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In vivo
measurements of cutaneous
melanin across spatial scales: using
multiphoton microscopy and spatial
frequency domain spectroscopy
Rolf B. Saager
Mihaela Balu
Viera Crosignani
Ata Sharif
Anthony J. Durkin
Kristen M. Kelly
Bruce J. Tromberg
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In vivo
measurements of cutaneous melanin across
spatial scales: using multiphoton microscopy and
spatial frequency domain spectroscopy
Rolf B. Saager,a,*Mihaela Balu,aViera Crosignani,aAta Sharif,aAnthony J. Durkin,aKristen M. Kelly,a,b and
Bruce J. Tromberga
aUniversity of California, Beckman Laser Institute, Laser Microbeam and Medical Program, Irvine, California, 92612, United States
bUniversity of California, Department of Dermatology, Irvine, California, 92697, United States
Abstract. The combined use of nonlinear optical microscopy and broadband reflectance techniques to assess
melanin concentration and distribution thickness in vivo over the full range of Fitzpatrick skin types is presented.
Twelve patients were measured using multiphoton microscopy (MPM) and spatial frequency domain spectros-
copy (SFDS) on both dorsal forearm and volar arm, which are generally sun-exposed and non-sun-exposed
areas, respectively. Both MPM and SFDS measured melanin volume fractions between ∼5% (skin type I
non-sun-exposed) and 20% (skin type VI sun exposed). MPM measured epidermal (anatomical) thickness
values ∼30–65 μm, while SFDS measured melanin distribution thickness based on diffuse optical path length.
There was a strong correlation between melanin concentration and melanin distribution (epidermal) thickness
measurements obtained using the two techniques. While SFDS does not have the ability to match the spatial
resolution of MPM, this study demonstrates that melanin content as quantified using SFDS is linearly correlated
with epidermal melanin as measured using MPM (R2¼0.8895). SFDS melanin distribution thickness is corre-
lated to MPM values (R2¼0.8131). These techniques can be used individually and/or in combination to
advance our understanding and guide therapies for pigmentation-related conditions as well as light-based treat-
ments across a full range of skin types. ©The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.
Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. [DOI: 10.1117/1.JBO.20.6
.066005]
Keywords: melanin; two-photon excited fluorescence; nonlinear optical microscopy; tissue spectroscopy; spatial frequency domain
imaging; optical properties.
Paper 140836R received Dec. 15, 2014; accepted for publication May 18, 2015; published online Jun. 12, 2015.
1 Introduction
Skin, like most biological tissues, is a stratified, well-differen-
tiated structure with compartments of distinct composition and
optical properties. Melanin, one of the most abundant chromo-
phores in human skin, appears in different concentration and
depth distribution depending on the baseline skin type1and
response to ultraviolet (UV) radiation exposure.2Both parame-
ters must be investigated in order to understand skin biology as
well as help to inform and guide diagnostic and therapeutic
procedures.
One of the long-standing challenges in skin imaging is to
develop quantitative methods for characterizing anatomy and
physiology in different skin structures. Methods, such as con-
focal reflectance imaging, optical coherence tomography
(OCT), and multiphoton microscopy (MPM), provide high-
resolution (e.g., ∼1–10 μm) structural images of skin with opti-
cal sectioning capability and limited penetration depth (e.g.,
∼100 μm–1mm). Unlike these microscopy-based methods,
model-based spatial and temporal modulation techniques
form quantitative images of tissue absorption and scattering
using multiply scattered photons. They take advantage of the
fact that tissue is a low-pass filter with frequency-dependent
light propagation properties. This enables limited optical sec-
tioning and quantitative measurements of tissue function at
low resolution.3
Spatial frequency domain imaging and spectroscopy (SFDI,
SFDS) employs structured light patterns (e.g., ∼0.05–0.5 mm−1)
that are projected onto tissue using a spatial light modulator,
such as a digital micromirror device (DMD).4–11 The spatial fre-
quency-dependent blurring and attenuation of these patterns is
determined by tissue optical properties (absorption, μa, and scat-
tering, μs’, coefficients). Quantitative maps of tissue absorption
and scattering properties can be generated using these methods
by fitting the measured frequency-dependent reflectance to a
mathematical model of light propagation. In addition to the
“low pass”filter characteristics of tissue in the spatial frequency
domain, there is significant optical frequency dependence to
light propagation.12–14 This well-known characteristic of light
propagation provides a convenient strategy for varying optical
path length and hence penetration depth over a range of fractions
of millimeters to several centimeters spanning the visible to
near-infrared (NIR) spectrum.
SFDI is typically performed in conjunction with an imaging
detector at discrete optical wavelengths in serial measurements.
When a spectrometer is used as the detection device, quantita-
tive tissue absorption and scattering spectra can be generated
in parallel from specific regions of interest. This approach,
first described as spatially modulated quantitative spectroscopy
*Address all correspondence to: Rolf B. Saager, E-mail: rsaager@uci.edu
Journal of Biomedical Optics 066005-1 June 2015 •Vol. 20(6)
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(SMoQS), [herein referred to as spatial frequency domain spec-
troscopy, (SFDS)], is particularly powerful because it provides
broadband absorption and scattering spectra over a wide range
of optical penetration depths. This enables additional tissue sec-
tioning control and can potentially provide quantitative mea-
surements of microscopic variations in skin components.15
Previous validation experiments, both of SMoQs and of other
spectroscopic imaging techniques that exploit layered models to
interpret the optical properties from skin tissue,15–18 have been
limited to simulated measurements or artificial tissue simulating
constructs. When these depth-dependent spectroscopic models
of light transport in tissue are developed, it is important to evalu-
ate the accuracy of these approaches in vivo to ensure that the
model and method adequately encompass the physiologically
relevant parameter space found in tissue. A significant challenge
to this approach is developing noninvasive methods with micro-
scopic-scale resolution that report on identical tissue contrast
elements and can independently verify model-based SFDI/
SFDS predictions.
