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Biomedical Optics Express

Biomedical Optics Express

  • Editor: Joseph A. Izatt
  • Vol. 5, Iss. 3 — Mar. 1, 2014
  • pp: 690–700
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4D tracking of clinical seminal samples for quantitative characterization of motility parameters

Giuseppe Di Caprio, Ahmed El Mallahi, Pietro Ferraro, Roberta Dale, Gianfranco Coppola, Brian Dale, Giuseppe Coppola, and Frank Dubois  »View Author Affiliations


Biomedical Optics Express, Vol. 5, Issue 3, pp. 690-700 (2014)
http://dx.doi.org/10.1364/BOE.5.000690


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Abstract

In this paper we investigate the use of a digital holographic microscope, with partial spatial coherent illumination, for the automated detection and tracking of spermatozoa. This in vitro technique for the analysis of quantitative parameters is useful for assessment of semen quality. In fact, thanks to the capabilities of digital holography, the developed algorithm allows us to resolve in-focus amplitude and phase maps of the cells under study, independently of focal plane of the sample image. We have characterized cell motility on clinical samples of seminal fluid. In particular, anomalous sperm cells were characterized and the quantitative motility parameters were compared to those of normal sperm.

© 2014 Optical Society of America

1. Introduction

The human spermatozoon is a polarized motile cell, which delivers the haploid male genome to the oocyte, introduces the centrosome and triggers the oocyte into activity. Abnormal sperm behavior is one of the most common important indicators for clinical male infertility [1

1. T. G. Cooper, E. Noonan, S. von Eckardstein, J. Auger, H. W. Baker, H. M. Behre, T. B. Haugen, T. Kruger, C. Wang, M. T. Mbizvo, and K. M. Vogelsong, “World Health Organization reference values for human semen characteristics,” Hum. Reprod. Update 16(3), 231–245 (2010). [CrossRef] [PubMed]

]. Of special relevance is sperm motility, i.e. curvilinear and linear velocity, wobble etc. In fact, both the concentration of progressively motile spermatozoa and their characteristics movements are significant factors in determining the outcome of homologous tests of human sperm-cervical mucus interaction [2

2. D. Mortimer, I. J. Pandya, and R. S. Sawers, “Relationship between human sperm motility characteristics and sperm penetration into human cervical mucus in vitro,” J. Reprod. Fertil. 78(1), 93–102 (1986). [CrossRef] [PubMed]

]. For this reason, there is growing interest in understanding the kinematics and dynamics of swimming spermatozoa [3

3. P. Denissenko, V. Kantsler, D. J. Smith, and J. Kirkman-Brown, “Human spermatozoa migration in microchannels reveals boundary-following navigation,” Proc. Natl. Acad. Sci. U.S.A. 109(21), 8007–8010 (2012). [CrossRef] [PubMed]

5

5. Y.-A. Chen, Z.-W. Huang, F.-S. Tsai, C.-Y. Chen, C.-M. Lin, and A. M. Wo, “Analysis of sperm concentration and motility in a microfluidic device,” Microfluid Nanofluidics 10(1), 59–67 (2011). [CrossRef]

]. A commercial semi-automated device is currently available to perform sperm motility analyses [6

6. S. T. Mortimer, “CASA--Practical aspects,” J. Androl. 21(4), 515–524 (2000). [PubMed]

]. This widely used instrument for clinical diagnostics is limited since the analysis is performed only in two dimensions and provides a partial in-plane representation of the motility features. In fact, 3D spatial motion is difficult to track using traditional microscopy as samples quickly move out of focus. A recent procedure for sperm cell tracking based on lens-less stereo microscopy has been demonstrated but, because of the low spatial resolution, it is incapable of imaging single cell features [7

7. T. W. Su, L. Xue, and A. Ozcan, “High-throughput lensfree 3D tracking of human sperms reveals rare statistics of helical trajectories,” Proc. Natl. Acad. Sci. U.S.A. 109(40), 16018–16022 (2012). [CrossRef] [PubMed]

]. We propose an approach for particle detection and three-dimensional tracking based on digital holographic (DH) microscopy. Digital holography allows the recording and the numerical reconstruction of the phase and amplitude of the specimen’s optical wavefront [8

8. L. Yu and L. Cai, “Iterative algorithm with a constraint condition for numerical reconstruction of a three-dimensional object from its hologram,” J. Opt. Soc. Am. A 18(5), 1033–1045 (2001). [CrossRef] [PubMed]

] and the refocusing by numerical propagation from a single recorded hologram without realigning of the optical imaging system. Consequently a 3D volumetric field can be reconstructed by means of a single image (the hologram). DH enables high-resolution lensless imaging [9

9. J. Garcia-Sucerquia, W. Xu, S. K. Jericho, P. Klages, M. H. Jericho, and H. J. Kreuzer, “Digital in-line holographic microscopy,” Appl. Opt. 45(5), 836–850 (2006). [CrossRef] [PubMed]

