spie proceedings [spie spie bios - san francisco, california, usa (saturday 2 february 2013)]...

In vivo skin chromophore mapping using

a multimode imaging dermoscope (SkinSpect™) Nicholas MacKinnon

a, Fartash Vasefi

a, Eugene Gussakovsky

a,

Gregory Bearman a, Robert Chave

a, Daniel L. Farkas

a, b,*

a Spectral Molecular Imaging Inc., 250 N. Robertson Blvd, Beverly Hills, CA, USA 90211

b Department of Biomedical Engineering, University of Southern California,

Los Angeles, CA, USA 90089

*(Corresponding author: [email protected])

ABSTRACT

We introduce a multimode dermoscope (SkinSpect™) we developed for early detection of

melanoma by combining fluorescence, polarization and hyperspectral imaging. Acquired reflection

image datacubes were input to a wavelength-dependent linear model to extract the relative

contributions of skin chromophores at every pixel. The oxy-hemoglobin, deoxy hemoglobin,

melanin concentrations, and hemoglobin oxygen saturation by the single step linear least square

fitting and Kubelka-Munk tissue model using cross polarization data cubes were presented. The

comprehensive data obtained by SkinSpect can be utilized to improve the accuracy of skin

chromophore decomposition algorithm with less computation cost. As an example in this work, the

deoxy-hemoglobin over-estimation error in highly pigmented lesion due to melanin and deoxy

hemoglobin spectral cross talk were analyzed and corrected using two-step linear least square fitting

procedure at different wavelength ranges. The proposed method also tested in skin with underlying

vein area for validating the proof of concept.

Keywords: hyperspectral imaging, polarization, autofluorescence, multimode dermoscope, skin cancer, melanoma

1. INTRODUCTION

Melanoma is an increasingly lethal form of skin cancer, especially when detected in later stages.

Melanoma risk during a lifetime has increased from 1:1500 in 1935 to 1:58 in 2009 [1], and is the

fastest growing cancer both in the U.S. and worldwide [2]. The National Cancer Institute estimates

that 76,250 patients will be diagnosed with melanoma of the skin in 2012 and that 9,180, or more

than one patient per hour, will die [3]. Survival rates strongly favor early diagnosis, ranging from

98.2% for early, primary site detection to at best 15.1% for late or metastasized detection, during a

recent 5-year study [4]. Approximately $2.4 billion is spent in the United States each year on

melanoma treatment [5]. There is also a radical cost decrement for treatment when melanoma is

detected in early stages: at current costs of treatment it is now approximately 2200 percent more

expensive to treat late (T4b) lesions, thicker than 4.0 mm with ulceration, compared to Melanoma in

situ when the tumor remains in the outermost layer of skin or epidermis. While this cost difference

and increase in favorable treatment outcomes occurs when melanoma is detected earlier [1], despite

great effort worldwide, relatively little significant advancement in this area (or treatment, for that

matter) has occurred. Therefore early detection is by far the most effective means of fighting this

disease that accounts for 75% of all skin cancer deaths [2], and technological advances and

academia-industry collaborations are needed to achieve this goal [6].

Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XI, edited byDaniel L. Farkas, Dan V. Nicolau, Robert C. Leif, Proc. of SPIE Vol. 8587, 85870U

© 2013 SPIE · CCC code: 1605-7422/13/$18 · doi: 10.1117/12.2005587

Proc. of SPIE Vol. 8587 85870U-1

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The present common standard in melanoma patient care is a dermatologists’ visual examination,

such as the ABCDE procedure [7]- [9] or revised 7-point checklist [8][10][11] in which the

practitioner (usually a dermatologist), looks for abnormalities in shape, size and color [12]. Around

2 million biopsies are performed annually in the US to detect melanoma, and the vast majority of

these (over 80%) are benign [13]. An alternative approach to enhance ABCDE evaluation can

include a dermoscope with (low power) magnification and/or specific illumination [14][15].

More recently, more complex imaging and/or sensing systems that quantify anatomical and

physiological information about skin constituents, such as SIAscope IV, have been developed [16].