In this work, we examine, for the first time, whether the use
of two well-established methods for controlling the optical
path length in tissue, optical wavelength and spatial frequency
modulation, can provide sufficient information content to
quantify melanin features in skin. All spatial frequency domain
measurements are compared to data obtained from multiphoton
microscopy using selective two-photon excited fluorescence
(TPEF) of melanin.19,20 Subjects ranged in pigmentation
from very light skin to dark skin (Fitzpatrick types I–VI).21
Using the depth-sectioning capabilities of MPM, we were
able to demonstrate, in the context of skin tissue, whether a
layered model interpretation of SFDS data is capable of lin-
early correlating in vivo average concentration and distribution
of melanin to within ∼15% and tens of microns, respectively.
Our goal is to develop inexpensive, noncontact spectroscopic
techniques for characterizing important microscopic skin fea-
tures in human subjects.
2 Methods
2.1 Study Design
All subjects were treated in strict compliance with the declara-
tion of Helsinki and the U.S. Code of Federal Regulations for
the protection of human subjects. The experiments were con-
ducted with the full consent of each subject using a protocol
approved by the Internal Review Board (IRB) for human experi-
ments in University of California, Irvine. This investigation was
carried out specifically under UCI IRB approved protocol 2008-
6307 with the objective of measuring in vivo cutaneous melanin
concentration and distribution using both MPM and SFDS. Data
were collected from 12 healthy subjects (ages 23–75) ranging
from very light skin types to very dark skin types. Skin type was
determined by a board certified dermatologist using Fitzpatrick
skin-type classification.21 Measurement sites included the
dorsal forearm and the upper inner arm. Multiple measure-
ments were taken using each technology at each anatomic
site on each subject. Because the dorsal forearm is usually a
sun-unprotected site while the upper inner arm is relatively
sun-unexposed site, we have chosen these locations for this
in vivo experiment with the expectation that with sun-protected
sites we will observe less melanin that will be apparent in sun-
unprotected sites.
2.2 Multiphoton Microscopy
2.2.1 MPTflex clinical tomograph
The laser-scanning-based clinical multiphoton tomograph,
MPTflex (JenLab GmbH, Germany) consists of a compact, turn-
key femtosecond laser (MaiTai Ti:Sappire oscillator, sub-100 fs,
80 MHz, tunable 690–1020 nm; Spectra Physics, Mountain
View, California), an articulated arm with NIR optics, and a
beam scanning module. The system has two photomultiplier
tube detectors employed for parallel acquisition of TPEF and
second-harmonic generation (SHG) signals. A customized met-
allic ring taped on the subject’s skin attaches magnetically to
the objective holder in the articulated arm, minimizing motion
artifacts. The excitation wavelengths used for this study were
790 nm for the acquisition of the cross-sectional (xz) images
and 880 nm for the acquisition of the z-stacks of horizontal
(xz) images. The z-stacks were obtained by moving the objective
in the zdirection, thus scanning at different depths in the skin.
The TPEF signal was detected over the spectral range of 410–
650 nm, while the SHG signal was detected over a narrow spec-
tral bandwidth of 385–405 nm through emission filters placed in
the TPEF and SHG detection channels, respectively. The xz
sections were 512 ×512 pixel images acquired at ∼6s∕frame.
The xz sections were 1024 ×1024 pixel images acquired at
∼30 s∕frame. We used a Zeiss objective (40×, 1.3 NA, oil
immersion) for focusing into the tissue.
We acquired the MPM data using two scanning modalities:
(1) xz scanning with z-stacks generated using en-face images
from the stratum corneum to the epidermal–dermal junction
(EDJ). The excitation wavelength selected for this acquisition
mode was 880 nm in order to maximize the melanin contrast
against TPEF signals from other components of the epidermis
(keratin, NADH/FAD). This was selected due to the fact that
melanin has a broad absorption spectrum (maximum around
300 nm gradually decreasing toward the NIR) and a broad
TPEF emission spectrum that peaks around 620–680 nm.19,20
The melanin appears in the MPM images as bright fluorescence
spots in the cellular cytoplasm that represents aggregates of mel-
anosomes. We used the data obtained by this scanning modality
to measure the melanin distribution as a function of depth and
the melanin volume fraction in the epidermis. Optical sections of
about 115 ×115 μm2at different depths ranging from 0 to about
60 μm(5μmsteps) were obtained. As the optical section is lim-
ited to a small scan field, to improve the overall characterization
of the examined skin, we acquired five stacks of images on each
site (volar arm and dorsal forearm) for all subjects. (2) xz scan-
ning which provided cross-sectional, “histology-like”images
from the stratum corneum to the superficial dermis. The exci-
tation wavelength selected for this acquisition mode was
790 nm in order to visualize both the epidermal cellular structure
and the collagen/elastin fibrillar components of the dermis. We
used the data obtained by this scanning modality to measure the
epidermal thickness. For each subject, we acquired one cross-
sectional image for each site.
2.2.2 MPM data processing
The quantitative parameters provided by the MPM data were:
(A) the melanin distribution as a function of depth in the
epidermis, (B) the melanin volume fraction in the epidermis,
and (C) the epidermal thickness. For measuring the parameters
related to melanin content (items A and B), we used the TPEF
Journal of Biomedical Optics 066005-2 June 2015 •Vol. 20(6)
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images acquired as z-stacks in five different locations on each
site. The TPEF images of the epidermis in each acquired image
stack were processed by using the following procedure: (1) If
present in the image, areas containing TPEF signal from keratin
in skin folds were cropped out and (2) The TPEF images were
converted into binary images using the same threshold for all
images. The threshold value of TPEF signal was defined as
Th1 ¼Mean Fl þ3×StDev;(1)
where Mean Fl is the mean intensity of all nonzero pixels in the
TPEF image and StDev is the standard deviation of pixel inten-
sity values from the mean. Pixels with intensity values larger
than the threshold value were defined as bright pixels and their
value set to 1. The rest of the pixels were defined as dark pixels
and their value was set to 0. The threshold value was selected as
an estimation for separating the pixels corresponding to melanin
from the background. Representative TPEF intensity images
and corresponding thresholded images are shown in Fig. 1.
