Open Access
23 July 2015 Measurements of morphology and refractive indexes on human downy hairs using three-dimensional quantitative phase imaging
SangYun Lee, Kyoohyun Kim, Yuhyun Lee, Sungjin Park, Heejae Shin, Jongwon Yang, Kwanhong Ko, HyunJoo Park, YongKeun Park
Author Affiliations +
Abstract
We present optical measurements of morphology and refractive indexes (RIs) of human downy arm hairs using three-dimensional (3-D) quantitative phase imaging techniques. 3-D RI tomograms and high-resolution two-dimensional synthetic aperture images of individual downy arm hairs were measured using a Mach–Zehnder laser interferometric microscopy equipped with a two-axis galvanometer mirror. From the measured quantitative images, the RIs and morphological parameters of downy hairs were noninvasively quantified including the mean RI, volume, cylinder, and effective radius of individual hairs. In addition, the effects of hydrogen peroxide on individual downy hairs were investigated.

1.

Introduction

Human body hairs exhibit distinct morphologies at their relative sites in the body, from soft downy hairs on the arms to long stiff hairs on the head. The characteristics of different hairs have been regarded as one of the evolutionary consequences incurred by human bipedalism.1,2 While human body hairs have lost their thermoregulatory roles in maintaining warmth through an evolutionary process, the decorative aspects of hair have gained attention and led many researchers to investigate the microstructures of hairs using diverse microscopic techniques, including atomic force microscopy,35 scanning electron microscopy,68 transmission electron microscopy,9,10 confocal microscopy,11,12 infrared spectroscopy,1315 optical reflectometry,16 and ellipsometry17 under various experimental conditions. Using an inverse Monte-Carlo method, the melanin contents of human hairs have been quantitatively estimated from digital images.18 Chemical analyses of hairs for drug detection (e.g., alcohol and cocaine) also have been extensively carried out, particularly in the fields of forensic science.1923 However, previous approaches have difficulties in providing simultaneous measurements of three-dimensional (3-D) morphologies and the refractive index (RI) of hairs quantitatively and noninvasively. Furthermore, most previous studies have used expensive instrumentation, which prevents these imaging techniques from being utilized in general laboratories in which body hairs are studied.

Here, we present quantitative phase imaging (QPI) as an effective imaging tool to study the morphological and optical properties of downy hairs in a noninvasive and quantitative manner. QPI techniques provide quantitative measurements of optical phase delay introduced by intrinsic RI distributions of transparent cells using interferometry.24,25 QPI techniques have been applied previously for biological studies of cells and tissues including blood cells,2633 cell growth monitoring,34 neurons,35 and optical imaging of tissue slices.36 The feasibility of noninvasive and quantitative imaging of QPI was demonstrated with individual downy hairs using the QPI techniques. The high-resolution two-dimensional (2-D) holographic synthetic aperture images and 3-D RI distribution maps of individual hairs were measured, from which the morphological and biochemical properties were retrieved, including the mean RI, volume, cylinder, and effective radius of individual hairs. Furthermore, we investigated the effects of hydrogen peroxide (H2O2), which is one of the widely-used hair bleaching agents, on individual downy hairs using the present method.

2.

Materials and Methods

2.1.

Sample Preparations

A total of 13 human downy hairs were collected from one arm of a healthy donor (Asian male) [Fig. 1(a1)]. Each hair was gently posed on the top of a coverslip (24×50mm2, C024501, Matsunami, LTD, Japan) with oil immersion (n=1.518), which decreases the RI contrast between the hairs and the medium. The sample was then covered with another coverslip [Fig. 1(a2)]. To study the effects of hydrogen peroxide on the hair structures, four hair samples were treated with 3% H2O2 solution (Sigma–Aldrich, St. Louis, Missouri) for 24 h. The shape of the edges of the hairs cells were imaged before and after the H2O2 treatments.

Fig. 1

(a) Human downy hair preparation. (a1) Left arm of a healthy donor from which downy hairs were collected. (a2) A downy hair with immersion oil loaded between two coverslips. (b) Mach–Zehnder interferometric microscopy equipped with a two-axis galvanometer mirror. BS, beam splitter; GM, galvanometer mirror; CL, condenser lens; OL, objective lens, M, mirror. (c) Holographic image reconstruction process. A set of retrieved (c1) phase and (c2) amplitude maps of complex optical fields. (c3) Synthetic aperture phase image. (c4) Emulated differential interference contrast (DIC) image. (c5) Reconstructed three-dimensional refractive index (3-D RI) distributions of a hair sample. (c6) 3-D RI isosurface of a hair tomogram.

JBO_20_11_111207_f001.png

2.2.

