A study of in-vivo classification of pigmented skin lesions using oblique-incidence diffuse reflectance spectroscopy is presented. Spatio-spectral data in the wavelength range from 455 to 765 nm are collected from 111 pigmented lesions including 10 histopathologically diagnosed as melanoma. The first 67 lesions are used for training the classifiers, and 44 lesions are used for testing. The first classifier separates (1) malignant melanoma and severe dysplastic nevi from (2) moderate and mild dysplastic nevi, common nevi, actinic and seborrheic keratoses. The second classifier next distinguishes between (a) moderate and mild dysplastic nevi, common nevi from (b) actinic and seborrheic keratoses. The third classifier further separates (I) moderate and mild dysplastic nevi from (II) common nevi. The first classifier performs with 100% sensitivity and 91% specificity with overall classification rates of 93% and 95 % for the training and testing sets, respectively. The second classifier has classification rates of 95% and 97 % for the training and testing sets, respectively, whereas the third classifier has classification rates of 98% and 94 % for the training and testing sets, respectively

Duvic, Madeleine

Garcia-Uribe, Alejandro

Smith, Elizabeth B.

Wang, Lihong V.

Caltech Authors - Main

PROCEEDINGS OF SPIE
SPIEDigitalLibrary.org/conference-proceedings-of-spie
White-light oblique-incidence diffuse
reflectance spectroscopy for
classification of in-vivo pigmented
skin lesions
Alejandro  Garcia-Uribe, Elizabeth B. Smith, Madeleine
Duvic, Lihong V. Wang
Alejandro  Garcia-Uribe, Elizabeth B. Smith, Madeleine  Duvic, Lihong V.
Wang, "White-light oblique-incidence diffuse reflectance spectroscopy for
classification of in-vivo pigmented skin lesions," Proc. SPIE 6435, Optical
Interactions with Tissue and Cells XVIII, 64350L (21 February 2007); doi:
10.1117/12.699633
Event: SPIE BiOS, 2007, San Jose, California, United States
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White Light Oblique-Incidence Diffuse Reflectance Spectroscopy for 
Classification of In Vivo Pigmented Skin Lesions 
 
Alejandro Garcia-Uribe1, Elizabeth B. Smith2, Madeleine Duvic3, Lihong V. Wang 4 
1
 Department of Electrical Engineering, Texas A&M University,  
3128 TAMU, College Station, Texas, USA 77843-3128 
2
 Department of Biomedical Engineering, Texas A&M University 
3120 TAMU, College Station, Texas, USA 77843-3120 
3
 Department of Dermatology, the University of Texas MD Anderson Cancer Center,  1515 Holcombe 
Blvd., Houston, Texas, USA 77030 
4
 Department of Biomedical Engineering, Washington University in St. Louis, 
St. Louis, Missouri, USA 63130-4899 
 
ABSTRACT 
 
A study of in-vivo classification of pigmented skin lesions using oblique-incidence diffuse reflectance spectroscopy 
is presented. Spatio-spectral data in the wavelength range from 455 to 765 nm are collected from 111 pigmented 
lesions including 10 histopathologically diagnosed as melanoma. The first 67 lesions are used for training the 
classifiers, and 44 lesions are used for testing. The first classifier separates (1) malignant melanoma and severe 
dysplastic nevi from (2) moderate and mild dysplastic nevi, common nevi, actinic and seborrheic keratoses. The 
second classifier next distinguishes between (a) moderate and mild dysplastic nevi, common nevi from (b) actinic 
and seborrheic keratoses. The third classifier further separates (I) moderate and mild dysplastic nevi from (II) 
common nevi. The first classifier performs with 100% sensitivity and 91% specificity with overall classification 
rates of 93% and 95 % for the training and testing sets, respectively.  The second classifier has classification rates of 
95% and 97 % for the training and testing sets, respectively, whereas the third classifier has classification rates of 
98% and 94 % for the training and testing sets, respectively. 
 
Keywords: Oblique incidence reflectometry, Spectroscopy, Skin cancer, Lesion classification,  
 
 
1. INTRODUCTION 
 
Melanoma is the most serious and fatal type of skin cancer. The American Cancer Society estimates that there will 
be 62,000 new cases of melanoma in the U.S. this year with 8,000 people dying of the disease1. Several researchers 
have studied the potential use of spectroscopy for early detection of cutaneous malignant tumor.  Wallace et al.2 
used multivariate discriminate analysis and neural networks to classify reflectance spectra of melanoma and 
common nevi with an accuracy rate of 86.7%.  McIntosh et al.3 utilized near-infrared reflectance spectroscopy with 
multivariate analysis to group spectra from non-melanoma skin cancers, pre-cancerous, and benign lesions, with 
classification accuracies of 72.4-97.7%.  Tomatis et al.4 used multispectral imaging to discriminate between 
melanoma and non-melanoma lesions with 85% accuracy.  Sigurdson et al.5 employed Raman spectroscopy to 
categorize skin lesions, including melanoma with a classification performance of 80.5%.  Garcia-Uribe et al.6 
reported a 95% classification rate distinguishing dysplastic nevi from common nevi and a 100% classification rate 
separating carcinomas from keratosis. 
 