(3) We defined the density of the bright pixels by the ratio
between the number of bright pixels (numerator) and total num-
ber of nonzero pixels (denominator) in the TPEF image thresh-
olded by the value of:
Th2 ¼Mean Fl þ1×StDev:(2)
The density of bright pixels was calculated for each image in
az-stack of several consecutive image planes (5μmstep) start-
ing from the top of the epidermis to the basal layer. The density
of bright pixels in each TPEF image was calculated by following
the procedure described above and represents an estimate of
the melanin concentration in the imaged epidermal layer.
(4) In order to calculate the distribution of melanin content
as a function of depth in the epidermis, we averaged the values
of melanin concentration corresponding to a specific depth in
each of the five acquired stacks. (5) For calculating the melanin
volume fraction in the epidermis, we averaged the melanin
volume fraction calculated for each of the five stacks. For
each stack, the melanin volume fraction was calculated as
VolFr ¼Vmelanin∕Vimaged ;(3)
where Vmelanin is the volume occupied by melanin and Vimaged is
the total imaged volume in the stack. The volume occupied by
melanin was defined as
Vmelanin ¼X
i
Ai
melanin ×h; (4)
where Ai
melanin is the area occupied by the bright pixels in each
binary image of the stack obtained by using the Th1 threshold,
and his the axial point-spread function value. The total imaged
volume in a stack was defined as
Vimaged ¼X
i
Ai
imaged ×h; (5)
where Ai
imaged is the area occupied by the bright pixels in each
binary image of the stack obtained by using the Th2 threshold.
We used the TPEF/SHG cross-sectional images acquired
at 790-nm excitation wavelength (Fig. 2) in order to estimate
epidermal thickness in order to provide reference points for
calculating the melanin volume fraction in the epidermis. The
epidermal thickness was measured in 10 different locations
starting from the end of the stratum corneum to the start of the
EDJ, as shown in Fig. 2. The epidermal thickness reported here,
in each case, represents the average of the ten measurements.
Fig. 1 Multiphoton microscopy (MPM) image processing for estima-
tion of melanin content: (a) raw two-photon excited fluorescence
(TPEF) image acquired in the non-sun-exposed site of skin type II,
(b) thresholded TPEF image shown in (a), (c) raw TPEF image
acquired in the non-sun-exposed site of skin type VI, (d) thresholded
TPEF image shown in (c). Scale bar is 20 μm.
Fig. 2 In vivo MPM cross-sectional image of human skin (xz scan).
Merged TPEF (green)/second-harmonic generation (SHG) (blue)
cross-sectional image acquired in volar forearm of type II skin. The
double-headed arrows show the locations at which the epidermal
thickness was measured. The scale bar is 20 μm.
Journal of Biomedical Optics 066005-3 June 2015 •Vol. 20(6)
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2.3 Spatial Frequency Domain Spectroscopy
2.3.1 Instrumentation
The SFDS instrument (also known as spatially modulated quan-
titative spectroscopy, SMoQS) that was used in this study was
developed at the Beckman Laser Institute (Fig. 3).
A 100 W Quartz-Tungsten-Halogen light source (Moritex,
MHF-D100LR) was coupled to a digital micro-mirror device,
DMD, (Texas Instruments, Inc.) in order to project structured
light patterns with spatial frequencies from 0 to 0.3 mm−1.
The projection field of view for this system is 22 ×17 mm2.
The distal end of a 1-mm core optical fiber (TechSpec, NA
0.39, Thorlabs, Inc.) was imaged at the center of the projection
field of view (1∶1magnification) and light collected by this fiber
is delivered to a spectrometer (Oriel 77480) enabling measure-
ments over the range 450–1000 nm. Crossed 2-inch diameter
wire-grid polarizing filters were used to reject specular reflec-
tion from the surface of the sample. The polarizer was inserted
between the DMD and projection optics and the analyzer was
placed between the fiber and collection optics.
In this study, six evenly spaced spatial frequencies were
acquired, ranging from 0 to 0.3 mm−1for a given anatomic loca-
tion (dorsal forearm and volar upper arm). At each location,
three measurements were acquired to ensure measurement con-
sistency, identify any potential motion artifacts during the meas-
urement acquisition, and help to improve the signal to noise. A
three-phase demodulation scheme was employed at each spatial
frequency, as has been described in the past, to isolate the spatial
frequency-dependent reflectance from the skin region of interest
from background contributions including those resulting from
ambient (room) light illumination. This resulted in a total of
12–18 spectral measurements for each measurement site.
Since tissue pigmentation ranged from types I to VI skin, inte-
gration time was automatically adjusted (LabView, National
Instruments Inc.,) to ensure that the full dynamic range of
the 16-bit detector was used for all subjects. Additionally, trip-
licate data were acquired for each projected pattern to improve
the signal-to-noise ratio. With this instrument configuration,
acquisition times ranged from 10 to 40 s, depending on the
amount of pigmentation in the region of interest.
