Abstract

In this paper, we investigate the use of a digital holographic microscope working with partially coherent spatial illumination for an automated detection and classification of living organisms. A robust automatic method based on the computation of propagating matrices is proposed to detect the 3D position of organisms. We apply this procedure to the evaluation of drinking water resources by developing a classification process to identify parasitic protozoan Giardia lamblia cysts among two other similar organisms. By selecting textural features from the quantitative optical phase instead of morphological ones, a robust classifier is built to propose a new method for the unambiguous detection of Giardia lamblia cyst that present a critical contamination risk.

© 2013 Optical Society of America

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    [CrossRef]
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2012 (2)

2011 (6)

2010 (3)

2009 (1)

L. Yu, S. Mohanty, J. Zhang, S. Genc, M. K. Kim, M. W. Berns, and Z. Chen, “Digital holographic microscopy for quantitative cell dynamics evaluation during laser microsurgery,” Appl. Opt. 17, 12031–12038 (2009).

2008 (4)

M. Bouzid, D. Steverding, and K. M. Tyler, “Detection and surveillance of waterborne protozoan parasites,” Curr. Opin. Biotechnol. 19, 302–306 (2008).
[CrossRef]

M. Antkowiak, N. Callens, C. Yourassowski, and F. Dubois, “Extended focusing imaging of a microparticle field with digital holographic microscope,” Opt. Express 33, 1626–1628 (2008).

B. Kemper and G. von Bally, “Digital holographic microscopy for live cell applications and technical inspection,” Appl. Opt. 47, A52–A61 (2008).
[CrossRef]

C. Minetti, N. Callens, G. Coupier, T. Podgorski, and F. Dubois, “Fast measurements of concentration profiles inside deformable objects in microflows with reduced spatial coherence digital holography,” Appl. Opt. 47, 5305–5314 (2008).
[CrossRef]

2007 (1)

A. Stern and B. Javidi, “Theoretical analysis of three-dimensional imaging and recognition of micro-organisms with a single-exposure on-line holographic microscope,” J. Opt. Soc. Am. 24, 163–168 (2007).
[CrossRef]

2006 (7)

2005 (4)

2004 (2)

M. Sezgin and B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation,” J. Electron. Imaging 13, 146–165 (2004).
[CrossRef]

F. Dubois, M.-L. Novella Requena, C. Minetti, O. Monnom, and E. Istasse, “Partial coherence effects in digital holographic microscopy with a laser source,” Appl. Opt. 43, 1131–1139 (2004).
[CrossRef]

2002 (1)

U. Schnars and W. Jüptner, “Digital recording and numerical reconstruction of holograms,” Meas. Sci. Technol. 13, R85–R101 (2002).
[CrossRef]

1999 (2)

1998 (1)

1997 (1)

H. G. Sheffield and B. Bjorvatn, “Ultrastructure of the cyst of Giardia lamblia,” Am. J. Trop. Med. Hyg. 26, 23–30(1997).

1995 (2)

C. Cortes and V. Vapnik, “Support vector network,” Mach. Learn. 20, 273–297 (1995).

M. R. Rodgers, D. J. Flanigan, and W. Jakubowski, “Identification of algae which interfere with the detection of Giardia cysts and Cryptosporidium oocysts and a method for alleviating this interference,” Appl. Environ. Microbiol. 61, 3759–3763 (1995).

1990 (1)

G. F. Craun, “Waterborne giardiasis,” Hum. Parasit. Dis. 3, 267–293 (1990).

1986 (1)

1982 (1)

Allano, D.

Antkowiak, M.

M. Antkowiak, N. Callens, C. Yourassowski, and F. Dubois, “Extended focusing imaging of a microparticle field with digital holographic microscope,” Opt. Express 33, 1626–1628 (2008).

Berns, M. W.

L. Yu, S. Mohanty, J. Zhang, S. Genc, M. K. Kim, M. W. Berns, and Z. Chen, “Digital holographic microscopy for quantitative cell dynamics evaluation during laser microsurgery,” Appl. Opt. 17, 12031–12038 (2009).