Optical Setup for Quantitative Phase Imaging

For quantitative measurements of human downy hairs, we employed a Mach–Zehnder interferometric microscope equipped with a two-axis galvanometer mirror.3739 The schematic of the setup is shown in Fig. 1(b). A diode-pumped solid state laser (λ=532nm, 50 mW, Cobolt Co., Solna, Sweden) is used as a coherent light source. A beam splitter (BS, BS016, Thorlabs) divides a laser beam into two arms: a reference beam and a sample beam. The sample beam passes through a downy hair loaded on the sample stage of an inverted microscope. A two-axis galvanometer mirror (GM, GVS012/M, Thorlabs) varies the angle of the illumination beam impinging onto the hair sample, from which 2-D optical field images of the sample are obtained with various illumination angles. For the reconstruction of off-axis interference patterns, the reference and the sample beams are recombined with a tilted angle by another beam splitter, and the resultant interferogams of the samples are recorded by a high-speed CMOS camera (Neo sCMOS, Andor Inc., Northern Ireland, UK).

An objective lens [UPLFLN, 60×, numerical aperture (NA)=0.9, Olympus Inc., San Diego, California] was used as a condenser lens with the tube lens of a focal length of 200 mm. For the imaging purpose, a high-NA objective lens (PLAPON, 60×, oil immersion, NA=1.42, Olympus Inc., San Diego, California) was used with an additional telescopic 4-f system, and the total magnification of the imaging system is 250×. The camera has 1776×1760pixels with a pixel size of 6.5μm. The full field of view at the sample plane was 46.18×45.76μm2, considering the total magnification of 250×. Each hair sample was illuminated with plane waves at 300 different incidence angles, which were systematically controlled by the two-axis GM at a frame rate of 100 Hz.

2.3.

Image Reconstruction Procedures

Complex optical fields of the sample were retrieved from the interferogram recorded by the QPI technique, via a retrieval algorithm based on Fourier transform.40,41 The phase and amplitude maps of a representative hair are shown in Figs. 1(c1) and 1(c2).

From a set of retrieved phase images [Fig. 1(c1)] with various illumination angles, the 2-D high-resolution synthetic aperture phase image [Fig. 1(c3)] was constructed with a synthetic aperture imaging algorithm.42,43 By numerically extending the aperture size, the synthetic aperture algorithm fully used the high-spatial frequency information of a sample which cannot be accessed with just a single laser illumination angle. The numerical extension of an aperture was conducted in the 2-D Fourier space, and the resultant 2-D synthetic aperture phase image [Fig. 1(c3)] exhibited a higher-spatial resolution and signal-to-noise ratio (SNR), compared to the phase image from a single hair interferogram [Fig. 1(c1)].44,45

In principle, the resolution of an imaging system is determined by the NAs of an objective lens. In general, for 2-D QPI with a single illumination, the maximum accessible spatial frequency |kmax| is determined as 2πNAimag/λ, where NAimag represents the NA of the imaging system. In synthetic aperture imaging, the maximum spatial frequency can be further extended by combining multiple phase images obtained with various incident angles; the maximum spatial frequency is extended to 2π(NAimag+NAillum)/λ, where NAillum is the NA of the condenser lens. The SNR of the synthetic aperture phase image is also more significantly enhanced than that of a single phase image, mainly due to speckle noise reduction.

A differential interference contrast (DIC) image can also be numerically reconstructed from the synthetic aperture phase image [Fig. 1(c4)]. This emulated DIC image is readily obtained from a synthetic aperture phase image by numerically interfering an original phase image and a slightly translated synthetic aperture phase image with an additional phase shift.46 Because the optical imaging contrast in DIC images is a result of the phase gradient, the imaging contrast is enhanced for objects with high RI changes or gradients, such as subcellular organelles in unstained biological cells or defects in nonbiological samples.

To reconstruct the 3-D RI distribution of a hair sample from a set of retrieved complex optical fields, we used the optical diffraction tomography (ODT) algorithm.39,4749 Compared to the projection algorithm,50,51 the ODT algorithm considers light diffraction at samples, and thus, provides high-resolution tomographic reconstruction with better image qualities, especially for large samples with high RI contrast.48

2.4.

Statistical Analysis

P-values are calculated by two-tailed paired t-test comparing quantitative parameters of human arm hairs. All the numbers following the ± sign in the text are standard deviations (SD).

3.

Result and Discussion

3.1.

High-Resolution Two-Dimensional Phase Images and Three-Dimensional Refractive Index Maps of Human Downy Hairs

In order to measure the morphologies of individual downy hairs, we measured and took 3-D RI maps and 2-D synthetic aperture phase images of human downy hairs collected from arms, using the Mach–Zehnder laser interferometry equipped with a two-axis galvanometer mirror (see Sec. 2). Figure 2 shows the measured images of representative human downy hairs. Measured hair samples were categorized into two groups based on their morphologies; hairs in the soft type exhibit smooth surfaces and internal RI distributions [Figs. 2(a1)2(a3)], and hairs in the rough type present complex internal structural defects and spiky surfaces [Figs. 2(b1)2(b3)].