2.3.2 SFDS data processing
As previously described in greater detail,8the three phases at
each spatial frequency are demodulated and calibrated against
a diffuse tissue-simulating phantom of known optical properties
to produce diffuse reflectance spectra as a function of spatial
frequency. At each wavelength, the reduction in AC reflectance
amplitude as a function of spatial frequency can be modeled and
analyzed via homogeneous Monte Carlo-based simulations via
discrete Hankel transformation of point-source reflectance pre-
dictions.8From this model, the contributions of absorption and
scattering can be independently identified at each wavelength,
resulting in broadband spectra for absorption and scattering
without the use of any spectral constraints or assumed
power-law dependence for reduced scattering. As this initial
step is based on a homogeneous model, the resulting absorption
and scattering spectra should be interpreted as a “measured opti-
cal response”from tissue as a function of the total volume inter-
rogated at each wavelength. The contribution of layer-specific
absorption and scattering will change as a function of the
total penetration depth interrogated, and hence as a function
of wavelength. Figure 4(a) shows the example (bulk) absorption
and reduced scattering spectra from subjects in this study, rep-
resenting four different skin type categories. Here, the scattering
spectrum for skin type VI no longer follows a simple power-law
like dependence. In general, scattering from dermal tissue will
be greater than that found in the epidermis, even with a high
concentration of melanin.22 The combination of a strong absorp-
tion coefficient at visible wavelengths and high melanin concen-
tration dramatically limits the depth penetrance at visible
wavelengths. Thus, the measured scattering coefficient in the
visible wavelength range is primarily related to the scattering
properties of epidermis. The penetration depth increases in
the NIR resulting in the increased contribution from the rela-
tively higher scattering dermal tissue. For the example presented
in Fig. 4(a), we not only measure the wavelength dependence of
scattering from epidermal and dermal tissues, but also the depth-
dependent transition between the relative contributions of these
scattering regimes (as a function of depth penetrance).
From these quantitative spectra, we can not only determine
the bulk concentration of melanin, hemoglobin, water, lipids,
and carotenoids in vivo, but also leverage the differential pen-
etration depths of light between the visible and NIR regimes to
isolate chomophore concentrations in depth. While the absorp-
tion spectra contain distinct features that can be used to separate
and quantify these chromophores relative to the total volume of
tissue interrogated by light (up to ∼5mmthrough this specific
approach), the combination of the wavelength-specific absorp-
tion and scattering provides us with an opportunity to estimate
the depth penetrance into tissue of that specific wavelength.23,24
As the penetrance of visible light into tissue is significantly less
than that from the NIR, we can leverage the differential volumes
and sensitivities to depth-specific chromophores in these spec-
tral regions to quantify melanin volume fraction, average depth
distribution and other chromophores found in tissue.15 We have
tested this model-based, depth segmented approach in layered
tissue simulating phantoms25 and in vivo benign pigmented
lesions, correlating the mean depths of pigmentation in vivo
to high-frequency ultrasound.26
To obtain depth-specific chromophore concentrations and
depth distribution thickness, we used the method described
by Saager et al.15 For this study, we used the spectral regions
of 450–600 nm and 650–950 nm to represent the visible and
Fig. 3 System diagram for spatially modulated quantitative spectros-
copy (SMoQS) instrument used in initial experiments.
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NIR spectral regions in the aforementioned method. As this
method is based on interpreting measured spectroscopic data
in terms of a two-layer model, the outputs of this method are
the top layer (optical) thickness (as determined by the absorption
signature of melanin, relative to the mean photon path length
traveled through the tissue) and the chromophore concentrations
specific to the respective layers. In the context of this investiga-
tion, we are interested in whether this diffuse optics-based SFDS
modality can isolate and assess the top layer properties, namely,
layer-specific melanin concentration and melanin layer (mean
distribution) thickness that is correlative to microscopy deter-
mined results. Figure 4(b) shows an example of this two-
layer, depth segmented decomposition of skin absorption from
in vivo tissue (skin type III, volar upper arm).
3 Results
3.1 Determination of Melanin Concentration In Vivo
3.1.1 Multiphoton microscopy
Melanin distribution as a function of depth in the epidermis was
measured in TPEF z-stacks from the top of the epidermis to the
EDJ. Generally, we measured a higher percentage of melanin in
the basal and supra-basal layers (10–15 μmfrom the basal layer)
compared to the upper epidermal layers (15–40 μmfrom the
basal layer). Type VI dorsal forearm exhibited a more uniform
distribution of melanin as a function of epidermal depth com-
pared to other skin types. Distribution of melanin concentration
as a function of depth in the epidermis for skin types I and VI is
shown in Fig. 5. The position across the epidermis was normal-
ized to the thickness of the epidermis in order to account for
variations in epidermal thickness for different skin locations
and different individuals. Therefore, fewer points in the plot rep-
resenting the percentage of melanin content across epidermal
layers in Fig. 5are an indication of a thinner epidermis.
There is a higher percentage of melanin content regardless of
depth and a more uniform melanin distribution as a function of
depth in type VI dorsal forearm compared to type I skin. These
effects are due to differences in melanosome biology; dark (type
VI) skin is characterized by intact melanosomes that are not
degraded by lysosomal enzymes in the upper epidermal layers
(stratum spinosum and granulosum).27 In contrast, light skin
melanosomes are degraded and only persist in the upper layers
as “melanin dust.”27
The melanin volume fraction estimated from the MPM data
for different skin types is shown in Fig. 6(a).
For the volar arm, an area generally not exposed to the sun,
melanin volume fraction increases with skin type and exhibits an
overall change of ∼2×from types I to VI. However, dorsal fore-
arm melanin volume fraction was not correlated with the skin
type. This is due to the fact that the melanin content in the dorsal
forearm, which typically has significant sun exposure, is also
related to factors such as frequency and duration of sun expo-
sure, the presence of freckles with increased melanin content,28
and the sun protection routine for each individual. Figure 6(a)
shows that the dorsal forearms of skin types I and II are particu-
larly susceptible to this effect.