Other systems, such as the MelaFind imaging system and the Verisante Aura Raman spectroscopy

device, use “black box” methods based on statistical classifiers [17][18]. Although all of these

optical systems provide (needed) high sensitivity, they have not achieved the desired level of

specificity in diagnosis. For these devices, the black box approach assumes there is an optical

signature difference between normal and cancerous tissues and addresses this by using statistical

classifiers and training-based discrimination functions. This method has shown reduced success as

these studies move from smaller to larger populations.

A telling example is the reduction in specificity for the MelaFind imaging system from studies

reported in 2001 to studies reported to the FDA in 2011 [19][20][21][22]. A similar drop in

specificity affected the Verisante Aura, as shown in the Table 1.

Table 1. Reduced specificity in Melanoma detection systems using classifiers when tested in larger populations.

MelaFind Early trials (%) Sensitivity: 100

Specificity: 85

Larger trials (%)

Sensitivity: 98.3

Specificity: 9.9

Verisante Aura Early trials (%) Sensitivity: 100

Specificity: 70

Larger trials (%) Sensitivity: 99

Specificity: 11.5

As reflected in these data the statistical classification approach is encountering fundamental barriers

to success as promising clinical devices show reduced performance when they are evaluated in

larger studies. Why these recent commercial attempts to use spectral imaging and spectroscopy have

not maintained their initial promise, especially in terms of specificity, will require detailed analysis

by the developers of these systems. However, when contemplating this approach to analysis, some

considerations may be drawn from the many publications from the large NIH program project

“Optical Technologies for Cervical Neoplasia” (OTCN) which examined technology assessment

models applied to new optical technologies. One of us (MacKinnon) was part of this project team,

that looked at optical spectroscopy and imaging as well as digital pathology, and applied both a

variety of statistical classifiers as well as forward and inverse models to data analysis.

In model-based approaches measurements are analyzed to quantify tissue constituents and structure

based on the biophysical characteristics of tissue to create idealized representations called forward

models, which should be specific to the phenomena being measured. For optical properties of tissue

the model may include properties that affect absorption and scattering such as hemoglobin content,

melanin content, collagen and elastin cross-linking, nuclear content, and tissue scattering properties.

Imaging models may look at spatial (2D, 3D) distribution effects of these properties. Ideally these

models are independent of population. Once the forward model is scientifically validated, it can

then be applied analytically as an inverse model, by perturbing the values of the inputs to the



forward model until they match the measurement made on a tissue, the inverse model yields the

biophysical distribution of the tissue constituents.

Statistical models typically comprise a large data set that can include measured features, calculated

parameters, and biographic factors. A training set is used to develop algorithms to discriminate

tissue state (e.g. cancer vs. non-cancer), based on known results such as tissue pathology data or

mortality. Typically each data feature becomes another dimension in an n-dimensional data space

and the data is analyzed to generate a classification function that can effectively characterize the

tissue being measured. These classification functions use techniques such as Euclidean distance,

Mahalanobis distance, principal component analysis, support vector analysis, linear discriminate

analysis, etc., to develop hyper-planes within the data space to statistically divide it into meaningful

clusters; then each tissue measurement set can be compared against the criteria to classify the

measurement. This approach usually requires large population studies for adequate validation; there

is a risk of overtraining and often a reduction in specificity occurs in larger populations.

The OTCN project mentioned above benefitted from highly qualified statisticians, large numbers of

patients, repeat measurements and in-depth expert review to analyze data to explore how to get the

best out of these methods. The data was taken along with many different quality standards and

multiple systems were replicated in several centers. A great deal of analysis was required to

indentify small changes in instrument performance and calibration drift that affected the

performance of the classification algorithms. Appropriate data corrections gleaned from the

standards measurements had to be applied to bring data into conformance. Much data had to be

ultimately removed from consideration because of measurement artifacts that confused the

classifiers. What this project showed was that when developing and using these blind classifiers it is

important to have well-developed calibration and data correction algorithms as well as good quality

control algorithms to detect measurements of insufficient quality at the time data is taken, so

measurements can be redone. All of these generally require larger trials and very capable analysis

and algorithm development teams. Interestingly, part way through this OTCN project, the forward

and inverse models of tissue that were part of the project were well enough developed to start to be

applied to measurement data to extract biophysical features from both reflectance and fluorescent

spectra and these immediately provided better results than the statistical classifiers were generating.