Bevilacqua, F.

Bjorvatn, B.

H. G. Sheffield and B. Bjorvatn, “Ultrastructure of the cyst of Giardia lamblia,” Am. J. Trop. Med. Hyg. 26, 23–30(1997).

Boss, D.

I. Moon, B. Javidi, F. Yi, D. Boss, and P. Marquet, “Automated statistical quantification of three-dimensional morphology and mean corpuscular hemoglobin of multiple red blood cells,” Opt. Express 20, 10295–10309 (2012).
[CrossRef]

R. Liu, D. K. Dey, D. Boss, O. Marquet, and B. Javidi, “Recognition and classification of red blood cells using digital holographic microscopy and data clustering with discriminant analysis,” J. Opt. Soc. Am. 28, 1204–1210 (2011).
[CrossRef]

Boucheron, R.

Bouzid, M.

M. Bouzid, D. Steverding, and K. M. Tyler, “Detection and surveillance of waterborne protozoan parasites,” Curr. Opin. Biotechnol. 19, 302–306 (2008).
[CrossRef]

Callens, N.

Carapezz, E.

Chang, C.-C.

C.-C. Chang and C.-J. Lin, “LIBSVM: a library for support vector machines,” ACM Trans. Intell. Syst. Technol. 2, 1–27 (2011).

Chen, Z.

L. Yu, S. Mohanty, J. Zhang, S. Genc, M. K. Kim, M. W. Berns, and Z. Chen, “Digital holographic microscopy for quantitative cell dynamics evaluation during laser microsurgery,” Appl. Opt. 17, 12031–12038 (2009).

Chmelik, R.

Clancy, J. L.

J. L. Clancy and P. R. Hunter, “Monitoring of Giardia and Cryptosporidium in water in the UK and US,” in The Pathogenic Enteric Protozoa: Giardia, Entamoeba, Cryptosporidium and Cyclospora, C. R. Sterling and R. D. Adam, eds. (Springer, 2004), pp. 129–139.

Clark, D. C.

Corbin, F.

Cortes, C.

C. Cortes and V. Vapnik, “Support vector network,” Mach. Learn. 20, 273–297 (1995).

Coupier, G.

Craun, G. F.

G. F. Craun, “Waterborne giardiasis,” Hum. Parasit. Dis. 3, 267–293 (1990).

Cuche, E.

Daneshpanah, M.

de Ridder, D.

F. van der Heijden, R. P. W. Duin, D. de Ridder, and D. M. L. Tax, Classification, Parameter Estimation and State Estimation (Wiley, 2004).

Debeir, O.

F. Dubois, C. Yourassowsky, O. Monnon, J.-C. Legros, O. Debeir, P. Van Ham, R. Kiss, and C. Decaestecker, “Digital holographic microscopy for three-dimensional dynamic analysis of in vitro cancer cell migration,” J. Biomed. Opt. 11, 054032 (2006).
[CrossRef]

Decaestecker, C.

F. Dubois, C. Yourassowsky, O. Monnon, J.-C. Legros, O. Debeir, P. Van Ham, R. Kiss, and C. Decaestecker, “Digital holographic microscopy for three-dimensional dynamic analysis of in vitro cancer cell migration,” J. Biomed. Opt. 11, 054032 (2006).
[CrossRef]

Depeursinge, C.

Dey, D. K.

R. Liu, D. K. Dey, D. Boss, O. Marquet, and B. Javidi, “Recognition and classification of red blood cells using digital holographic microscopy and data clustering with discriminant analysis,” J. Opt. Soc. Am. 28, 1204–1210 (2011).
[CrossRef]

Ding, H.

Dubois, F.