3.1.2 Spatial frequency domain spectroscopy
Figure 6(b) shows that higher melanin concentration is also
detected using SFDS for the dorsal forearm relative to the
volar upper arm for each subject. For the presentation of
these data, we assumed a 5% error based on our initial validation
work of the empirical model used to isolate layer-specific
chromophore concentrations.15 Although the Fitzpatrick skin
type scale is not a direct representation of skin pigmentation,
but rather a subjective, scaled assessment of skin response to
sun exposure, there was a general trend of increasing detected
melanin concentration with increasing skin type. A factor of ∼2-
fold increase was observed comparing volar types I to VI, while
∼3-fold higher % melanin values were obtained comparing dor-
sal types I to VI. These relative ranges of increase are in agree-
ment with other quantification methods of melanin across skin
Fig. 4 (a) Representative bulk absorption (top) and reduced scattering (bottom) coefficient spectra from
Fitzpatrick skin types II, III, IV, and VI as measured by spatial frequency domain spectroscopy (SFDS).
Spectra are derived from three measurements taken from individual subjects (dorsal forearm) at each of
the four skin types. (b) Example of depth-sensitive spectral decomposition of in vivo skin tissue (volar
upper arm of a single subject, skin type III). Blue: tissue absorption measured by SFDS; black (dashed
line): depth-specific quantification of melanin relative to total volume of tissue interrogated at each wave-
length; red: absorption features specific to the dermis and deeper tissue; green (dotted line): least-
squares fit for oxy/deoxy hemoglobin. (Residual absorption features between red and green correspond
to carotenoids, lipids, and water).
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types and sun-exposed skin regions.29–33 Unlike the melanin vol-
ume fraction measured by MPM in the dorsal forearm in skin
types I and II, SFDS reported a general increase in melanin con-
tent as a function of skin type. The key distinction between these
imaging modalities in this particular case is the volume of inter-
rogation. Discussed in more detail in Sec. 4, SFDS interrogates
∼50-fold larger volume than MPM. In this case, it spatially
averages heterogeneous pigmentation common in sun-exposed
areas of skin for these skin types.
3.2 Melanin Distribution Thickness
3.2.1 Multiphoton microscopy
The epidermal thickness measured for different skin types is
shown in Fig. 7(a).
MPM measurements showed no correlation between epider-
mal thickness and skin type. However, we measured a signifi-
cant difference in the overall epidermal thickness of volar versus
dorsal arm (40 μmversus 50 μm, respectively, p¼0.007).
3.2.2 Spatial frequency domain spectroscopy
Similarly, Fig. 7(b) shows that the melanin distribution thickness
was greater for the dorsal forearm that for the volar upper arm.
This has been suggested by other literature for two general rea-
sons: (1) epidermal thickness can vary in skin based on anatomi-
cal location;34 namely, the epidermal thickness of the dorsal
forearm has been reported to be thicker, on average, than that
in the volar upper arm. As with the determination of layer-spe-
cific melanin concentration, we also assumed a 5% error for
Fig. 5 Melanin distribution as a function of depth in the epidermis in skin types I and VI. Percentage of
melanin content in layers across epidermis from the basal layer to the top of the epidermis for volar arm
(circle) and dorsal forearm (asterisk) corresponding to skin type I (a) and skin type VI (b). Data represent
the average of the melanin content in layers of five different stacks acquired for the same site. The error
bars represent standard deviation of the five measurements. The position across the epidermis was
normalized to the thickness of the epidermis.
Fig. 6 Melanin volume fraction estimated from MPM and SFDS data. (a) Melanin volume fraction as
function of skin type, corresponding to volar upper arm (circle) and dorsal forearm (asterisk) as deter-
mined by MPM. Data represent the average of the melanin volume fraction calculated for five stacks
acquired for the same site (volar or dorsal) in each of the 12 patients. The error bars represent standard
deviation of the five measurements. (b) Average melanin concentration determined by SFDS. Red (aster-
isks) are estimates from the dorsal forearm as a function of assessed skin type, black (circles) are esti-
mates from the volar upper arm. The error bars represent an estimated model error from SFDS layered
decomposition.
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distribution thickness based on our initial validation work of the
empirical model used to isolate layer-specific chromophore
concentrations.15
It is worth noting that for more heavily pigmented skin (IV-
VI), there is an observed correlation between pigmentation and
estimated melanin distribution thickness that was not evident in
the MPM measurements. We discuss this outcome in Sec. 4.
3.3 Correlation of Results Between Multiphoton
Microscopy and SFDS
Figure 8shows the correlation between SFDS and MPM for
dorsal and volar forearms in estimates of (A) % melanin and
(B) melanin distribution thickness. Data are shown for all
patients and measurement sites. The MPM–SFDI correlation
is slightly better for determining % melanin content (R2¼
0.8895) versus melanin distribution thickness (R2¼0.8131).
This is likely due to the fact that MPM provides direct visuali-
zation of layer thickness, while SFDS utilizes multiple scattered
light and model-based analysis to obtain a similar number. This
is likely due to a difference in the mechanisms that each tech-
nology employs in order to access sources of optical contrast.
MPM provides direct melanin visualization of layer thickness,
while SFDS utilizes multiple scattered light and model-based
analysis to access melanin contrast. In this case, SFDS reports
the optical path length within each layer. Distances reported by
Fig. 7 Epidermal and melanin distribution thickness estimated from MPM and SFDS data, respectively.
(a) Epidermal thickness as a function of skin type corresponding to volar arm (circle) and dorsal forearm
(asterisk) as determined by MPM. Data represent the average of the epidermal thickness calculated in 10
different locations across the MPM images acquired as xz scans as shown in Fig. 2. The error bars
represent standard deviation of the 10 measurements. (b) Melanin distribution thickness as determined
by SFDS. Red (asterisk) refers to the dorsal forearm and black (circle) the volar upper arm. Note that
the thickness values presented are in terms of optical path length and not actual anatomic thickness.
Each data point represents a measurement corresponding to each of the 12 patients, acquired from
the volar upper arm and dorsal forearm. The error bars represent an estimated model error from
SFDS layered decomposition.
Fig. 8 Correlation of mean (a) melanin concentration (N¼24) and (b) melanin distribution thickness
(N¼18) between MPM and SFDS. Here the mean, site-specific [i.e., volar upper arm (black-circle)
and dorsal forearm (red-asterisk)] results from each subject are plotted between each modality, inde-
pendent of any skin type assessment. The error bars represent the variance within the measurement
volume along the MPM axis and the estimated model error along the SFDS axis.