This was likely due to the immediate noise reduction effect from applying spectral basis functions to

measurement data in the inverse models. While ultimately the statistical classifiers were able to

produce better sensitivity and specificity than the inverse models, once the data calibration

corrections were applied and the bad measurements weeded out, it points out the challenges

described above with implementing these types of black box methods in medical devices.

In other cases applying tissue models to data also produces problems. Models need to be matched

appropriately to the data and the desired information. Errors in quantization of tissue constituents

can come from having limited measurement data and applying it to a more complex inverse model,

or from having comprehensive and detailed data and an overly simple model (both cases will be

discussed below). One of the most commonly observed examples of this mismatch between data

and model in the case of human skin is the measurement of pigmented nevi, where one often sees

crosstalk between attempts to quantify melanin and hemoglobin in images created by several clinical

and pre-clinical devices with limited data such as RGB images or simple IR and fluorescence

images.[23] The biological implausibility of the correlation between melanin and hemoglobin and

melanin and collagen renders these image processing results questionable.



Biological plausibility is also a problem for the black box approach. The algorithm is only indirectly

linked to tissue anatomy, which given their limited performance makes it difficult for these systems

to inspire sufficient confidence for clinicians and dermatologists to embrace them, especially for an

improvement of specificity from 3-4% to the 10-13% range, when both the change of existing

procedure and the equipment expense are considered.

Our goal is to try to maintain critical high sensitivity while achieving a much higher specificity that

is independent of study size because it does not rely on population statistics. We use a multimode

imaging approach that integrates into a single instrument hyperspectral diffuse reflectance and

autofluorescence imaging, augmented by polarization control of both the illumination and imaging

paths to produce multimode scans. This is the multimode dermoscopy device we call SkinSpect.

Both conceptually and technologically it builds not only on our experience in melanoma imaging

[24][25][26][27][28], but also on multimode optical imaging that we introduced, advocated and

applied to microscopy [29], pathology [30] and in vivo cancer monitoring [31].

These comprehensive multimode measurements provide information that will allow depth-resolved

analysis of tissue structures in pigmented nevi in skin, in other skin conditions and in other epithelial

tissues. The combination of fluorescence, hyperspectral imaging in multiple polarization modes

yields data that can be combined to calculate and cross-validate three dimensional tissue structural

and metabolic maps.

In this paper, we describe the SkinSpect research prototype system hardware and generated

database. As a preliminary example of a spectral decomposition algorithm, a (slightly modified)

Kubelka-Munk model is applied to estimate the relative concentrations of melanin and hemoglobin

together with oxygenation saturation in various normal skin samples with nevi, and vein conditions.

The effect of applied spectral range (visible versus near infrared) on estimation of the concentrations

of melanin and hemoglobin together with oxygenation saturation in skin tissue are also discussed.

2. METHODS

2.1 SkinSpect system

We developed one SkinSpect research prototype unit with customized optics, hardware and software

that enables the multimode imaging-based measurement of skin lesions. As shown in Figure 1, the

system consisted of a console and a handpiece probe. The console module housed the tunable

spectrum illumination source, and a UNIX operating computer to control the specimen (via the

handpiece) illumination, data acquisition, image processing archiving and data transmission.

Additionally, a touch-screen Windows computer was employed to create a user-friendly interface

and manage patient record data and visual interfaces. The SkinSpect console employed a spectrally

programmable digital light source, the OneLight Spectra micro-mirror based system incorporating a

Xenon arc lamp, and two LED sources with drivers. The handpiece contained two cameras; a central

chassis; a beam splitter; and fiber guides that directed the light from the console illumination source

to a fixture that positions this assembly at the correct depth to illuminate the tissue surface. Linear

polarizers were placed in front of the fiber optics to allow only linearly polarized light to illuminate

the tissue surface. The two cameras each had a polarization filter installed and oriented orthogonally

to one another. This configuration captured images of the skin that maintained the linear polarization

present in reflectance from both surface and deeper layers of tissue), and cross polarization images

where approximately 95% of the light comes from the deeper layers of tissue. A synchronized image