A. El Mallahi and F. Dubois, “Dependency and precision of the refocusing criterion based on amplitude analysis in digital holographic microscopy,” Opt. Express 19, 6684–6698(2011).
[CrossRef]

C. Minetti, N. Callens, G. Coupier, T. Podgorski, and F. Dubois, “Fast measurements of concentration profiles inside deformable objects in microflows with reduced spatial coherence digital holography,” Appl. Opt. 47, 5305–5314 (2008).
[CrossRef]

M. Antkowiak, N. Callens, C. Yourassowski, and F. Dubois, “Extended focusing imaging of a microparticle field with digital holographic microscope,” Opt. Express 33, 1626–1628 (2008).

F. Dubois, N. Callens, C. Yourassowsky, M. Hoyos, P. Kurowsky, and O. Monnom, “Digital holographic microscopy with reduced spatial coherence for three-dimensional particle flows analysis,” Appl. Opt. 45, 864–871 (2006).
[CrossRef]

F. Dubois, C. Schockaert, N. Callens, and C. Yourassowsky, “Focus plane detection criteria in digital holography microscopy by amplitude analysis,” Opt. Express 14, 5895–5908 (2006).
[CrossRef]

F. Dubois, C. Yourassowsky, O. Monnon, J.-C. Legros, O. Debeir, P. Van Ham, R. Kiss, and C. Decaestecker, “Digital holographic microscopy for three-dimensional dynamic analysis of in vitro cancer cell migration,” J. Biomed. Opt. 11, 054032 (2006).
[CrossRef]

F. Dubois, M.-L. Novella Requena, C. Minetti, O. Monnom, and E. Istasse, “Partial coherence effects in digital holographic microscopy with a laser source,” Appl. Opt. 43, 1131–1139 (2004).
[CrossRef]

F. Dubois, L. Johannes, and J.-C. Legros, “Improved three-dimensional imaging with digital holography microscope using a partial spatial coherence source,” Appl. Opt. 38, 7085–7094 (1999).
[CrossRef]

Duin, R. P. W.

F. van der Heijden, R. P. W. Duin, D. de Ridder, and D. M. L. Tax, Classification, Parameter Estimation and State Estimation (Wiley, 2004).

El Mallahi, A.

Emery, Y.

Ferraro, P.

Finizio, A.

Flanigan, D. J.

M. R. Rodgers, D. J. Flanigan, and W. Jakubowski, “Identification of algae which interfere with the detection of Giardia cysts and Cryptosporidium oocysts and a method for alleviating this interference,” Appl. Environ. Microbiol. 61, 3759–3763 (1995).

Fréchon, D.

Genc, S.

L. Yu, S. Mohanty, J. Zhang, S. Genc, M. K. Kim, M. W. Berns, and Z. Chen, “Digital holographic microscopy for quantitative cell dynamics evaluation during laser microsurgery,” Appl. Opt. 17, 12031–12038 (2009).

Gonzalez, R. C.

R. C. Gonzalez and R. E. Woods, Digital Image Processing (Prentice Hall, 2002).

Gross, M.

Haynie, D. T.

Hoyos, M.

Hsu, B. M.

B. M. Hsu, N. M. Wu, F. C. Shih, M. T. Wa, and C. M. Kung, “Using the flow cytometry to quantify the Giardia and Cryptosporidium oocysts in water samples,” Environ. Monit. Assess. 104, 155–162 (2005).
[CrossRef]

Hu, X.

Huang, D. B.

D. B. Huang and A. C. White, “An updated review on Cryptosporidium and Giardia,” Gastroenterol. Clin. North Am. 35, 291–314 (2006).
[CrossRef]

Hunter, P. R.

J. L. Clancy and P. R. Hunter, “Monitoring of Giardia and Cryptosporidium in water in the UK and US,” in The Pathogenic Enteric Protozoa: Giardia, Entamoeba, Cryptosporidium and Cyclospora, C. R. Sterling and R. D. Adam, eds. (Springer, 2004), pp. 129–139.

Ina, H.

Istasse, E.

Jakubowski, W.