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SFDS will be longer than those reported by MPM since photon
paths that are recorded by SFDS will travel a more tortuous path
through the epidermis.
4 Discussion
In this work we examine, for the first time, how two optical
methods that probe different spatial scales can be used to quan-
tify microscopic melanin features in skin. SFDS measurements
are compared to label-free, MPM images of melanin fluores-
cence. The two modalities were used to measure, in vivo:
(1) melanin volume fraction and (2) epidermal and melanin dis-
tribution thickness in human skin. In addition, MPM was able to
determine melanin distribution as a function of depth in the
assessed skin types.
Both MPM and SFDS measured melanin volume fraction
values that ranged between ∼5% (skin type I non-sun-exposed)
and 20% (skin type VI sun exposed). MPM measured epidermal
(anatomical) thickness values between ∼30 μmand ∼65 μm,
while SFDS measured melanin distribution (optical) thickness
values that ranged between ∼90 μmand ∼105 μmfor both
sun-exposed and non-sun-exposed tissue locations. Although
there was a strong agreement between melanin concentration
and melanin distribution (epidermal) thickness values measured
by the two techniques (R2¼0.8895 and 0.8131, respectively),
the sources of contrast, resolution limits and fields of view differ
between these approaches. The implications and potential meas-
urement errors/biases derived from these differences are dis-
cussed below in the context of the determination of melanin
concentration and melanin distribution thickness.
The values corresponding to melanin volume fraction mea-
sured by the two modalities were comparable and followed a
similar trend for the non-sun-exposed area (volar upper arm),
as shown in Fig. (6). However, for the sun-exposed area (dorsal
forearm) melanin volume fraction values measured by MPM did
not correlate with the skin type and did not follow a similar trend
with the values measured by SFDS. For the sun-exposed area
(dorsal forearm), the values of melanin volume fraction for
skin types I and II measured by MPM were higher compared
to the values measured by SFDS. While Sec. 3.1.1 includes
some explanation for the relatively elevated melanin concentra-
tion in lighter skin types observed by MPM, there are other
differences between these modalities that may bias this concen-
tration determination that are worthy of further discussion.
Although MPM and SFDS both measure the optical properties
of tissue, the spatial scale that each technique accesses differs
somewhat between the two approaches. Thus, the specific man-
ner in which melanin contrast manifests is different between the
two techniques. For example, MPM quantifies the concentration
and distribution of melanin by recording high-resolution three-
dimensional images of melanin TPEF. Although selective wave-
length excitation and background suppression minimize contri-
butions from other fluorophores, these effects could impact the
accuracy of our estimates. Additionally, fluorescence re-absorp-
tion by melanin in highly pigmented skin might lead to under-
estimates by MPM.
In contrast, the concentration and volume fraction of melanin
determined using SFDS employs the melanin extinction coeffi-
cient spectrum reported by Jacques and McAuliffe35 This
extinction coefficient spectrum is simply reported as that of
melanin. In fact, melanin in tissue typically contains contribu-
tions from both pheomelanin and eumelanin, which may vary
slightly in concentration in relation to one another on both an
intersubject and intrasubject basis. The pure forms of these
melanin sub-types differ from one another in terms of spectral
shape (most notably in the visible), thus the measured SFDS
spectrum is likely to differ slightly from the melanin chromo-
phore spectrum.36
A second major difference in these methods is that MPM
determines melanin volume fraction over a smaller volume
than that interrogated by SFDS. The fields of view used by
MPM in this study span ∼115 ×115 μm2. Given that the thick-
nesses over which melanin is distributed are ∼50 μm, MPM
uses only a volume of 0.0007 mm3to estimate volume fraction.
As shown in Fig. 1, distributions of melanin over this field of
view can be quite heterogeneous, particularly in the cases of skin
type I where freckling may be present. SFDS, by comparison,
collects light from tissue over a large spot size ∼1mmin diam-
eter. This results in an estimated volume of interrogation of at
least ∼0.04 mm3around where the melanin is distributed. SFDS
measurements, therefore, have ∼50-fold greater volume averag-
ing of melanin concentration from tissue.
Despite the potential sources of error and measurement bias
from each optical modality, the linear fit of melanin concentra-
tion of all skin types and locations describes a line that nearly
has a slope of 1 (0.8) and an intercept near zero (−0.3%)
[Fig. 8(a)]. This indicates that there is a ∼20% underestimation
of melanin by SFDS relative to the values calculated by MPM.
We believe this is consistent with the fact that the total volume of
the melanin layer is defined by each modality in a slightly differ-
ent way. SFDS uses a two-layer model that assumes a homo-
geneous distribution within the segmented tissue layer, while
MPM only considers the volume where melanin exists within
the tissue. In this case, MPM defines a slightly smaller volume
(∼10%–20%) and hence reports a commensurately higher mela-
nin concentration.
Regarding the epidermal and melanin distribution thickness,
for more heavily pigmented skin (IV-VI), there was an observed
correlation between pigmentation and estimated melanin distri-
bution thickness in the SFDS data that were not evident in the
MPM measurements (Fig. 7). Additionally, the depths obtained
using SFDS were approximately 2–3 times larger than those
measured using MPM. These differences stem from how
thickness is defined in the spectroscopic layered model.
Determination of melanin distribution thickness in the current
spectroscopic method is based on optical path length estimators
rather than anatomic thickness. In this case, the distances
reported here reflect the mean distance photons travel through
each region of tissue. Because of multiple light scattering, this is
a tortuous path rather than just a straight line.