(a) - (c)

CONSOLE

Display

Single boardcomputer

Dichroic mirror(425 nm)

LFII)(385 )1np

LED(455 nos)

Illumination spectralselection device Dichrole(OneLight®) beamspliite

(495 nm)

Fiber optics

Polarizers

Illumination beamDetection optics

EmissionLW? tiller Polarizer

405 nm

JIANDPIECE

Skinsample

(d)

RGB: P- polarized

RGB: X- polarized

Fluorescence:P and X- polarized

Reflectance datacube : P- polarized

Reflectance datacube : X- polarized

11 '444I011111Wavebands (- 30)

acquisition by the two cameras generated two images of an area of skin about 11 mm × 16 mm in

size in both parallel and cross polarizations.

The handpiece probe was light in weight, and could readily be positioned anywhere on the patient’s

body for a complete skin survey. The system operated in RGB, fluorescence, and hyperspectral

modes when activated by a handpiece trigger. Data was acquired by sequential imaging of the target

area at the following illumination wavelengths: blue 455 nm, green 527 nm, red 592 nm (these three

bands formed an RGB image), UVA 385 nm (fluorescence excitation), and sequential illumination

of 30-50 wavelength bands from visible to near infrared range. The system software provided

automatic data acquisition under control of an operator and automated calibration to verify

performance on each patient.

Figure 1 (a) SkinSpect research prototype and the (b) handpiece module; (c) the CAD design of handpiece module; (d) Block diagram of SkinSpect research prototype system.

Figure 2 SkinSpect data output includes RGB, fluorescence and hyperspectral reflection images in parallel and cross

polarization modes.

In the RGB mode, the illumination spectral selection device (OneLight Spectra) generated red and

green illumination light of 50 nm FWHM bandwidth centered at 592 nm and 527 nm. Blue

illumination was generated by an LED system at 455 nm. A dichroic beamsplitter (495 nm) ensured

all three colors of illumination could reach the sample. The RGB image was captured in both

parallel and cross polarization modes for immediate display of the scanned field of view. In the

fluorescence mode, a 385 nm LED light source was used to excite the skin autofluorescence. A

dichroic mirror (425 nm) merged the excitation illumination to the illumination path. The emission

long-pass filter in the detection path (cut-off wavelength = 405 nm) allowed only emitted

fluorescence photons to reach each individual camera. The illumination spectral selection device



generated a sequential spectral illumination of 31 wavelength bands with 22 nm FWHM bandwidth

and 15 nm wavelength intervals that spanned a wavelength region from 497 nm to 945 nm.

The system software consisted of two sub-modules: 1) The SkinSpect GUI programmed in a

Microsoft Windows environment, managed the user interface, data entry, and showed the processed

image, 2) The SkinSpect image acquisition software programmed under Linux, managed high speed

image acquisition and processing functions such as camera control, light source control , and digital

image processing.

Prior to tissue scanning, the system was calibrated by scanning a white calibration target

(Spectralon) to 1) correct illumination inhomogeneities 2) adjust exposure time for each spectral

band to ensure an acceptable signal to noise ratio, 3) subtract dark the current image, and 4) remove

hot/bad pixel defects from the camera.

Figure 2 shows a sample of the SkinSpect output after sequential image capture. The output

datacube contains RGB, fluorescence, and reflected light images of the skin. The total field of view

covered an 11 mm × 16 mm area. The minimum spatially resolvable line width detected by P and X

cameras was 39.4 µm and 111 µm respectively, which were derived by imaging a USAF 1951

resolution test target.

2.2 Spectral decomposition algorithm

As a proof of concept, we simplified the skin tissue model to a homogenous structure, assuming an

isotropically absorbing and scattering layer of infinite thickness, and employed the Kubelka-Munk

model to extract the relative skin tissue chromophore concentrations [32].