Fig. 2

(a) and (b) Quantitative phase images of representative human downy hairs for soft and rough type: (a1) and (b1) the two-dimensional synthetic aperture phase image (top) and the corresponding emulated DIC image (bottom) of the representative hair samples. (a2) and (b2) The cross-sectional slices of the reconstructed 3-D RI tomogram in x-y, z-y, and x-z plane. (a3) and (b3) Perspective view of the representative 3-D hair RI isosurface. RI threshold set as a value of 1.545. Each colored arrow in the figures represents the same fine structures of the measured downy hair.

JBO_20_11_111207_f002.png

High-resolution 2-D synthetic aperture phase images [Figs. 2(a1) and 2(b1), top], corresponding emulated DIC images [Figs. 2(a1) and 2(b1), bottom] and three cross-sectional slices of the reconstructed 3-D RI tomograms [Figs. 2(a2) and 2(b2)] of the representative downy hairs showed significant morphological differences (see Sec. 2). The renderings of 3-D RI isosurfaces (n>1.545) of the hair samples are also shown in Figs. 2(a3) and 2(b3).

In total, 13 human downy hairs were measured; six hair cells were categorized as the soft type, and seven hair cells were in the rough type. The representative holographic images of the soft type hair [Figs. 2(a1)2(a3)] had soft and continuously connected cell boundaries without any apparent structural defects. The relatively homogenous RI distribution of the soft type hair [Fig. 2(a2)] reflects the homogeneity of its internal structures. Figures 2(b1)2(b3) show quantitative phase images of the representative rough type downy hair.

Significantly low speckle noise level in the backgrounds of the DIC image indicates that the observed fine structures in the hair sample are from real structural defects. The characteristic fine structures, indicated with the colored arrows in Fig. 2(b1), are also shown in the reconstructed 3-D RI tomogram [Fig. 2(b2)]. Particularly, the structural defect [the black arrow in Fig. 2(b1)] can be clearly seen in the z-y sectional RI slice. Finally, the RI isosurface of the rough type hair [Fig. 2(b3)] shows more complicated surface structures when compared to the RI isosurface of the soft type hair [Fig. 2(a3)].

3.2.

Retrieval of Quantitative Downy Hair Parameters

To demonstrate the quantitative imaging capability of the present method for the study of downy hairs, we retrieved the RIs and morphological parameters from measured 3-D RI maps of individual hairs. The retrieved parameters included mean RI values, the volumes for 20-μm length hairs [Fig. 3(a)], cylindrical radii of the hairs as a function of length [Fig. 3(b)], and the effective radii of hair edges [Fig 3(c)].

Fig. 3

RIs and morphological parameters of human downy hairs. (a) Retrieved mean RIs and volumes of 20-μm length hairs. (b) Cylindrical radii of hairs as a function of length. The shaded area represents standard deviations. Inset: a schematic describing the radius and effective radius of a hair. (c) Retrieved effective radius. (d) Correlation map between the effective radii and the cylindrical radii. Each colored dot denotes individual hairs. Boxes, median with upper and lower quartiles; whiskers, parameter range.

JBO_20_11_111207_f003.png

To obtain the mean RI value of a hair, we averaged the RI values over the sample area in the focal plane [i.e., the x-y plane in Figs. 2(a2) and 2(b2)]. Volumes of the 20-μm length hairs were calculated by integrating voxels higher than the RI threshold (n=1.53). The mean values of the retrieved mean RI at 532 nm and volume for the 20-μm length hairs were 1.557±0.007 (mean±SD) and 692±173 femtoliter (fL), respectively, which are in good agreement with a previous RI measurement.52

The cylindrical radii of hairs as a function of hair lengths are retrieved assuming a cylindrical symmetry [inset in Fig. 3(b)]. Then the cylinder radius represents the hair thickness as a function of hair length. The graph in Fig. 3(b) depicts the measured cylindrical radii for 13 downy hairs along their hair axis. The red line denotes the mean cylinder radii, and the shaded region corresponds to SD. The mean cylindrical radii seem to saturate beyond a length of 5μm.

The roundness of the hair tips can be effectively described by measuring the effective radii of the tips [Fig. 3(c)]. In order to retrieve the effective radius of a hair R, the following relation was used: R=(r2+h2)/2h, where r is the radius of a circular slice orthogonal to the hair axis, h is the distance between the circular slice and the hair tip. For the case of a perfect sphere, any circular slice of the sphere with a different h gives the same R. This is not the case for hairs, and the height h should be determined appropriately in order to have a sphere with an effective radius Reff describing the roundness of the hair edge. We determined h from the plane on the hair axis where the cylindrical radius equals 85% of the saturated cylindrical radius. Results are shown in Fig. 3(c). The mean effective radius of the measured hairs was 3.00±0.37μm.

To address the relationship between the effective radii Reff and the saturated cylindrical radii of the hairs rsat, the correlation of these two parameters were investigated [Fig. 3(d)]. The correlation clearly shows a linear relationship; they are well described by a linear regression model Reff=0.79 rsat+0.26μm with R2=0.96. This linear relation of these radii implies that each human arm hair shares a morphological similarity with each other. In addition, a coefficient of proportionality less than one means that hair edges are sharper than ideal hemispheres, in accordance with the measured 3-D RI tomograms.