This effect is enhanced due to the fact that melanin has a
significant scattering cross section due to its packaging in
high-index, micron-dimension melanosomes. Therefore, the
“optical thickness”would not only be a greater distance than
just a straight line through tissue, but it would also significantly
increase in the presence of: (1) an increase in melanin concen-
tration and (2) an increased volume of melanin distribution. As
shown by our MPM results, both of these conditions are present
in the darker skin types. Here, melanin will significantly
increase epidermal light scattering, and hence increase the effec-
tive path length described by light transport models.
Apart from the differences discussed above that mainly stem
from the two modalities probing different spatial scales, there
was a strong correlation between the results measured by the
two techniques (Fig. 8). These data suggest that SFDS data
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Saager et al.: In vivo measurements of cutaneous melanin across spatial scales. . .
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are capable of correlating, in vivo, the average concentration and
distribution of melanin to within ∼15% and tens of microns,
respectively. Data also show the potential of the two techniques
to be used individually and/or in combination to assess the con-
centration and distribution/depth of melanin in human skin. The
ability to quantify these parameters across spatial scales can play
a critical role in advancing our understanding of the distribution
and organization of melanin in tissue, specifically in the case of
noninvasively measuring its progression into dermal tissue, as
well as guide and inform therapies.
While there are spatial scales where quantitative optical
information from both MPM and SFDS overlap, they are com-
plementary technologies. MPM provides subcellular resolution
and structural detail. In addition to quantifying the volume
fraction and depth resolved distribution of melanin, MPM can
visualize and quantify many other properties, such as cellular
structure and organization, collagen, elastin, keratin, and so
on. MPM is, however, limited in penetration depth to a few hun-
dred microns beneath the surface and has a small field of view.
SFDS may have relatively limited spatial resolution; how-
ever, it can image and report functional information (hemoglo-
bin, lipids, water, and so on.) from deeper tissues, such as the
dermis and subcutaneous fat, in addition to characterizing and
isolating the effects of melanin. Macroscale spectroscopic tech-
niques may never match all of the spatial and depth sectioning
capabilities of microscopy, but they typically hold a few distinct
advantages in terms of clinical translation and use: (1) wide field
imaging, (2) acquisition speed, and (3) cost. As a wide-field im-
aging approach, SFDS is an inherently scalable modality in
terms of field of view. In its current implementation, it is a single
point spectroscopy instrument, but this approach has the
capability to scale its field of view through multiplexing the
detection channel or multispectral imaging through the use of
discrete spectral wavelengths at the source.
5 Conclusions
We have, for the first time, demonstrated the correlation between
in vivo cutaneous melanin as measured microscopically using
MPM and macroscopically using SFDS. MPM provides
detailed structural images at the cellular level to the upper der-
mis; SFDS can provide quantitative layer-averaged content from
the epidermis and dermis up to depths of ∼5mm. Measure-
ments were made using both techniques across a broad range
of skin pigmentation. Despite significant differences in sam-
pling volume and depth between the two methods, statistical
correlations (0.8895 for melanin concentration, 0.8131 for
thickness distribution) demonstrate that the data collected by
these two modalities intersect at a spatial scale relevant to the
structure of skin. Because SFDS offers wide fields of view
and enhanced tissue penetration depth, our results suggest that
spectroscopic approaches could be rapidly and effectively trans-
lated to the clinic as low-cost noninvasive screening tools to
independently assess melanin concentration and invasiveness.
Acknowledgments
We acknowledge support from NIH: P41EB015890 (Laser
Microbeam Medical Program, LAMMP), R42GM077713,
R21EB014440, and UL1 TR000153; as well as Unilever, Inc.
and the Beckman Foundation. We also thank JenLab, GmbH for
loan of the MPTflex clinical tomograph. The content is solely the
responsibility of the authors and does not necessarily represent
the official views of the NIH. BJT and AJD are co-inventors of
SFDI technology described in this paper, the patents for which
are owned by the regents of the University of California. Some
of these patents have been licensed to private companies; includ-
ing Modulated Imaging, Inc. BJT and AJD are co-founders of
Modulated Imaging, Inc.
References
1. H. Y. Thong et al., “The patterns of melanosome distribution in kera-
tinocytes of human skin as one determining factor of skin colour,”
Br. J. Dermatol. 149(3), 498–505 (2003).
2. K. P. Nielsen et al., “The importance of the depth distribution of melanin
in skin for DNA protection and other photobiological processes,”
J. Photochem. Photobiol. B 82(3), 194–198 (2006).
3. T. D. O’Sullivan et al., “Diffuse optical imaging using spatially and
temporally modulated light,”J. Biomed. Opt. 17(7), 071311 (2012).
4. K. P. Nadeau et al., “Quantitative assessment of renal arterial occlusion
in a porcine model using spatial frequency domain imaging,”Opt. Lett.
38(18), 3566–3599 (2013).
5. A. J. Lin et al., “Visible spatial frequency domain imaging with a digital
light microprojector,”J. Biomed. Opt. 18(9), 096007 (2013).
6. A. M. Laughney et al., “Spectral discrimination of breast pathologies in
situ using spatial frequency domain imaging,”Breast Cancer Res.
15(4), R61 (2013).
7. J. R. Weber et al., “Multispectral imaging of tissue absorption and scat-
tering using spatial frequency domain imaging and a computed-tomog-
raphy imaging spectrometer,”J. Biomed. Opt. 16(1), 011015 (2011).
8. D. J. Cuccia et al., “Quantitation and mapping of tissue optical proper-
ties using modulated imaging,”J. Biomed. Opt. 14(2), 024012 (2009).
9. R. B. Saager, D. J. Cuccia, and A. J. Durkin, “Determination of optical
properties of turbid media spanning visible and near-infrared regimes
via spatially modulated quantitative spectroscopy,”J. Biomed. Opt.
15(1), 017012 (2010).
10. R. H. Wilson et al., “Quantitative short-wave infrared multispectral im-
aging of
in vivo
tissue optical properties,”J. Biomed. Opt. 19(8), 086011
(2014).