M. R. Rodgers, D. J. Flanigan, and W. Jakubowski, “Identification of algae which interfere with the detection of Giardia cysts and Cryptosporidium oocysts and a method for alleviating this interference,” Appl. Environ. Microbiol. 61, 3759–3763 (1995).

Javidi, B.

I. Moon, B. Javidi, F. Yi, D. Boss, and P. Marquet, “Automated statistical quantification of three-dimensional morphology and mean corpuscular hemoglobin of multiple red blood cells,” Opt. Express 20, 10295–10309 (2012).
[CrossRef]

R. Liu, D. K. Dey, D. Boss, O. Marquet, and B. Javidi, “Recognition and classification of red blood cells using digital holographic microscopy and data clustering with discriminant analysis,” J. Opt. Soc. Am. 28, 1204–1210 (2011).
[CrossRef]

M. Daneshpanah, B. Javidi, and E. A. Watsin, “Three dimensional object recognition with photon counting imagery in the presence of noise,” Opt. Express 18, 26450–26460 (2010).
[CrossRef]

A. Stern and B. Javidi, “Theoretical analysis of three-dimensional imaging and recognition of micro-organisms with a single-exposure on-line holographic microscope,” J. Opt. Soc. Am. 24, 163–168 (2007).
[CrossRef]

B. Javidi, I. Moon, and S. Yeom, “Three-dimensional identification of biological microorganism using integral imaging,” Opt. Express 14, 12096–12108 (2006).
[CrossRef]

B. Javidi, S. Yeom, I. Moon, and M. Daneshpanah, “Real-time automated 3D sensing, detection, and recognition of dynamic biological micro-organic events,” Opt. Express 14, 3806–3826 (2006).
[CrossRef]

I. Moon and B. Javidi, “Shape tolerant three-dimensional recognition of biological microorganisms using digital holography,” Opt. Express 13, 9612–9622 (2005).
[CrossRef]

B. Javidi, I. Moon, S. Yeom, and E. Carapezz, “Three-dimensional imaging and recognition of microorganisms using single-exposure on-line (SEOL) digital holography,” Opt. Express 13, 4492–4506 (2005).
[CrossRef]

Johannes, L.

Joseph, J.

Jüptner, W.

U. Schnars and W. Jüptner, “Digital recording and numerical reconstruction of holograms,” Meas. Sci. Technol. 13, R85–R101 (2002).
[CrossRef]

Kemper, B.

Kim, M. K.

X. Hu, M. Gross, C. Liu, D. C. Clark, D. T. Haynie, and M. K. Kim, “Measurement of the traction force of biological cells by digital holography,” Biomed. Opt. Express 3, 153–159 (2012).
[CrossRef]

L. Yu, S. Mohanty, J. Zhang, S. Genc, M. K. Kim, M. W. Berns, and Z. Chen, “Digital holographic microscopy for quantitative cell dynamics evaluation during laser microsurgery,” Appl. Opt. 17, 12031–12038 (2009).

Kiss, R.

F. Dubois, C. Yourassowsky, O. Monnon, J.-C. Legros, O. Debeir, P. Van Ham, R. Kiss, and C. Decaestecker, “Digital holographic microscopy for three-dimensional dynamic analysis of in vitro cancer cell migration,” J. Biomed. Opt. 11, 054032 (2006).
[CrossRef]

Kobayashi, S.

Kolman, P.

Kreis, T.

Kung, C. M.

B. M. Hsu, N. M. Wu, F. C. Shih, M. T. Wa, and C. M. Kung, “Using the flow cytometry to quantify the Giardia and Cryptosporidium oocysts in water samples,” Environ. Monit. Assess. 104, 155–162 (2005).
[CrossRef]

Kurowsky, P.

Lebrun, D.

Legros, J.-C.