], nano sized particle detection [10

10. F. Verpillat, F. Joud, P. Desbiolles, and M. Gross, “Dark-field digital holographic microscopy for 3D-tracking of gold nanoparticles,” Opt. Express 19(27), 26044–26055 (2011). [CrossRef] [PubMed]

] and the extension of the depth of focus [11

11. 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(18), 6738–6749 (2005). [CrossRef] [PubMed]

]. Furthermore, DH may provide detection of three-dimensional features of biological microstructures [12

12. 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(1), A68–A80 (2013). [CrossRef] [PubMed]

], and allows quantitatively retrieving, in far field region, the amplitude and phase of the wavefront interacting with the structures themselves [13

13. Y. Lim, J. Hahn, S. Kim, J. Park, H. Kim, and B. Lee, “Plasmonic light beaming manipulation and its detection using holographic microscopy,” IEEE J. Quantum Electron. 46(3), 300–305 (2010). [CrossRef]

15

15. G. Di Caprio, G. Coppola, L. De Stefano, M. De Stefano, A. Antonucci, R. Congestri, and E. De Tommasi, “Shedding light on diatom photonics by means of digital holography,” J. Biophotonics DOI: http://dx.doi.org/ [CrossRef] (2012).

]. DH has been successfully employed to perform morphological analysis on bovine [16

16. G. Di Caprio, M. Gioffrè, N. Saffioti, S. Grilli, P. Ferraro, R. Puglisi, D. Balduzzi, A. Galli, and G. Coppola, “Quantitative label-free animal sperm imaging by means of digital holographic microscopy,” IEEE J. Quantum Electron. 16(4), 833–840 (2010).

18

18. F. Merola, L. Miccio, P. Memmolo, G. Di Caprio, A. Galli, R. Puglisi, D. Balduzzi, G. Coppola, P. Netti, and P. Ferraro, “Digital holography as a method for 3D imaging and estimating the biovolume of motile cells,” Lab Chip 13(23), 4512–4516 (2013). [CrossRef] [PubMed]

] and human [19

19. I. Crha, J. Zakova, M. Huser, P. Ventruba, E. Lousova, and M. Pohanka, “Digital holographic microscopy in human sperm imaging,” J. Assist. Reprod. Genet. 28(8), 725–729 (2011). [CrossRef] [PubMed]

, 20

20. G. Coppola, G. Di Caprio, M. Wilding, P. Ferraro, G. Esposito, L. Di Matteo, R. Dale, G. Coppola and B. Dale, “Digital holographic microscopy for the evaluation of human sperm structure,” Zygote DOI: http://dx.doi.org/ [CrossRef] (2013).

] sperm cells and the unique potentialities of DH for 3D particle tracking have been already demonstrated [9

9. J. Garcia-Sucerquia, W. Xu, S. K. Jericho, P. Klages, M. H. Jericho, and H. J. Kreuzer, “Digital in-line holographic microscopy,” Appl. Opt. 45(5), 836–850 (2006). [CrossRef] [PubMed]

, 21

21. P. Langehanenberg, G. von Bally, and B. Kemper, “Autofocusing in digital holographic microscopy,” 3D Research 2, 1–11 (2001).

]. In this paper, we investigate the use of DH working with a partial spatial coherent source to detect automatically the 4D tracking (the 3D spatial motion over time) of organisms recorded on holograms, and to deduce from the retrieved path the relative motility parameters. The developed tracking algorithm has been initially implemented on easily available specimens characterized by complex motions, i.e. Paramecia moving in a free 3D liquid and a Giardia lamblia flowing in a microfluidic channel. Once tested and optimized, the DH tracking routine was applied to a clinical seminal sample moving in a free 3D space. The developed algorithm allowed us to collect resolved in-focus amplitude and phase maps of the cells under study, independently of the focus condition of the sample in the acquired image.

2. Experimental Setup

The complex field of the object beam is reconstructed numerically from the frequency spectrum of the acquired hologram. The off-axis configuration of the employed set-up, i.e. a small angle is introduced between the two interfering beams, allows a spatial separation of the real and conjugate images due to the holographic reconstruction. Thus, the real image (or the conjugate one) can be separated from the whole spatial frequency spectrum with a bandwidth filter and shifted to the origin of the plane. In this way the complex spectrum of the field, except for a constant [23

23. C. Mann, L. Yu, C. M. Lo, and M. Kim, “High-resolution quantitative phase-contrast microscopy by digital holography,” Opt. Express 13(22), 8693–8698 (2005). [CrossRef] [PubMed]

] is obtained and a quantitative phase measurement can be performed. Partial coherent illumination also removes the multiple reflections that can occur with coherent illumination when microscope slides on which biological specimen lying, are used. In fact, we assume that a reflection introduces an increase of the optical path d. If the distance d introduces a significant decorrelation of the speckle pattern, the contrast of the interference fringe pattern between the reflected beam and the direct beam is reduced.