( ( ))

( )

where ( ) was the normalized reflectance spectra, calculated from the spectral intensity of skin-

reflected light divided by the spectral intensity of light reflected from the white Spectralon target at

the corresponding spatial coordinate. and are the total absorption and scattering coefficients of

skin tissue. By assuming that additivity of the skin chromophore absorbers is still valid, then:

+ +…

where and are the relative concentration and extinction coefficient of each skin chromophore.

We considered eumelanin, pheomelanin, oxy- and deoxy- hemoglobin as providing the major

chromophore contributions in the skin. The skin absorption spectra basis functions were taken from

published data [33][34]. The skin scattering coefficient was modeled as an expanded power

function as described in other publications [35][36]. The measured spectral datacube was

fitted by the predicted spectral datacube using a MATLAB standard non-negative least square

(NNLS) function to extract the functional parameters of targeted skin for each pixel (x,y). We

employed two approaches to extract the skin chromophore concentrations:

Single step least square fitting: In this approach, we have used a one-step NNLS fitting

procedure where i) the datacube from normalized reflection spectra was formed, ii) the

proper wavelength range (e.g. 605 -783 nm, total 13 wavebands with 15 nm intervals) was

chosen, iii) a four chromophore absorption model (eumelanin, pheomelanin, oxy and deoxy Hb)



was used, iv) the predicted spectrum were fitted to the measured spectrum using NNLS

fitting, v)total melanin, total hemoglobin, and oxygenation saturation percentage were derived.

The contribution of fat and water absorbance was considered to be negligible.

Two-step least square fitting: In this second approach, we used a two-step NNLS fitting

procedure where i) the datacube from the normalized reflection spectra was formed, ii) the

proper wavelength range (e.g. 660 -880 nm, total 18 wavebands with 15 nm intervals) was

chosen, iii) a four chromophore absorption model (eumelanin, pheomelanin, oxy and deoxy Hb)

was used, and iv) the predicted spectrum was fitted to the measured spectrum using

NNLS fitting, v) only the total melanin (eumelanin and pheomelanin) was derived, vi) we

created a new ( ⁄ ) function in which the eumelanin and pheomelanin contributions are

subtracted , vii) the new wavelength range was selected in NIR (which had the minimum

contribution of melanin and spanned either side of isosbestic point for oxy and deoxy Hb spectra

(e.g. 750 -850 nm, total 7 wavebands)), viii) a two chromophore absorption model (oxy and

deoxy Hb) was considered, ix) predicted ( ⁄ ) spectrum were fitted to the measured ( ⁄ )

spectrum using NNLS fitting, and x) total hemoglobin and oxygen saturation percentage were

derived. Again, water and fat absorbance were not taken into account.

3. RESULTS AND DISCUSSION

3.1 Hyperspectral and polarization data from SkinSpect

A key feature of the SkinSpect optical hardware design is the ability to collect RGB, fluorescence,

and hyperspectral data in two polarization modes simultaneously. Surface reflections appear in

parallel polarization. Diffused reflectance from deeper tissue is more evident in cross polarization

data. Comparison of parallel and cross-polarized scans permits the isolation of skin surface effects

from the data cube. Both surface effects and diffused reflectance may translate to higher specificity

in the diagnostic result [37][38][39][40][41].

Polarization effects seen in Figure 3, shows normalized reflectance spectra of skin with a pigmented

mole and skin with vitiligo (absence of melanocytes) conditions, as acquired by SkinSpect. Each

graph shows the mean and the variance of reflectance intensity from a 10×10 pixels area in both

parallel and in cross polarization modes. Two regions of interest (ROI) comparing the mole and

surrounding normal tissue were selected in Figure 3a and two ROI comparing vitiligo and the

surrounding normal tissue were selected in Figure 3b as examples for high melanin and low melanin

concentration. The detected spectral range spans 30 wavebands from 497 to 945 nm. The

normalized reflectance spectra selected in different areas of mole or vitiligo lesion are compared

with their surrounding normal skin. As seen in Figure 3c and Figure 3d, the normalized spectral

reflectance under parallel polarization mode is higher than under cross polarization along the whole

spectral range (blue and red curves in comparison with black and green curves, respectively). This

occurs because the parallel polarization mode comprises the reflected intensity of both diffusely

backscattered photons and specular reflected photons from tissue surface, whereas the cross

polarization datacube contains mostly diffusely backscattered photons. With access to both

polarization images, extraction of the specular reflectance spatial map becomes easy. Secondarily,

an image providing characterization of skin surface topology and spectral signature is also obtained.