3.3.

Effects of Hydrogen Peroxide on Individual Human Downy Hairs

To further show the capability of QPI, we performed experiments to study the effects of hydrogen peroxide on individual hairs. Using the present method, four hairs were systematically measured before and after a 24-h treatment with a 3% hydrogen peroxide solution (see Sec. 2). Hydrogen peroxide is one of the widely used bleaching agents which induce irreversible alterations in the physicochemical properties of the melanin granules in hair proteins.53 It is also known that physical damage occurs to keratin proteins from oxidization.

To investigate possible alterations induced by hydrogen peroxide, we measured the morphological and biochemical properties of hairs. The x-y cross-sectional slices of the reconstructed 3-D RI tomograms for four intact arm hairs [Figs. 4(a1)4(a4)] and the same hairs treated with 3% H2O2 [Figs. 4(a5)4(a8)] are shown in Fig. 4(a). The arrows and the dashed region denote the same fine structures which were used to track the same hairs before and after the H2O2 treatment. Each graph in Figs. 4(b) and 4(c), respectively, describes the mean RIs and volumes of the 20-μm length hairs before and after H2O2 treatment. To retrieve these parameters, the same procedures described above were used. The mean values of RI at 532 nm for intact and H2O2 treated hairs were 1.557±0.003 and 1.556±0.005, respectively. In addition, the mean volume for the intact and H2O2 treated hair groups was 724±141 and 731±166fL, respectively. The paired t-test yielded p-values larger than 0.5 for both the mean RI and the volume between the two hair groups, and thus does not show statistical difference before and after the treatment.

Fig. 4

Effects of hydrogen peroxide on human downy hairs. (a) x-y cross-sectional slices of RI tomograms for (a1)–(a4) intact hairs and (a5)−(a8) the same hairs after a 24 hr H2O2 treatment, respectively. (b) Mean RIs and (c) volumes of four hairs before and after the treatment. Same color denotes the same individual downy hair.

JBO_20_11_111207_f004.png

The statistical indifference between the intact hairs and the H2O2 treated ones, however, does not imply that the edge structures of human downy hairs are not affected by hydrogen peroxide. It seems that alterations in the edge structures of body hairs by hydrogen peroxide are small as predicted by minute morphological changes in Figs. 4(a1)4(a8), and so are comparable with the measurement errors. Noticeably, we could not observe the black stains that may be responsible for melanin pigments at hair edges, contrary to observations at thicker parts of the hair. The absence of melanin pigments at the hair edges possibly gives rise to minute changes in the measured parameters by hydrogen peroxide.

4.

Conclusion

Herein, we performed quantitative and noninvasive optical measurements on human downy arm hairs using 3-D QPI. With a Mach–Zehnder interferometer equipped with a dual axis galvanometer mirror, 3-D RI tomograms and 2-D synthetic aperture images of individual hairs were measured. To fully exploit the quantitative imaging capability of the present method, RIs and morphological parameters including mean RI, volume, cylinder radius, and effective radius for individual downy hairs were retrieved from the measured 3-D RI maps. Finally, the RIs and morphological alterations in the downy hairs by hydrogen peroxide were also investigated with the present method.

We expect that the unique advantages of the QPI techniques with its high-resolution 3-D RI imaging capabilities can be beneficial in investigating alterations in human hairs from various chemical agents such as bleaching agents,9,13 shampoos, and rinses. Furthermore, structural variations in hair between different human species6,54 or gathered body sites55,56 can also be investigated from both compositional and morphological aspects. Furthermore, recently advanced QPI techniques, including a readily implementable QPI unit,38,57 real-time 3-D measurements,58 spectroscopic RI imaging,5962 polarization-sensitive imaging,63,64 and a super-resolution technique,65 will also extend the applicability toward quantitative studies on the dynamic phenomena relevant to hair growths66,67 or formations of structural defects under diverse experimental conditions.

Acknowledgments

This work was supported by KAIST-Khalifa University Project, APCTP, KAIST Endrun Project, the Korean Ministry of Education, Science and Technology, and the National Research Foundation (2014K1A3A1A09063027, 2013M3C1A3063046, 2012-M3C1A1-048860, 2014M3C1A3052537).

References

1. 

P. E. Wheeler, “The evolution of bipedality and loss of functional body hair in hominids,” J. Hum. Evol., 13 (1), 91 –98 (1984). http://dx.doi.org/10.1016/S0047-2484(84)80079-2 JHEVAT 0047-2484 Google Scholar

2. 

P. E. Wheeler, “The loss of functional body hair in man: the influence of thermal environment, body form and bipedality,” J. Hum. Evol., 14 (1), 23 –28 (1985). http://dx.doi.org/10.1016/S0047-2484(85)80091-9 JHEVAT 0047-2484 Google Scholar

3. 