3. Theoretical background

Once the whole field is known, it is possible to reconstruct the optical wavefront at different distances from the plane of acquisition, applying the Fourier formulation of the Fresnel-Kirchhoff diffraction formula [24

24. J. W. Goodman, Introduction to Fourier Optics, 2nd edn. (McGraw-Hill 1996).

]. An interesting approach based on the operator algebra, compatible with our propagation range, has been proposed by Shamir [25

25. M. Nazarathy and J. Shamir, “Fourier optics described by operator algebra,” J. Opt. Soc. Am. 70(2), 150–159 (1980). [CrossRef]

]. Fresnel diffraction is described by replacing the Fresnel-Kirchhoff integral, the lens transfer factor, and other operations by operators. The resulting operator algebra leads to the description of Fourier optics in a simple and compact way, bypassing the cumbersome integral calculus. The detail of the formalism can be found in [25

25. M. Nazarathy and J. Shamir, “Fourier optics described by operator algebra,” J. Opt. Soc. Am. 70(2), 150–159 (1980). [CrossRef]

]. By means of this approach the propagated field Oprop(ξ,η) as a function of the initial field can be rewritten as:
Oprop(ξ,η)=exp(ikd){F1[exp(ikdλ22(υ2+μ2))][F(Oi(u,y))]}
(1)
being F|f(x)| and F1|f(x)| the Fourier transform and anti-transform, respectively, of the function f(x), k=2πnλ (with n refractive index of the medium), ν and μ spatial frequencies defined as ν=ξλd and μ=ηλd, and d the reconstruction distance. For digital reconstruction Eq. (1) is implemented in a discrete form [26

26. F. Dubois, C. Yourassowky, N. Callens, C. Minetti, P. Queeckers, T. Podgorski, and A. Brandenbrurger, “Digital Holographic Microscopy working with a Partially Spatial Coherent Source,” Coherent Light Microscopy (Springer 2011).

]
Oprop(m,n)= exp(ikd){FD1[exp(ikdλ22N2Δ2(U2+V2))][FDOi(h,k)]}
(2)
where N is the number of pixels in both directions, Δ is the sampling distance (i.e. the pixel dimension), m, n, U, V, h and j are integer numbers varying from 0 to N-1. The discretized Fourier transform is defined as

FD{g(k,l)}=1Nk,l=0N1exp[2πiN(mk+nl)]g(k,l).
(3)

Intensity and phase distributions can be retrieved starting from the propagated field according to the following relations:

Iprop(m,n)=|Oprop(m,n)|2,φprop=arctanIm[Oprop(m,n)]Re[Oprop(m,n)].
(4)

The possibility offered by DH to manage the phase of the reconstructed image allows removing and/or compensating the unwanted wavefront variations (such as optical aberrations, slide deformations etc.) [27

27. G. Pedrini, S. Schedin, and H. J. Tiziani, “Aberration compensation in digital holographic reconstruction of microscopic objects,” J. Mod. Opt. 48, 1035–1041 (2001).

]. In this paper, a double exposure technique [28

28. P. Ferraro, S. De Nicola, A. Finizio, G. Coppola, S. Grilli, C. Magro, and G. Pierattini, “Compensation of the inherent wave front curvature in digital holographic coherent microscopy for quantitative phase-contrast imaging,” Appl. Opt. 42(11), 1938–1946 (2003). [CrossRef] [PubMed]

] has been used. A first exposure is relative to the object under investigation, whereas the second one refers to a flat reference region in proximity of the object. This second hologram contains information about all the aberrations introduced by the optical components, i.e. the defocusing due to the microscope objective. By numerically manipulating the two holograms, it is possible to compensate for these aberrations. As described by Eq. (2), DH provides a numerical investigation of the third dimension by performing a plane-by-plane refocusing. Indeed, if digital holographic reconstruction can refocus a sample slice-by-slice as the focusing stage of a classical imaging system, the refocusing degree of an object has to be determined by an external criterion. Several different solutions have been developed [29

29. P. Langehanenberg, B. Kemper, D. Dirksen, and G. von Bally, “Autofocusing in digital holographic phase contrast microscopy on pure phase objects for live cell imaging,” Appl. Opt. 47(19), D176–D182 (2008). [CrossRef] [PubMed]

31

31. J. Kostencka, T. Kozacki, and K. Lizewski, “Autofocusing method for tilted image plane detection in digital holographic microscopy,” Opt. Commun. 297, 20–26 (2013). [CrossRef]

], the one we use in this study is based on the invariance of both energy and amplitude [32

32. F. Dubois, C. Schockaert, N. Callens, and C. Yourassowsky, “Focus plane detection criteria in digital holography microscopy by amplitude analysis,” Opt. Express 14(13), 5895–5908 (2006). [CrossRef] [PubMed]

, 33

33. A. El Mallahi and F. Dubois, “Dependency and precision of the refocusing criterion based on amplitude analysis in digital holographic microscopy,” Opt. Express 19(7), 6684–6698 (2011). [CrossRef] [PubMed]

]. Those invariance properties allow building two focus criteria, respectively for pure amplitude and pure phase objects, based on the score of the integrated amplitude modulus. It has been demonstrated in [32

32. F. Dubois, C. Schockaert, N. Callens, and C. Yourassowsky, “Focus plane detection criteria in digital holography microscopy by amplitude analysis,” Opt. Express 14(13), 5895–5908 (2006). [CrossRef] [PubMed]

] that the criterion is minimized for amplitude object while it is maximized for phase object. Biological cells are mostly transparent and can be considered as pure phase objects.