F. Dubois, C. Yourassowsky, O. Monnon, J.-C. Legros, O. Debeir, P. Van Ham, R. Kiss, and C. Decaestecker, “Digital holographic microscopy for three-dimensional dynamic analysis of in vitro cancer cell migration,” J. Biomed. Opt. 11, 054032 (2006).
[CrossRef]

F. Dubois, L. Johannes, and J.-C. Legros, “Improved three-dimensional imaging with digital holography microscope using a partial spatial coherence source,” Appl. Opt. 38, 7085–7094 (1999).
[CrossRef]

Lin, C.-J.

C.-C. Chang and C.-J. Lin, “LIBSVM: a library for support vector machines,” ACM Trans. Intell. Syst. Technol. 2, 1–27 (2011).

Liu, C.

Liu, R.

R. Liu, D. K. Dey, D. Boss, O. Marquet, and B. Javidi, “Recognition and classification of red blood cells using digital holographic microscopy and data clustering with discriminant analysis,” J. Opt. Soc. Am. 28, 1204–1210 (2011).
[CrossRef]

Magistretti, P.

Marquet, O.

R. Liu, D. K. Dey, D. Boss, O. Marquet, and B. Javidi, “Recognition and classification of red blood cells using digital holographic microscopy and data clustering with discriminant analysis,” J. Opt. Soc. Am. 28, 1204–1210 (2011).
[CrossRef]

Marquet, P.

Méès, L.

Memmolo, P.

Miccia, L.

Minetti, C.

Mohanty, S.

L. Yu, S. Mohanty, J. Zhang, S. Genc, M. K. Kim, M. W. Berns, and Z. Chen, “Digital holographic microscopy for quantitative cell dynamics evaluation during laser microsurgery,” Appl. Opt. 17, 12031–12038 (2009).

Monemhagdoust, Z.

Monnom, O.

Monnon, O.

F. Dubois, C. Yourassowsky, O. Monnon, J.-C. Legros, O. Debeir, P. Van Ham, R. Kiss, and C. Decaestecker, “Digital holographic microscopy for three-dimensional dynamic analysis of in vitro cancer cell migration,” J. Biomed. Opt. 11, 054032 (2006).
[CrossRef]

Montfort, F.

Moon, I.

Moser, C.

Nelleri, A.

Novella Requena, M.-L.

Paturzo, M.

Podgorski, T.

Popescu, G.

Rappaz, B.

Rodgers, M. R.

M. R. Rodgers, D. J. Flanigan, and W. Jakubowski, “Identification of algae which interfere with the detection of Giardia cysts and Cryptosporidium oocysts and a method for alleviating this interference,” Appl. Environ. Microbiol. 61, 3759–3763 (1995).

Sankur, B.

M. Sezgin and B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation,” J. Electron. Imaging 13, 146–165 (2004).
[CrossRef]

Schnars, U.

U. Schnars and W. Jüptner, “Digital recording and numerical reconstruction of holograms,” Meas. Sci. Technol. 13, R85–R101 (2002).
[CrossRef]

Schockaert, C.

Sezgin, M.

M. Sezgin and B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation,” J. Electron. Imaging 13, 146–165 (2004).
[CrossRef]

Sheffield, H. G.

H. G. Sheffield and B. Bjorvatn, “Ultrastructure of the cyst of Giardia lamblia,” Am. J. Trop. Med. Hyg. 26, 23–30(1997).

Shih, F. C.

B. M. Hsu, N. M. Wu, F. C. Shih, M. T. Wa, and C. M. Kung, “Using the flow cytometry to quantify the Giardia and Cryptosporidium oocysts in water samples,” Environ. Monit. Assess. 104, 155–162 (2005).
[CrossRef]

Singh, K.

Stern, A.

A. Stern and B. Javidi, “Theoretical analysis of three-dimensional imaging and recognition of micro-organisms with a single-exposure on-line holographic microscope,” J. Opt. Soc. Am. 24, 163–168 (2007).
[CrossRef]

Steverding, D.

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Figures (10)

Fig. 1.
Fig. 1.