S. D. O’Connor, K. L. Komisarek and J. D. Baldeschwieler, “Atomic force microscopy of human hair cuticles: a microscopic study of environmental effects on hair morphology,” J. Invest. Dermatol., 105 (1), 96 –99 (1995). http://dx.doi.org/10.1111/1523-1747.ep12313377 Google Scholar

4. 

C. LaTorre and B. Bhushan, “Investigation of scale effects and directionality dependence on friction and adhesion of human hair using AFM and macroscale friction test apparatus,” Ultramicroscopy, 106 (8), 720 –734 (2006). http://dx.doi.org/10.1016/j.ultramic.2005.11.010 ULTRD6 0304-3991 Google Scholar

5. 

S. Gurden et al., “Quantitative analysis and classification of AFM images of human hair,” J. Microsc., 215 (1), 13 –23 (2004). http://dx.doi.org/10.1111/j.0022-2720.2004.01350.x JMICAR 0022-2720 Google Scholar

6. 

W. Hess et al., “Human hair morphology: a scanning electron microscopy study on a male Caucasoid and a computerized classification of regional differences,” Scanning Microsc., 4 (2), 375 –386 (1990). SCMIEU 0891-7035 Google Scholar

7. 

B. Lindelöf, B. Forslind and M.-A. Hedblad, “Human hair form: morphology revealed by light and scanning electron microscopy and computer aided three-dimensional reconstruction,” Arch. Dermatol., 124 (9), 1359 –1363 (1988). http://dx.doi.org/10.1001/archderm.1988.01670090015003 ARDEAC 0003-987X Google Scholar

8. 

R. Dawber and S. Comaish, “Scanning electron microscopy of normal and abnormal hair shafts,” Arch. Dermatol., 101 (3), 316 –322 (1970). http://dx.doi.org/10.1001/archderm.1970.04000030060009 ARDEAC 0003-987X Google Scholar

9. 

T. Imai, “The influence of hair bleach on the ultrastructure of human hair with special reference to hair damage,” Okajimas Folia Anat. Jpn., 88 (1), 1 –9 (2011). http://dx.doi.org/10.2535/ofaj.88.1 OFAJAE 0030-154X Google Scholar

10. 

L. Pötsch, G. Skopp and J. Becker, “Ultrastructural alterations and environmental exposure influence the opiate concentrations in hair of drug addicts,” Int. J. Legal Med., 107 (6), 301 –305 (1995). http://dx.doi.org/10.1007/BF01246877 IJLMEA 1427-1596 Google Scholar

11. 

M. Jourlin and Y. Duvault, “3D reconstruction of human hair by confocal microscopy,” J. Soc. Cosmet. Chem, 44 1 –12 (1993). Google Scholar

12. 

C. Hadjur et al., “Cosmetic assessment of the human hair by confocal microscopy,” Scanning, 24 (2), 59 –64 (2002). http://dx.doi.org/10.1002/sca.4950240202 SCNNDF 0161-0457 Google Scholar

13. 

J. Strassburger and M. M. Breuer, “Quantitative Fourier transform infrared spectroscopy of oxidized hair,” J. Soc. Cosmet. Chem, 36 61 –74 (1985). JSCCA5 Google Scholar

14. 

D. Ammann et al., “Degradation of the ethyl glucuronide content in hair by hydrogen peroxide and a non-destructive assay for oxidative hair treatment using infra-red spectroscopy,” Forensic Sci. Int., 244 30 –35 (2014). Google Scholar

15. 

Y. Miyamae, Y. Yamakawa and Y. Ozaki, “Evaluation of physical properties of human hair by diffuse reflectance near-infrared spectroscopy,” Appl. Spectrosc., 61 (2), 212 –217 (2007). http://dx.doi.org/10.1366/000370207779947503 Google Scholar

16. 

X. Wang et al., “Characterization of human scalp hairs by optical low-coherence reflectometry,” Opt. Lett., 20 (6), 524 –526 (1995). http://dx.doi.org/10.1364/OL.20.000524 Google Scholar

17. 

D. Chan et al., “Structural investigations of human hairs by spectrally resolved ellipsometry,” J. Biomed. Opt., 11 (1), 014029 (2006). http://dx.doi.org/10.1117/1.2162848 JBOPFO 1083-3668 Google Scholar

18. 

A. N. Bashkatov et al., “Estimate of the melanin content in human hairs by the inverse Monte-Carlo method using a system for digital image analysis,” Quantum Electron., 36 (12), 1111 (2006). http://dx.doi.org/10.1070/QE2006v036n12ABEH013336 QUELEZ 1063-7818 Google Scholar

19. 

F. Pragst and M. A. Balikova, “State of the art in hair analysis for detection of drug and alcohol abuse,” Clin. Chim. Acta, 370 (1), 17 –49 (2006). http://dx.doi.org/10.1016/j.cca.2006.02.019 CCATAR 0009-8981 Google Scholar

20. 