4. Tracking procedure

The afore-described tracking algorithm has been initially tested on specimens with complex motions and more easily available than the seminal clinical samples. In particular, the movements of a Giardia lamblia flowing in a microfluidic channel (width of 120 μm and depth of 80 μm) have been initially tracked. These data have been captured using a 63 × microscope objective (NA = 0.70, Depth of Field = 1.2 μm) at 24 fps. 123 holograms of a Giardia lamblia have been acquired and the retrieved phase map is shown in Fig. 2(a)
Fig. 2 Procedure for transversal tracking. (a) Compensated phase map; (b) a numerical mask is applied to remove the borders of the microchannel; (c) Region of interest; (d) binary image obtained from (c) via the Otsu’s method; e) Particle position detected via correlation function; (f) Reconstructed transversal path. Scale bars are 20 μm in a), b) and e), 5 μm in c) and d).
. Once the phase irregularities due to the edges of the microchannel have been removed by means of a mask (Fig. 2(b)), the region of interest can be then automatically selected (Fig. 2(c)) and, by using Otsu’s criterion, a binary image of the object is obtained (Fig. 2(d)). This image is used to find the same object in each phase map image. In fact, the central position of the cell corresponds to the maximum value of the correlation function (Fig. 2(e)) between the selected object and the whole compensated phase map. The position is recorded as the X and Y coordinates of the cell (Fig. 2(f)).

This path is characterized by rapid change the Z position. The Z position is evaluated, by applying the self-focusing function. (Fig. 3(a)
Fig. 3 Tracking of a Giardia Lamblia flowing in a microfluidic channel (frame from Media 1). (a) Retrieved path of the particle under study; Reconstructed phase map in the acquisition plane (b) and propagated phase map (c) in the focus plane evaluated by means of the self-focusing criterion. The titles of (b) and (c) refers to the Z positions. Scale bars are 6 μm, data were acquired over 5.12 s.
). Figure 3(b) shows one out-of-focus image of the cell in the acquisition plane (Z = 0 μm); the numerically reconstructed in focus image of the same cell is shown in Fig. 3(c) (frame from Media1).

In order to increase the reliability of the tracking algorithm, the proximity criterion was applied to detect the Z position, too. In other words, the proximity criterion has been extended to a 3D box, allowing the tracking of objects whose phase map is not enough contrasted to apply successfully the Otsu’s threshold method, as in the case, for example, of a blurred out of focus particle.

5. 4D tracking of spermatozoa

Our algorithm has been applied to highlight spermatozoa anomalous behaviors. The cell in Fig. 6
Fig. 6 Single sperm cells tracking (frame from Media 3). Transversal (a) and three-dimensional path (b) of a sperm cell presenting a bent tail; c) a phase map of the sperm cell, showing the morphological defect; the colorbar is in rad. Scale bar is 20 μm in a) and 10 μm in b), data were acquired over 36.8 s.
is affected a morphological anomaly, known as “bent tail”, which causes the non-linear out of plane motion (Fig. 6(a)-6(b), frame from Media 3). Using the holograms collected to track the spermatozoon motion, we can reconstruct the phase map of the cell (Fig. 6(c)) and highlight the morphological defect inducing the anomalous out of plane path. The unique feature of the Digital holography to retrieve quantitative phase information, allows using the tracking algorithm to evaluate many parameters useful for a reliable assessment of semen quality. In the following, the same parameters defined in [6

6. S. T. Mortimer, “CASA--Practical aspects,” J. Androl. 21(4), 515–524 (2000). [PubMed]

] to provide quantitative motion values, have been estimated. The velocity values that are determined are the curvilinear velocity (VCL), straight-line velocity (VSL), and average path velocity (VAP). The VCL refers to the total distance that the sperm head covers in the observation period and it is always the highest of the 3 velocity values. The VSL is determined from the straight-line distance between the first and last points of the trajectory and gives the net space gain in the observation period. This is always the lowest of the 3 velocity values for any spermatozoon. The VAP is the distance the spermatozoon has travelled in the average direction of movement in the observation period: in cases in which the sperm head’s trajectory is very regular and linear, with very little lateral movement, then the VAP is almost the same as the VSL. However, with irregular trajectories, such as those that are not linear, or where there is a high degree of lateral deviation of the head about the direction of movement, then the VAP will be much higher than the VSL. The average path is thus determined by adaptively smoothing the VSL. To describe the trajectory further, three velocity ratio values have been developed. These are linearity (LIN), a comparison of the straight-line and curvilinear paths, straightness (STR), a comparison of the straight-line and average paths, and wobble (WOB) a comparison of the average and curvilinear paths. They are thus defined as follows:

LIN=VSLVCL×100;STR=VSLVAP×100;WOB=VAPVCL×100.
(6)