Skin with mole

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600 700 800 900'Wavelength Dun]

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500 600 700 800 900(d) wydeoch fma

The reflectance intensity in parallel polarization mode has higher variance compared to cross

polarization mode. This higher variance results from the variability in specular intensity due to

surface topology of the primary creases of the epidermis and dermis. The secondary and tertiary

creases are below the optical resolution of our current device but may add some noise as well. In

skin with moles present, Figure 3c, the highest intensity difference between mole and normal skin

occurred in the 600 - 650 nm range for both parallel and cross polarization modes (comparing blue

and black curves with red and green curves, respectively) and the differences get smaller in the

longer wavelengths. This effect is mainly due to the difference in melanin content and corresponding

lower melanin absorption spectra. For skin with vitiligo (a benign depigmentation), the reflectance

intensity difference between vitiligo and normal skin is a maximum in 500 nm and 600 - 650 nm

ranges and again the difference get smaller in longer wavelengths from 750 to 900 nm. The

minimum spectral intensity differences were observed in both mole and vitiligo regions in the NIR.

These spectra show that in the skin with the mole, Figure 3c, the reflectance spectra slope increases

in 600nm range for normal skin. However in wavelengths above 620 nm the spectra slope for the

mole region becomes larger. This increase is one of the signatures of a high melanin concentration.

The opposite behavior is observed for vitiligo, where the spectral slope for the vitiligo region

becomes greater at around 600 nm compared to the surrounding normal skin and plateaus earlier

than with normal skin.

Figure 3 (a) picture of skin with mole (b) skin with vitiligo (c) normalized reflectance spectra corresponding to normal skin

and mole regions (d) normalized reflectance spectra corresponding to normal skin and mole regions (error bars are standard

variation of the reflectance intensity in 10× 10 pixels area).

3.2 Image analysis for skin compositional mapping

Figure 4 shows the RGB image and derived chromophore maps of the skin region with the mole.

The skin compositional maps were calculated from a Kubelka-Munk spectral data cube derived from

cross-polarized, normalized reflectance data. In this approach we used a four-chromophore model

including eumelanin, pheomelanin, oxy-hemoglobin and deoxy hemoglobin. The total melanin

content was calculated by the summation of eumelanin and pheomelanin concentrations. Total

hemoglobin was calculated by the summation of oxy-hemoglobin and deoxy-hemoglobin. The

oxygenation saturation parameter (OSP) was calculated as a ratio of oxy-hemoglobin by the total



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hemoglobin as a percentage. The melanin and deoxy-hemoglobin content is increased in the mole

while the oxy-hemoglobin decreases to almost zero value (Figure 4d). Uncorrected, this shows an

unusual value for the oxygenation saturation parameter (OSP) in Figure 4f.

Figure 4. Skin with mole chromophore maps using a single step KM model and least square fitting (a) RGB image, (b) total melanin,

(c) total hemoglobin, (d) oxy hemoglobin, (e) deoxy hemoglobin, and (f) hemoglobin oxygen saturation parameter (OSP) in %.

Although this same behavior is reported in melanoma compositional maps elsewhere [37][23], we

believe that this unusual behavior is due to a strong correlation between melanin and deoxy-

hemoglobin spectral signatures. If this correlation is not taken into consideration it leads to an over

estimation of deoxy-hemoglobin in the pigmented lesion. The overestimation of deoxy–hemoglobin

leads to underestimation of oxy-hemoglobin. This provides OSP values which are not biologically

plausible. To test this premise we applied the same algorithm to skin with vitiligo condition which

has less melanin. The same crosstalk is observed between total melanin and deoxy–hemoglobin in

vitiligo skin condition as shown in Figure 5. We hypothesized that a more realistic OSP reading

might be obtained by correcting the oxy and deoxy hemoglobin concentrations in the regions with

high and low melanin concentrations.