11. R. B. Saager et al., “A light emitting diode (LED) based spatial fre-
quency domain imaging system for optimization of photodynamic
therapy of nonmelanoma skin cancer: quantitative reflectance imaging,”
Lasers Surg. Med. 45(4), 207–215 (2013).
12. S. D. Konecky et al., “Quantitative optical tomography of sub-surface
heterogeneities using spatially modulated structured light,”Opt. Express
17(17), 14780–14790 (2009).
13. S. D. Konecky et al., “Spatial frequency domain tomography of proto-
porphyrin IX fluorescence in preclinical glioma models,”J. Biomed.
Opt. 17(5), 056008 (2012).
14. A. Mazhar et al., “Structured illumination enhances resolution and con-
trast in thick tissue fluorescence imaging,”J. Biomed. Opt. 15(1),
010506 (2010).
15. R. B. Saager et al., “Method for depth-resolved quantitation of optical
properties in layered media using spatially modulated quantitative spec-
troscopy,”J. Biomed. Opt. 16(7), 077002 (2011).
16. D. Yudovsky, J. Q. Nguyen, and A. J. Durkin, “
In vivo
spatial frequency
domain spectroscopy of two layer media,”J. Biomed. Opt. 17(10),
107006 (2012).
17. P. Naglic et al., “Applicability of diffusion approximation in analysis of
diffuse reflectance spectra from healthy human skin,”Proc. SPIE 9032,
90320N (2013).
18. I. Fredriksson, M. Larsson, and T. Stromberg, “Inverse Monte Carlo
method in a multilayered tissue model for diffuse reflectance spectros-
copy,”J. Biomed. Opt. 17(4), 047004 (2012).
19. T. B. Krasieva et al., “Two-photon excited fluorescence lifetime imaging
and spectroscopy of melanins
in vitro
and
in vivo
,”J. Biomed. Opt.
18(3), 031107 (2013).
20. K. Teuchner et al., “Femtosecond two-photon excited fluorescence of
melanin,”Photochem. Photobiol. 70(2), 146–151 (1999).
21. T. B. Fitzpatrick, “The validity and practicality of sun-reactive skin
types I through VI,”Arch. Dermatol. 124(6), 869–871 (1988).
22. R. Marchesini et al., “Optical properties of
in vitro
epidermis and
their possible relationship with optical properties of
in vivo
skin,”
J. Photochem. Photobiol. B 16(2), 127–140 (1992).
Journal of Biomedical Optics 066005-9 June 2015 •Vol. 20(6)
Saager et al.: In vivo measurements of cutaneous melanin across spatial scales. . .
Downloaded From: http://biomedicaloptics.spiedigitallibrary.org/ on 06/12/2015 Terms of Use: http://spiedl.org/terms
23. S. A. Carp, S. A. Prahl, and V. Venugopalan, “Radiative transport in the
delta-P1 approximation: accuracy of fluence rate and optical penetration
depth predictions in turbid semi-infinite media,”J. Biomed. Opt. 9(3),
632–647 (2004).
24. S. L. Jacques, “Simple theory, measurements, and rules of thumb for
dosimetry during photodynamic therapy,”Proc. SPIE 1065, 100–108
(1989).
25. R. B. Saager et al., “Multi-layer silicone phantoms for the evaluation of
quantitative optical techniques in skin imaging,”Proc. SPIE 7567,
756706 (2010).
26. R. B. Saager et al., “Development of spatial frequency domain instru-
ment for the quantification of layer specific optical properties of
pigmented lesions,”in Proc. Biomedical Optics and 3-D Imaging,
Optical Society of America, Miami, Florida (2012).
27. M. Brenner and V. J. Hearing, “The protective role of melanin against
UV damage in human skin,”Photochem. Photobiol. 84(3), 539–549
(2008).
28. J. P. Ortonne, “Pigmentary changes of the ageing skin,”Br. J. Dermatol.
122(Suppl 35), 21–28 (1990).
29. S. Alaluf et al., “Ethnic variation in melanin content and composition in
photoexposed and photoprotected human skin,”Pigment Cell Res.
15(2), 112–118 (2002).
30. C. D. Kaur and S. Saraf, “Skin care assessment on the basis of skin
hydration, melanin, erythema and sebum at various body sites,”Int.
J. Pharm. Pharm. Sci. 3(4), 209–213 (2011).
31. P. J. Matts, P. J. Dykes, and R. Marks, “The distribution of melanin in
skin determined
in vivo
,”Br. J. Dermatol. 156(4), 620–628 (2007).
32. T. Dwyer et al., “Cutaneous melanin density of Caucasians measured by
spectrophotometry and risk of malignant melanoma, basal cell carci-
noma, and squamous cell carcinoma of the skin,”Am. J. Epidemiol.
155(7), 614–621 (2002).
33. T. Tadokoro et al., “Mechanisms of skin tanning in different racial/
ethnic groups in response to ultraviolet radiation,”J. Invest. Dermatol.
124(6), 1326–1332 (2005).
34. J. T. Whitton and J. D. Everall, “The thickness of the epidermis,”Br. J.
Dermatol. 89(5), 467–476 (1973).
35. S. L. Jacques and D. J. McAuliffe, “The melanosome: threshold temper-
ature for explosive vaporization and internal absorption coefficient during
pulsed laser irradiation,”Photochem. Photobiol. 53(6), 769–775 (1991).
36. T. Sarna and H. A. Swartz, Eds., The Physical Properties of Melanins.
The Pigmentary System: Physiology and Pathophysiology, 2nd ed.,
pp. 311–341, Blackwell Publishing Ltd, Oxford, UK (1998).
Biographies of the authors are not available.
Journal of Biomedical Optics 066005-10 June 2015 •Vol. 20(6)
Saager et al.: In vivo measurements of cutaneous melanin across spatial scales. . .
Downloaded From: http://biomedicaloptics.spiedigitallibrary.org/ on 06/12/2015 Terms of Use: http://spiedl.org/terms