Linearity is an expression of the relationship between the three-dimensional path taken by the spermatozoon (i.e. curvilinear path) and its net space gain. A circling trajectory would have low linearity, because the curvilinear path (i.e. the circumference of the circle) would be much higher than the net space gain (i.e. the distance between the first and last points of the trajectory). A high linearity trajectory is one where the curvilinear path has a relatively low amplitude of lateral head displacement (see below), and the general direction of movement is the same as that of the straight-line path. Straightness gives an indication of the relationship between the net space gain and the general trajectory of the spermatozoon. A trajectory with evenly spaced track points and low amplitude would have a high STR value, since the average path would approximate the straight-line path. A circling track would be expected to have a low STR because the average path is the average of the curvilinear path, so the STR would be higher than the LIN, but still low. Wobble is the expression of the relationship between the average and curvilinear paths. WOB would be low for a track with a wide trajectory, but high for a circling track, since the curvilinear and average paths would be similar. For the cell in Fig. 6, VCL = 90.9 ± 0.5 μm/s, VSL = 1.2 ± 0.5 μm/s, VAP = 90.8 ± 0.5 μm/s, and consequently LIN = 1, STR = 1, WOB = 99.9.

The tracking of multiple spermatozoa, moving on different focal plane, is made possible by the full 3D approach described above. In Fig. 7(a)
Fig. 7 Multiple sperm cells tracking (frame from Media 4). Transversal (a) and reconstructed three-dimensional path (b). Scale bar is 20 μm, data were acquired over is 11 s.
-7(b) five different spermatozoa are successfully tracked. It’s worth noting that the sequence of holograms is acquired at a constant sample – microscope objective distance, allowing an in vitro analysis and subsequently a numerical approach allows the 3D volumetric field reconstruction. In the spermatozoa set shown in Fig. 7, an anomalous sperm cell is present, whose movement is plotted in green (frame from Media 4). In fact, while every other cell moves in parallel, swimming against a slight flow direction, the anomalous cell advances slower, along a broken track and on a tilted direction.

Retrieving the motility parameters can highlight this anomalous behavior (Table 1

Table 1. Motility parameters evaluated for the cells plotted in Fig. 7.

table-icon
View This Table
). In particular the VSL measured for Cell1 is lower than for every other cell and describes effectively the inefficient cell movement. Accordingly, the wobble is pretty uniform for Cell2-5, varying between 97 and 99 and representing a motion with reduced oscillation around the average path. This value is instead sensibly lower for Cell1, providing a quantitative description of the wide fluctuation of the spermatozoon head. The reported data are consisted with the values reported in literature [6

6. S. T. Mortimer, “CASA--Practical aspects,” J. Androl. 21(4), 515–524 (2000). [PubMed]

].

5. Conclusions

In this paper a label-free approach for spermatozoa tracking has been described. The algorithm, based on digital holography, allows acquiring a sequence of images at a constant specimen-microscope objective distance. The proposed algorithm has been developed and optimized in different experimental conditions, demonstrating a high robustness and versatility. Finally, in collaboration with CFA-ITALY, we studied the 4D tracking of human sperm cells, with the intent of providing a new and more complete approach for clinical investigations in the field of male fertility. Anomalous sperm cells were characterized and the retrieved quantitative motility parameters were compared to those of normal cells. The algorithm is potentially usable in every application where a three dimensional tracking may provide interesting information (i.e. migration analysis of cells in cancer research). Preliminary results of the experiments described in the present paper have already been reported in [37

37. G. Di Caprio, A. El Mallahi, P. Ferraro, G. Coppola and F. Dubois, “Automatic algorithm for the detection and 3D tracking of biological particles in digital holographic microscopy.” Proceeding. EOS: Topical Meeting on Optical Microsystems (2011).

] and [38

38. G. Di Caprio, Quantitative label-free cell imaging by means of digital holographic miscroscopy: a roadmap for a complete characterization of biological samples, PhD dissertation (University of Naples “Federico II” 2011).

], and are the first reports of four-dimensional sperm cells tracking.

References and links

1.

T. G. Cooper, E. Noonan, S. von Eckardstein, J. Auger, H. W. Baker, H. M. Behre, T. B. Haugen, T. Kruger, C. Wang, M. T. Mbizvo, and K. M. Vogelsong, “World Health Organization reference values for human semen characteristics,” Hum. Reprod. Update 16(3), 231–245 (2010). [CrossRef] [PubMed]

2.

D. Mortimer, I. J. Pandya, and R. S. Sawers, “Relationship between human sperm motility characteristics and sperm penetration into human cervical mucus in vitro,” J. Reprod. Fertil. 78(1), 93–102 (1986). [CrossRef] [PubMed]

3.

P. Denissenko, V. Kantsler, D. J. Smith, and J. Kirkman-Brown, “Human spermatozoa migration in microchannels reveals boundary-following navigation,” Proc. Natl. Acad. Sci. U.S.A. 109(21), 8007–8010 (2012). [CrossRef] [PubMed]

4.