P. Kintz, Analytical and Practical Aspects of Drug Testing in Hair, Taylor & Francis Group, CRC Press(2006). Google Scholar

21. 

S. Hartwig, V. Auwärter and F. Pragst, “Effect of hair care and hair cosmetics on the concentrations of fatty acid ethyl esters in hair as markers of chronically elevated alcohol consumption,” Forensic Sci. Int., 131 (2), 90 –97 (2003). http://dx.doi.org/10.1016/S0379-0738(02)00412-7 FSINDR 0379-0738 Google Scholar

22. 

V. Auwärter et al., “Fatty acid ethyl esters in hair as markers of alcohol consumption. Segmental hair analysis of alcoholics, social drinkers, and teetotalers,” Clin. Chem., 47 (12), 2114 –2123 (2001). Google Scholar

23. 

W. Baumgartner, V. Hill and W. Blahd, “Hair analysis for drugs of abuse,” J. Forensic Sci., 34 (6), 1433 –1453 (1989). Google Scholar

24. 

G. Popescu, Quantitative Phase Imaging of Cells and Tissues, McGraw-Hill, New York (2011). Google Scholar

25. 

K. Lee et al., “Quantitative phase imaging techniques for the study of cell pathophysiology: from principles to applications,” Sensors, 13 (4), 4170 –4191 (2013). http://dx.doi.org/10.3390/s130404170 SNSRES 0746-9462 Google Scholar

26. 

Y. Park et al., “Metabolic remodeling of the human red blood cell membrane,” Proc. Natl. Acad. Sci., 107 (4), 1289 (2010). http://dx.doi.org/10.1073/pnas.0910785107 Google Scholar

27. 

Y. Park et al., “Measurement of red blood cell mechanics during morphological changes,” Proc. Natl. Acad. Sci., 107 (15), 6731 (2010). http://dx.doi.org/10.1073/pnas.0909533107 Google Scholar

28. 

N. T. Shaked et al., “Quantitative microscopy and nanoscopy of sickle red blood cells performed by wide field digital interferometry,” J. Biomed. Opt., 16 (3), 030506 (2011). http://dx.doi.org/10.1117/1.3556717 Google Scholar

29. 

H. Byun et al., “Optical measurement of biomechanical properties of individual erythrocytes from a sickle cell patient,” Acta Biomaterialia, 8 (11), 4130 –4138 (2012). Google Scholar

30. 

P. Memmolo et al., “3D morphometry of red blood cells by digital holography,” Cytomet. Part A, 85 (12), 1030 –1036 (2014). http://dx.doi.org/10.1002/cyto.a.22570 1552-4922 Google Scholar

31. 

H. Park et al., “3-D refractive index tomograms and deformability of individual human red blood cells from cord blood of newborn infants and maternal blood,” (2015). Google Scholar

32. 

J. Yoon et al., “Label-free characterization of white blood cells by measuring 3D refractive index maps,” (2015). Google Scholar

33. 

H. Park et al., “Alterations in cell surface area and deformability of individual human red blood cells in stored blood,” (2015). Google Scholar

34. 

G. Popescu et al., “Optical imaging of cell mass and growth dynamics,” Am. J. Physiol., 295 (2), C538 –C544 (2008). http://dx.doi.org/10.1152/ajpcell.00121.2008 1522-1563 Google Scholar

35. 

P. Jourdain et al., “Determination of transmembrane water fluxes in neurons elicited by glutamate ionotropic receptors and by the cotransporters KCC2 and NKCC1: a digital holographic microscopy study,” J. Neurosci., 31 (33), 11846 –11854 (2011). http://dx.doi.org/10.1523/JNEUROSCI.0286-11.2011 JNNUEF Google Scholar

36. 

Z. Wang et al., “Tissue refractive index as marker of disease,” J. Biomed. Opt., 16 (11), 116017 (2011). http://dx.doi.org/10.1117/1.3656732 Google Scholar

37. 

S. Lee et al., “High-resolution 3-D refractive index tomography and 2-D synthetic aperture imaging of live phytoplankton,” J. Opt. Soc. Korea, 18 (6), 691 –697 (2014). http://dx.doi.org/10.3807/JOSK.2014.18.6.691 1226-4776 Google Scholar

38. 

K. Kim et al., “Diffraction optical tomography using a quantitative phase imaging unit,” Opt. Lett., 39 (24), 6935 –6938 (2014). http://dx.doi.org/10.1364/OL.39.006935 OPLEDP 0146-9592 Google Scholar

39. 

V. Lauer, “New approach to optical diffraction tomography yielding a vector equation of diffraction tomography and a novel tomographic microscope,” J. Microsc., 205 (2), 165 –176 (2002). http://dx.doi.org/10.1046/j.0022-2720.2001.00980.x JMICAR 0022-2720 Google Scholar

40. 