Optical setup of the DHM: L1, focusing lens; RGG, rotating ground glass for spatial coherence reduction; M1–M3, mirrors; L2, collimating lens; BS1, BS2, beam splitters; L3, L4, identical microscope objectives lenses ( × 63 ); L5, refocusing lens; CCD, charge-coupled device camera; MC, microchannel; PS, pump system; S, sample.

Fig. 2.
Fig. 2.

Cropped studied organisms-compensated phase images. (a) Giardia lamblia, (b) Chlorella autotrophica, and (c) Scenedesmus dimorphus.

Fig. 3.
Fig. 3.

(a) Hologram of a Giardia lamblia cyst evolving in a microchannel of 100 μm height. The particle is recorded out of focus and is at a distance of 18 μm from the DHM focus plan. (b) Intensity image and (c) phase image. The phase values are remapped to 256 gray levels. (d) Compensated phase map where a mask is added to remove the borders of the microchannel.

Fig. 4.
Fig. 4.

(a) Hologram of a Chlorella autotrophica alga. (b) Intensity image, (c) phase image, and (d) compensated phase map where we can see that some algae (with the arrows) cannot be detected because there are far from the recorded plane.

Fig. 5.
Fig. 5.

Propagating matrices (a)  I max , (b)  I min , (c)  P max ( + compensation ) , (d)  P min ( + compensation ) and (e) by thresholding these four matrices and then combining them, a binary matrix can be computed on which a robust 2D detection can be done.

Fig. 6.
Fig. 6.

Pseudo-3D representation of the compensated phase of the organisms illustrated in Fig. 2 where we can see that even if the three organisms have the similar morphology features, thanks to the optical phase, it becomes possible to distinguish them. Textural features are therefore selected to perform the classification process rather than morphological ones. The z -axis is the optical thickness.

Fig. 7.
Fig. 7.

Box plots for the set of morphological features.

Fig. 8.
Fig. 8.

Box plots for the set of textural features based on the compensated phase information.

Fig. 9.
Fig. 9.

Box plots for the set of textural features based on the intensity information.

Fig. 10.
Fig. 10.

Feature space representation using (a) intensity information of the detected particles of the three species. (b) Compensated phase of the detected particles of the three species.

Tables (3)

Tables Icon

Table 1. Sequence of the Motion of the Same Giardia Cyst in the Microchannel, Showing that Morphological Features are not Good Discriminant as a Large Possible Range of Values for Each Feature is Observed for a Same Particle

Tables Icon

Table 2. Set of the 6 Texture Features that are Computed for the Intensity and the Compensated Phase Images on the Detected Particles

Tables Icon

Table 3. Performance of the Classifier (%)a

Equations (5)

Equations on this page are rendered with MathJax. Learn more.

u d ( x , y ) = exp ( j k d ) F x , y 1 exp ( j k d λ 2 2 ( ν x 2 + ν y 2 ) ) F ν x , ν y + 1 u 0 ( x , y ) ,
u d ( s Δ , t Δ ) = exp ( j k d ) F s , t 1 exp ( j k d λ 2 2 N 2 Δ 2 ( U 2 + V 2 ) ) F U 2 , V 2 + 1 u 0 ( s Δ , t Δ ) ,
I ( s Δ , t Δ , d ) = Re ( u d ( s Δ , t Δ ) ) 2 + Im ( u d ( s Δ , t Δ ) ) 2 P ( s Δ , t Δ , d ) = tan 1 ( Im ( u d ( s Δ , t Δ ) ) Re ( u d ( s Δ , t Δ ) ) ) ,
I min ( s Δ , t Δ ) = min d ( I ( s Δ , t Δ , d ) ) , I max ( s Δ , t Δ ) = max d ( I ( s Δ , t Δ , d ) ) , P min ( s Δ , t Δ ) = min d ( P ( s Δ , t Δ , d ) ) , P max ( s Δ , t Δ ) = max d ( P ( s Δ , t Δ , d ) ) ,
μ n = i = 0 L 1 ( q i m ) n p ( q i ) ,

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