M. D. Lopez-Garcia, R. L. Monson, K. Haubert, M. B. Wheeler, and D. J. Beebe, “Sperm motion in a microfluidic fertilization device,” Biomed. Microdevices 10(5), 709–718 (2008). [CrossRef] [PubMed]

5.

Y.-A. Chen, Z.-W. Huang, F.-S. Tsai, C.-Y. Chen, C.-M. Lin, and A. M. Wo, “Analysis of sperm concentration and motility in a microfluidic device,” Microfluid Nanofluidics 10(1), 59–67 (2011). [CrossRef]

6.

S. T. Mortimer, “CASA--Practical aspects,” J. Androl. 21(4), 515–524 (2000). [PubMed]

7.

T. W. Su, L. Xue, and A. Ozcan, “High-throughput lensfree 3D tracking of human sperms reveals rare statistics of helical trajectories,” Proc. Natl. Acad. Sci. U.S.A. 109(40), 16018–16022 (2012). [CrossRef] [PubMed]

8.

L. Yu and L. Cai, “Iterative algorithm with a constraint condition for numerical reconstruction of a three-dimensional object from its hologram,” J. Opt. Soc. Am. A 18(5), 1033–1045 (2001). [CrossRef] [PubMed]

9.

J. Garcia-Sucerquia, W. Xu, S. K. Jericho, P. Klages, M. H. Jericho, and H. J. Kreuzer, “Digital in-line holographic microscopy,” Appl. Opt. 45(5), 836–850 (2006). [CrossRef] [PubMed]

10.

F. Verpillat, F. Joud, P. Desbiolles, and M. Gross, “Dark-field digital holographic microscopy for 3D-tracking of gold nanoparticles,” Opt. Express 19(27), 26044–26055 (2011). [CrossRef] [PubMed]

11.

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(18), 6738–6749 (2005). [CrossRef] [PubMed]

12.

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(1), A68–A80 (2013). [CrossRef] [PubMed]

13.

Y. Lim, J. Hahn, S. Kim, J. Park, H. Kim, and B. Lee, “Plasmonic light beaming manipulation and its detection using holographic microscopy,” IEEE J. Quantum Electron. 46(3), 300–305 (2010). [CrossRef]

14.

G. Di Caprio, P. Dardano, G. Coppola, S. Cabrini, and V. Mocella, “Digital holographic microscopy characterization of superdirective beam by metamaterial,” Opt. Lett. 37(7), 1142–1144 (2012). [CrossRef] [PubMed]

15.

G. Di Caprio, G. Coppola, L. De Stefano, M. De Stefano, A. Antonucci, R. Congestri, and E. De Tommasi, “Shedding light on diatom photonics by means of digital holography,” J. Biophotonics DOI: http://dx.doi.org/ [CrossRef] (2012).

16.

G. Di Caprio, M. Gioffrè, N. Saffioti, S. Grilli, P. Ferraro, R. Puglisi, D. Balduzzi, A. Galli, and G. Coppola, “Quantitative label-free animal sperm imaging by means of digital holographic microscopy,” IEEE J. Quantum Electron. 16(4), 833–840 (2010).

17.

P. Memmolo, G. Di Caprio, C. Distante, M. Paturzo, R. Puglisi, D. Balduzzi, A. Galli, G. Coppola, and P. Ferraro, “Identification of bovine sperm head for morphometry analysis in quantitative phase-contrast holographic microscopy,” Opt. Express 19(23), 23215–23226 (2011). [CrossRef] [PubMed]

18.

F. Merola, L. Miccio, P. Memmolo, G. Di Caprio, A. Galli, R. Puglisi, D. Balduzzi, G. Coppola, P. Netti, and P. Ferraro, “Digital holography as a method for 3D imaging and estimating the biovolume of motile cells,” Lab Chip 13(23), 4512–4516 (2013). [CrossRef] [PubMed]

19.

I. Crha, J. Zakova, M. Huser, P. Ventruba, E. Lousova, and M. Pohanka, “Digital holographic microscopy in human sperm imaging,” J. Assist. Reprod. Genet. 28(8), 725–729 (2011). [CrossRef] [PubMed]

20.

G. Coppola, G. Di Caprio, M. Wilding, P. Ferraro, G. Esposito, L. Di Matteo, R. Dale, G. Coppola and B. Dale, “Digital holographic microscopy for the evaluation of human sperm structure,” Zygote DOI: http://dx.doi.org/ [CrossRef] (2013).

21.

P. Langehanenberg, G. von Bally, and B. Kemper, “Autofocusing in digital holographic microscopy,” 3D Research 2, 1–11 (2001).

22.

F. Dubois, M. L. Requena, C. Minetti, O. Monnom, and E. Istasse, “Partial spatial coherence effects in digital holographic microscopy with a laser source,” Appl. Opt. 43(5), 1131–1139 (2004). [CrossRef] [PubMed]

23.

C. Mann, L. Yu, C. M. Lo, and M. Kim, “High-resolution quantitative phase-contrast microscopy by digital holography,” Opt. Express 13(22), 8693–8698 (2005). [CrossRef] [PubMed]

24.