M. Takeda, H. Ina and S. Kobayashi, “Fourier-transform method of fringe-pattern analysis for computer-based topography and interferometry,” J. Opt. Soc. Am., 72 (1), 156 –160 (1982). http://dx.doi.org/10.1364/JOSA.72.000156 JSDKD3 Google Scholar

41. 

S. K. Debnath and Y. Park, “Real-time quantitative phase imaging with a spatial phase-shifting algorithm,” Opt. Lett., 36 (23), 4677 –4679 (2011). http://dx.doi.org/10.1364/OL.36.004677 OPLEDP 0146-9592 Google Scholar

42. 

S. A. Alexandrov et al., “Synthetic aperture Fourier holographic optical microscopy,” Phys. Rev. Lett., 97 (16), 168102 (2006). http://dx.doi.org/10.1103/PhysRevLett.97.168102 Google Scholar

43. 

K. Lee et al., “Synthetic Fourier transform light scattering,” Opt. Express, 21 (19), 22453 –22463 (2013). http://dx.doi.org/10.1364/OE.21.022453 OPEXFF 1094-4087 Google Scholar

44. 

V. Mico et al., “Synthetic aperture superresolution with multiple off-axis holograms,” J. Opt. Soc. Am. A, 23 (12), 3162 –3170 (2006). http://dx.doi.org/10.1364/JOSAA.23.003162 Google Scholar

45. 

T. R. Hillman et al., “High-resolution, wide-field object reconstruction with synthetic aperture Fourier holographic optical microscopy,” Opt. Express, 17 (10), 7873 –7892 (2009). http://dx.doi.org/10.1364/OE.17.007873 OPEXFF 1094-4087 Google Scholar

46. 

N. Lue et al., “Quantitative phase imaging of live cells using fast Fourier phase microscopy,” Appl. Opt., 46 (10), 1836 –1842 (2007). http://dx.doi.org/10.1364/AO.46.001836 APOPAI 0003-6935 Google Scholar

47. 

Y. Sung et al., “Optical diffraction tomography for high resolution live cell imaging,” Opt. Express, 17 (1), 266 –277 (2009). http://dx.doi.org/10.1364/OE.17.000266 OPEXFF 1094-4087 Google Scholar

48. 

K. Kim et al., “High-resolution three-dimensional imaging of red blood cells parasitized by Plasmodium falciparum and in situ hemozoin crystals using optical diffraction tomography,” J. Biomed. Opt., 19 (1), 011005 (2014). http://dx.doi.org/10.1117/1.JBO.19.1.011005 Google Scholar

49. 

J. Lim et al., “Comparative study of iterative reconstruction algorithms for missing cone problems in optical diffraction tomography,” Opt. Express, 23 (13), 16933 –16948 (2015). http://dx.doi.org/10.1364/OE.23.016933 OPEXFF 1094-4087 Google Scholar

50. 

W. Choi et al., “Tomographic phase microscopy,” Nat. Methods, 4 (9), 717 –719 (2007). http://dx.doi.org/10.1038/nmeth1078 1548-7091 Google Scholar

51. 

Y. Park et al., “Refractive index maps and membrane dynamics of human red blood cells parasitized by Plasmodium falciparum,” Proc. Natl. Acad. Sci., 105 (37), 13730 (2008). http://dx.doi.org/10.1073/pnas.0806100105 Google Scholar

52. 

M. Greenwell, A. Willner and P. L. Kirk, “Human hair studies. III. Refractive index of crown hair,” J. Crim. Law Criminol., 31 (6), 746 –752 (1941). Google Scholar

53. 

L. J. Wolfram, K. Hall and I. Hui, “The mechanism of hair bleaching,” J. Soc. Cosmet. Chem, 21 875 –900 (1970). Google Scholar

54. 

A. Franbourg et al., “Current research on ethnic hair,” J. Am. Acad. Dermatol., 48 (6), S115 –S119 (2003). http://dx.doi.org/10.1067/mjd.2003.277 JAADDB 0190-9622 Google Scholar

55. 

N. Otberg et al., “Variations of hair follicle size and distribution in different body sites,” J. Invest. Dermatol., 122 (1), 14 –19 (2004). http://dx.doi.org/10.1046/j.0022-202X.2003.22110.x Google Scholar

56. 

O. Lunde, “A study of body hair density and distribution in normal women,” Am. J. Phys. Anthropol., 64 (2), 179 –184 (1984). http://dx.doi.org/10.1002/ajpa.1330640211 AJPNA9 Google Scholar

57. 

K. Lee and Y. Park, “Quantitative phase imaging unit,” Opt. Lett., 39 (12), 3630 –3633 (2014). http://dx.doi.org/10.1364/OL.39.003630 OPLEDP 0146-9592 Google Scholar

58. 

K. Kim et al., “Real-time visualization of 3-D dynamic microscopic objects using optical diffraction tomography,” Opt. Express, 21 (26), 32269 –32278 (2013). http://dx.doi.org/10.1364/OE.21.032269 OPEXFF 1094-4087 Google Scholar

59. 

