition of biological microorganisms using digital holography," Opt. Exp. 13, 9612-9622 (2005).
10. R. Liu et al., "Recognition and classification of red blood cells using digital holographic microscopy and data clustering with discriminant analysis," J. Opt. Soc. Am. A 28(6), 1204-1210 (2011).
11. B. Rappaz et al., "Spatial analysis of erythrocyte membrane fluctuations by digital holographic microscopy," Blood Cells Mol. Dis. 42(3), 228-232 (2009).
12. Z. Schish et al., "Digital holographic microscopy-innovative and nondestructive analysis of living cells," Microsc. Sci. Technol. Appl. Educ. 4, 1055-1062 (2010).
13. Y. Jang, J. Jang, and Y. Park, "Dynamic spectroscopic phase microscopy for quantifying hemoglobin concentration and dynamic membrane fluctuation in red blood cells," Opt. Exp. 20(9), 9673-9681 (2012).
14. N. Gov and S. Safran, "Red blood cell membrane fluctuations and shape controlled by ATP-induced cytoskeletal defects," Biophysical 88, 1859-1874 (2005).
15. D. Boss et al., "Exploring red blood cell membrane dynamics with digital holographic microscopy," Proc. SPIE 7715, 77153l (2010).
16. W. Reinhart and S. Chien, "Red cell rheology in stomatocyte-echinocyte transformation: roles of cell geometry and cell shape," Blood 67(4), 1110-1118 (1986).
17. B. Bhaduri et al., "Optical assay of erythrocyte function in banked blood," Sci. Rep. 4, 6211 (2014).
18. J. Laurie, D. Wyncoll, and C. Harrison, "New versus old blood-the debate continues," Crit. Care 14, 130-131 (2010).
19. C. Koch et al., "Duration of red-cell storage and complications after cardiac surgery," N. Engl. J. Med. 358, 1229-1239 (2008).
20. L. Gerald, M. Wortis, and R. Mukhopadhyay, "Stomatocyte-discocyteechinocyte sequence of the human red blood cell: evidence for the bilayer-couple hypothesis from membrane mechanics," Proc. Natl. Acad. Sci. U. S. A. 99(26), 4457-4461 (2002).
21. K. Khairy, J. Foo, and J. Howard, "Shapes of red blood cells: comparison of 3D confocal images with the bilayer-couple model," Cell. Mol. Bioeng. 1(2-3), 173-181 (2008).
22. M. Battistelli et al., "Rhodiola rosea as antioxidant in red blood cells: ultrastructural and hemolytic behavior," Eur. J. Histochem. 49(3), 243-254 (2005).
23. T. Tishko, Holographic Microscopy of Phase Microscopic Objects: Theory and Practice, World Scientific, Maryland (2011).
24. 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).
25. T. Colomb et al., "Automatic procedure for aberration compensation in digital holographic microscopy and application to specimen shape compensation," Appl. Opt. 45, 851-863 (2006).
26. F. Yi, I. Moon, and Y. H. Lee, "Extraction of target specimens from bioholographic images using interactive graph cuts," J. Biomed. Opt. 18(12), 126015 (2013).
27. F. Yi et al., "Automated segmentation of multiple red blood cells with digital holographic microscopy," J. Biomed. Opt. 18(2), 026006 (2013).
28. I. Moon et al., "Automated statistical quantification of three-dimensional morphology and mean corpuscular hemoglobin of multiple red blood cells," Opt. Express 20(9), 10295-10309 (2012).
29. E. Gose and R. Johnsonbaugh, Pattern Recognition and Image Analysis, Prentice Hall, New Jersey (1996).
30. R. Gonzalez and R. Woods, Digital Imaging Processing, Prentice Hall, New Jersey (2002).
31. H. Ney, U. Essen, and R. Kneser, "On the estimation of 'small' probabilities by leaving-one-out," IEEE Trans. Pattern Anal. Mach. Intell. 17(12), 1202-1212 (1995).
32. P. Marquet et al., "Digital holographic microscopy: a noninvasive contrast imaging technique allowing quantitative visualization of living cells with subwavelength axial accuracy," Opt. Lett. 30, 468-470 (2005).
33. R. Duda, P. Hart, and D. Stork, Pattern Classification, Wiley-Interscience, New York (2000).
34. J. Bacus and J. Weens, "Automated method of differential red blood cell classification with application to the diagnosis of anemia," J. Histochem. Cytochem. 25, 514-632 (1977).
35. A. Rencher, Methods of Multivariate Analysis, Wiley-Interscience, New York (2002).