Figure 5. Calculated relative (a) total melanin map in comparison with (b) deoxy hemoglobin (the crosstalk limitation in vitiligo).

Figure 6 shows an RGB image and spatial maps of tissue total melanin, and hemoglobin distribution

in the skin with mole data using a two-step least square fitting algorithm as described in the methods

section. The Kubelka-Munk spectral data cubes were calculated from cross-polarized reflectance

spectral images. At the first non-negative least squares (NNLS) fitting, four-chromophore systems

were selected to evaluate total melanin concentration. The oxy-hemoglobin and deoxy-hemoglobin

estimation were then determined using a second stage of NNLS fitting using the new KM function in



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which the melanin absorptions were subtracted over this new spectral range of 750 – 850 nm. The

second NNLS fitting employs only a two-chromophore system (oxy-hemoglobin and deoxy-

hemoglobin). This second spectral range (750 – 850 nm) was chosen because it is less affected by

melanin absorption and spans the isosbestic spectral point in which the oxy-hemoglobin and deoxy-

hemoglobin absorption curves intersect each other. By improving the deoxy-hemoglobin

quantification, the oxy-hemoglobin artifact is corrected and the OSP quantity approximates the

accepted range of 50-70% in healthy skin.

Figure 6. Skin with mole chromophore maps using two-step KM model and least square fitting (a) RGB image, (b) total melanin, (c)

total hemoglobin, (d) oxy hemoglobin, (e) deoxy hemoglobin, and (f) hemoglobin oxygen saturation parameter (OSP) in %.SkinSpect.

Figure 7. SkinSpect images of skin with venous feature showing chromophore maps derived with a two-step KM model and NNLS

fitting (a) RGB image, (b) total melanin, (c) total hemoglobin, (d) oxy Hb, (e) deoxy Hb, and (f) Hb saturation in %

In order to obtain an independent validation of the two-stage NNLS fitting algorithm proposed

above, a skin region with obvious venous structures was scanned using SkinSpect and KM data

cubes obtained from cross polarized spectral reflectance images were used. The vein structure is

visible as bluish pixels in the RGB image in Figure 7a. In this result the melanin variation was very



small and more deoxy-hemoglobin and oxy-hemoglobin were detected in the vein area. Ultimately

an OSP quantity range of 60-70% was estimated which is a biologically plausible result that matches

closely results reported elsewhere [42].

4. CONCLUSIONS

A multimode SkinSpect research prototype system combining fluorescence, polarization and

hyperspectral imaging has been introduced as a tool for skin tissue analysis and mapping. Our main

target application for it is non-invasive early detection of melanoma. Reflectance image datacubes

were applied to a wavelength-dependent linear model to extract the relative contribution of skin

chromophores at each pixel. Oxy-hemoglobin, deoxy-hemoglobin, melanin concentrations, and

oxygen saturation by the single step linear least square fitting and Kubelka-Munk tissue model using

cross polarization data cubes were presented. These maps exhibited biologically implausible cross-

talk between hemoglobin and melanin, also shown in work by other researchers. The comprehensive

data obtained by SkinSpect was then utilized to improve the accuracy of the skin chromophore

decomposition algorithm using a two-step linear least square fitting procedure at different

wavelength ranges. The deoxy hemoglobin over-estimation error due to melanin and deoxy

hemoglobin spectral cross talk were analyzed and corrected. This method was also tested on skin

with an underlying vein to validate the proof of concept. In both cases values were derived that were

consistent with the underlying biology.

5. ACKNOWLEDGEMENTS

This work was supported (D1969B1) by the Qualifying Therapeutic Discovery Program under the

US Department of Health and Human Services Patent Protection and Affordable Care Act of 2010,

and SMI corporate funds. We wish to also thank the NIH, NSF and DoD for past funding which

made this work possible, and Drs. E. Lindsley, J.M. Kirkwood, A.C. Halpern, A. Marghoob, M.

Rajadhyaksha, E.N. Atkinson, A. Joseph, C. Pentico, L. Pilon, R. Saager, A.J. Durkin and B.

Tromberg for useful discussions and advice.

REFERENCES

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