J. W. Goodman, Introduction to Fourier Optics, 2nd edn. (McGraw-Hill 1996).

25.

M. Nazarathy and J. Shamir, “Fourier optics described by operator algebra,” J. Opt. Soc. Am. 70(2), 150–159 (1980). [CrossRef]

26.

F. Dubois, C. Yourassowky, N. Callens, C. Minetti, P. Queeckers, T. Podgorski, and A. Brandenbrurger, “Digital Holographic Microscopy working with a Partially Spatial Coherent Source,” Coherent Light Microscopy (Springer 2011).

27.

G. Pedrini, S. Schedin, and H. J. Tiziani, “Aberration compensation in digital holographic reconstruction of microscopic objects,” J. Mod. Opt. 48, 1035–1041 (2001).

28.

P. Ferraro, S. De Nicola, A. Finizio, G. Coppola, S. Grilli, C. Magro, and G. Pierattini, “Compensation of the inherent wave front curvature in digital holographic coherent microscopy for quantitative phase-contrast imaging,” Appl. Opt. 42(11), 1938–1946 (2003). [CrossRef] [PubMed]

29.

P. Langehanenberg, B. Kemper, D. Dirksen, and G. von Bally, “Autofocusing in digital holographic phase contrast microscopy on pure phase objects for live cell imaging,” Appl. Opt. 47(19), D176–D182 (2008). [CrossRef] [PubMed]

30.

P. Gao, B. Yao, J. Min, R. Guo, B. Ma, J. Zheng, M. Lei, S. Yan, D. Dan, and T. Ye, “Autofocusing of digital holographic microscopy based on off-axis illuminations,” Opt. Lett. 37(17), 3630–3632 (2012). [CrossRef] [PubMed]

31.

J. Kostencka, T. Kozacki, and K. Lizewski, “Autofocusing method for tilted image plane detection in digital holographic microscopy,” Opt. Commun. 297, 20–26 (2013). [CrossRef]

32.

F. Dubois, C. Schockaert, N. Callens, and C. Yourassowsky, “Focus plane detection criteria in digital holography microscopy by amplitude analysis,” Opt. Express 14(13), 5895–5908 (2006). [CrossRef] [PubMed]

33.

A. El Mallahi and F. Dubois, “Dependency and precision of the refocusing criterion based on amplitude analysis in digital holographic microscopy,” Opt. Express 19(7), 6684–6698 (2011). [CrossRef] [PubMed]

34.

N. Otsu, “A threshold selection method from gray-level histograms,” Automatica 11, 285–296 (1975).

35.

G. E. Forsythe, M. A. Malcolm, and C. B. Moler, Computer Methods for Mathematical Computations (Prentice-Hall, 1976).

36.

Y. Hao and A. Asundi, “Impact of charge-coupled device size on axial measurement error in digital holographic system,” Opt. Lett. 38(8), 1194–1196 (2013). [CrossRef] [PubMed]

37.

G. Di Caprio, A. El Mallahi, P. Ferraro, G. Coppola and F. Dubois, “Automatic algorithm for the detection and 3D tracking of biological particles in digital holographic microscopy.” Proceeding. EOS: Topical Meeting on Optical Microsystems (2011).

38.

G. Di Caprio, Quantitative label-free cell imaging by means of digital holographic miscroscopy: a roadmap for a complete characterization of biological samples, PhD dissertation (University of Naples “Federico II” 2011).

OCIS Codes
(170.1530) Medical optics and biotechnology : Cell analysis
(170.3010) Medical optics and biotechnology : Image reconstruction techniques
(180.0180) Microscopy : Microscopy
(090.1995) Holography : Digital holography

ToC Category:
Microscopy

History
Original Manuscript: October 14, 2013
Revised Manuscript: December 6, 2013
Manuscript Accepted: December 30, 2013
Published: February 11, 2014

Citation
Giuseppe Di Caprio, Ahmed El Mallahi, Pietro Ferraro, Roberta Dale, Gianfranco Coppola, Brian Dale, Giuseppe Coppola, and Frank Dubois, "4D tracking of clinical seminal samples for quantitative characterization of motility parameters," Biomed. Opt. Express 5, 690-700 (2014)
http://www.opticsinfobase.org/boe/abstract.cfm?URI=boe-5-3-690