J.-H. Jung, J. Jang and Y. Park, “Spectro-refractometry of individual microscopic objects using swept-source quantitative phase imaging,” Anal. Chem., 85 (21), 10519 –10525 (2013). http://dx.doi.org/10.1021/ac402521u ANCHAM 0003-2700 Google Scholar

60. 

J. Jung et al., “Biomedical applications of holographic microspectroscopy [Invited],” Appl. Opt., 53 (27), G111 –G122 (2014). http://dx.doi.org/10.1364/AO.53.00G111 APOPAI 0003-6935 Google Scholar

61. 

Y. Park et al., “Spectroscopic phase microscopy for quantifying hemoglobin concentrations in intact red blood cells,” Opt. Lett., 34 (23), 3668 –3670 (2009). http://dx.doi.org/10.1364/OL.34.003668 OPLEDP 0146-9592 Google Scholar

62. 

Y. Jang, J. Jang and Y. Park, “Dynamic spectroscopic phase microscopy for quantifying hemoglobin concentration and dynamic membrane fluctuation in red blood cells,” Opt. Express, 20 (9), 9673 –9681 (2012). http://dx.doi.org/10.1364/OE.20.009673 OPEXFF 1094-4087 Google Scholar

63. 

Y. Kim et al., “Polarization holographic microscopy for extracting spatio-temporally resolved Jones matrix,” Opt. Express, 20 (9), 9948 –9955 (2012). http://dx.doi.org/10.1364/OE.20.009948 OPEXFF 1094-4087 Google Scholar

64. 

Z. Wang et al., “Jones phase microscopy of transparent and anisotropic samples,” Opt. Lett., 33 (11), 1270 –1272 (2008). http://dx.doi.org/10.1364/OL.33.001270 OPLEDP 0146-9592 Google Scholar

65. 

Y. Cotte et al., “Marker-free phase nanoscopy,” Nat. Photonics, 7 (2), 113 –117 (2013). http://dx.doi.org/10.1038/nphoton.2012.329 NPAHBY 1749-4885 Google Scholar

66. 

R. Hoffmann et al., “Cytokines and growth factors influence hair growth in vitro. Possible implications for the pathogenesis and treatment of alopecia areata,” Arch. Dermatol. Res., 288 (3), 153 –156 (1996). http://dx.doi.org/10.1007/BF02505825 ADREDL 0340-3696 Google Scholar

67. 

M. P. Philpott, M. R. Green and T. Kealey, “Human hair growth in vitro,” J. Cell Sci., 97 463 –471 (1990). JNCSAI 0021-9533 Google Scholar

Biography

SangYun Lee received his BS degree in physics from Korea Advanced Institute of Science and Technology (KAIST). Currently, he is a PhD student at KAIST in the Department of Physics.

Kyoohyun Kim received his BS degree in physics from KAIST. Currently, he is a PhD student at KAIST in the Department of Physics.

Yuhyun Lee is currently a student at Daejeon Dongsin Science High School. He participated in this project as part of his research and education program.

Sungjin Park is currently a student at Daejeon Dongsin Science High School. He participated in this project as part of his research and education program.

Heejae Shin is currently a student at Daejeon Dongsin Science High School. He participated in this project as part of his research and education program.

Jongwon Yang is currently a student at Daejeon Dongsin Science High School. He participated in this project as part of his research and education program.

Kwanhong Ko received his BS degree in education of physics from Korea National University of Education. Starting off as a science teacher in Jinjam Middle School, Daejeon, Korea, since 1995 he has taught in Hanbat High School, Daejeon Science High School and Daejeon High School. He is currently teaching physics at Daejeon Dongsin Science High School.

HyunJoo Park received her PhD in physics from KAIST. Since 2013, she has worked as a postdoctoral fellow at KAIST.

YongKeun Park is an associate professor in the Department of Physics, KAIST. He received his PhD in medical physics and medical engineering from Harvard-MIT Health Science and Technology in 2010, and joined KAIST. To learn more about his research projects, visit his website: http://bmol.kaist.ac.kr.

© 2015 Society of Photo-Optical Instrumentation Engineers (SPIE) 1083-3668/2015/$25.00 © 2015 SPIE
SangYun Lee, Kyoohyun Kim, Yuhyun Lee, Sungjin Park, Heejae Shin, Jongwon Yang, Kwanhong Ko, HyunJoo Park, and YongKeun Park "Measurements of morphology and refractive indexes on human downy hairs using three-dimensional quantitative phase imaging," Journal of Biomedical Optics 20(11), 111207 (23 July 2015). https://doi.org/10.1117/1.JBO.20.11.111207
Published: 23 July 2015
Lens.org Logo
CITATIONS
Cited by 13 scholarly publications and 1 patent.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
3D image processing

Hydrogen

Refractive index

Phase imaging

Mirrors

3D metrology

Digital image correlation

Back to Top