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References

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  12. 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(1), A68–A80 (2013). [CrossRef] [PubMed]
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  16. G. Di Caprio, M. Gioffrè, N. Saffioti, S. Grilli, P. Ferraro, R. Puglisi, D. Balduzzi, A. Galli, and G. Coppola, “Quantitative label-free animal sperm imaging by means of digital holographic microscopy,” IEEE J. Quantum Electron.16(4), 833–840 (2010).
  17. P. Memmolo, G. Di Caprio, C. Distante, M. Paturzo, R. Puglisi, D. Balduzzi, A. Galli, G. Coppola, and P. Ferraro, “Identification of bovine sperm head for morphometry analysis in quantitative phase-contrast holographic microscopy,” Opt. Express19(23), 23215–23226 (2011). [CrossRef] [PubMed]
  18. F. Merola, L. Miccio, P. Memmolo, G. Di Caprio, A. Galli, R. Puglisi, D. Balduzzi, G. Coppola, P. Netti, and P. Ferraro, “Digital holography as a method for 3D imaging and estimating the biovolume of motile cells,” Lab Chip13(23), 4512–4516 (2013). [CrossRef] [PubMed]
  19. I. Crha, J. Zakova, M. Huser, P. Ventruba, E. Lousova, and M. Pohanka, “Digital holographic microscopy in human sperm imaging,” J. Assist. Reprod. Genet.28(8), 725–729 (2011). [CrossRef] [PubMed]
  20. G. Coppola, G. Di Caprio, M. Wilding, P. Ferraro, G. Esposito, L. Di Matteo, R. Dale, G. Coppola and B. Dale, “Digital holographic microscopy for the evaluation of human sperm structure,” Zygote DOI: http://dx.doi.org/ (2013). [CrossRef]
  21. P. Langehanenberg, G. von Bally, and B. Kemper, “Autofocusing in digital holographic microscopy,” 3D Research2, 1–11 (2001).
  22. F. Dubois, M. L. Requena, C. Minetti, O. Monnom, and E. Istasse, “Partial spatial coherence effects in digital holographic microscopy with a laser source,” Appl. Opt.43(5), 1131–1139 (2004). [CrossRef] [PubMed]
  23. C. Mann, L. Yu, C. M. Lo, and M. Kim, “High-resolution quantitative phase-contrast microscopy by digital holography,” Opt. Express13(22), 8693–8698 (2005). [CrossRef] [PubMed]
  24. J. W. Goodman, Introduction to Fourier Optics, 2nd edn. (McGraw-Hill 1996).
  25. M. Nazarathy and J. Shamir, “Fourier optics described by operator algebra,” J. Opt. Soc. Am.70(2), 150–159 (1980). [CrossRef]
  26. F. Dubois, C. Yourassowky, N. Callens, C. Minetti, P. Queeckers, T. Podgorski, and A. Brandenbrurger, “Digital Holographic Microscopy working with a Partially Spatial Coherent Source,” Coherent Light Microscopy (Springer 2011).
  27. G. Pedrini, S. Schedin, and H. J. Tiziani, “Aberration compensation in digital holographic reconstruction of microscopic objects,” J. Mod. Opt.48, 1035–1041 (2001).
  28. P. Ferraro, S. De Nicola, A. Finizio, G. Coppola, S. Grilli, C. Magro, and G. Pierattini, “Compensation of the inherent wave front curvature in digital holographic coherent microscopy for quantitative phase-contrast imaging,” Appl. Opt.42(11), 1938–1946 (2003). [CrossRef] [PubMed]
  29. P. Langehanenberg, B. Kemper, D. Dirksen, and G. von Bally, “Autofocusing in digital holographic phase contrast microscopy on pure phase objects for live cell imaging,” Appl. Opt.47(19), D176–D182 (2008). [CrossRef] [PubMed]
  30. P. Gao, B. Yao, J. Min, R. Guo, B. Ma, J. Zheng, M. Lei, S. Yan, D. Dan, and T. Ye, “Autofocusing of digital holographic microscopy based on off-axis illuminations,” Opt. Lett.37(17), 3630–3632 (2012). [CrossRef] [PubMed]
  31. J. Kostencka, T. Kozacki, and K. Lizewski, “Autofocusing method for tilted image plane detection in digital holographic microscopy,” Opt. Commun.297, 20–26 (2013). [CrossRef]
  32. F. Dubois, C. Schockaert, N. Callens, and C. Yourassowsky, “Focus plane detection criteria in digital holography microscopy by amplitude analysis,” Opt. Express14(13), 5895–5908 (2006). [CrossRef] [PubMed]
  33. A. El Mallahi and F. Dubois, “Dependency and precision of the refocusing criterion based on amplitude analysis in digital holographic microscopy,” Opt. Express19(7), 6684–6698 (2011). [CrossRef] [PubMed]
  34. N. Otsu, “A threshold selection method from gray-level histograms,” Automatica11, 285–296 (1975).
  35. G. E. Forsythe, M. A. Malcolm, and C. B. Moler, Computer Methods for Mathematical Computations (Prentice-Hall, 1976).
  36. Y. Hao and A. Asundi, “Impact of charge-coupled device size on axial measurement error in digital holographic system,” Opt. Lett.38(8), 1194–1196 (2013). [CrossRef] [PubMed]
  37. G. Di Caprio, A. El Mallahi, P. Ferraro, G. Coppola and F. Dubois, “Automatic algorithm for the detection and 3D tracking of biological particles in digital holographic microscopy.” Proceeding. EOS: Topical Meeting on Optical Microsystems (2011).
  38. G. Di Caprio, Quantitative label-free cell imaging by means of digital holographic miscroscopy: a roadmap for a complete characterization of biological samples, PhD dissertation (University of Naples “Federico II” 2011).

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