2. EXPERIMENTAL SETUP 
 
The schematic of the system is illustrated in Figure 1. White light from a halogen lamp is coupled to an optical fiber 
(200 µm diameter). When the probe is placed perpendicular to the area of interest, the center source fiber is normal 
to the surface of incidence. The fiber optic probe consists of five source fibers and 24 collection fibers.  The outer 
source fibers are situated at 45 degrees and the remaining source fibers were placed at 25 degrees, Figure 2. An 
optical multiplexer allows delivering light through only one source fiber at a time to the area of interest. Once the 
light is delivered to the skin, it interacts with the medium and the diffuse reflectance is collected by another set of 
optical fibers (100 µm diameter). The collection fibers are coupled with an imaging spectrograph (Newport, Oriel 
Optical Interactions with Tissue and Cells XVIII, edited by Steven L. Jacques, William P. Roach,
Proc. of SPIE Vol. 6435, 64350L, (2007) · 1605-7422/07/$18 · doi: 10.1117/12.699633
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MS 257) that generates an optical spectrum for each fiber.  A CCD camera (Andor 412DV) collects the spectral-
images from the wavelength range 455 to 765 nm. This information is stored on a computer for future data analysis.   
 
 
 
Figure 1: System Setup. 
 
 
Figure 2: Probe description 
 
3. DATA DESCRIPTION 
 
Image acquisition was performed at The University of Texas M.D. Anderson Cancer Center (Melanoma and Skin 
Center). The oblique-incidence spectroscopic probe was used to collect spatio-spectral images from skin lesions and 
adjacent healthy skin. Data was collected with the probe placed perpendicular to the skin plane, ensuring contact but 
not compressing the skin. The spectral images were collected from five source fibers at four probe positions 
2 mm 
4.5 mm 
Collection fibers (100 µm) 
Oblique incidence  
source fiber  
(200 µm) 
1.8 mm 
1.4 mm 
2 mm 
5mm 
1 mm 
CCD camera 
Imaging 
spectrograph 
Halogen light 
source 
Optical 
multiplexer 
Probe 
Collection 
fibers 
Incidence 
fibers 
Computer 
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fiher niimher
0.8
0.6
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resulting in 20 images from each lesion and corresponding surrounding healthy tissue.  And example of a spectral 
image is shown in figure 3.  
 
 
Figure 3. Spectral Image 
 
The 111 pigmented lesions included in data analysis include benign common nevi (CN), mildly dysplastic nevi 
(DN1), moderately dysplastic nevi (DN2), severely dysplastic nevi (DN3), melanoma in situ (M1), invasive 
melanoma (M2), actinic keratosis (AK), seborrheic keratosis (SK).  The number of lesions is described in table 1. 
 
Table 1. Data description 
Melanoma 5 Mild Dysplastic Nevi 21 Actinic Keratosis 3 
Melanoma in situ 5 Moderate Dysplastic Nevi 41 
Seborrheic 
Keratosis 12 
Severe Dysplastic 
Nevi 5 Common Nevi 19   
 
4. DATA PROCESSING 
 
Each spectral image was normalized by the related healthy tissue image.  This is based on the assumption that the 
melanin content of healthy skin tissue will correlate with that of the imaged lesion.  The skin is not homogeneous 
and will vary within the collection site.  To reduce the error, the collection fiber intensity found at each probe 
position was averaged for each source fiber.  For simplification, groups were separated into two classes at a time.  
Features were extracted from the pigmented lesions to break them into two groups.  This was repeated for the 
subgroups until the desired categories were achieved. Figure 4 illustrates the classification order followed for 
pigmented lesions. 
 
 
 
Figure 4. Breakdown of pigmented lesions 
Common nevi  
Actinic keratosis  
Seborrheic keratosis  
Mild Dysplastic nevi  
Moderate Dysplastic nevi  
Severe Dysplastic nevi  
Melanoma 
Melanoma in situ 
Melanoma 
Melanoma in situ 
Severe Dysplastic nevi 
Actinic keratosis  
Seborrheic keratosis  
Common nevi  
Mild Dysplastic nevi  
Moderate Dysplastic 
nevi 
Actinic keratosis  
Seborrheic keratosis 
Common nevi  
Mild Dysplastic nevi  
Moderate Dysplastic 
nevi 
Mild Dysplastic nevi  
Moderate Dysplastic 
nevi 
Common nevi  
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The continuous wavelet transform (CWT) was employed to extract the best five features per fiber in the two classes 
under analysis.  The CWT of ( )f t  with respect to the wavelet function Ψ is defined as follows7:  
 
1( , ) ( ) t bWf b a f t dt
aa
∞
Ψ
−∞
−
⎛ ⎞
= Ψ
⎜ ⎟
⎝ ⎠
∫ , 0a <  (1) 
 
The parameter b is called the translation parameter. This parameter indicates the position or location of the wavelet 
function. The parameter a is called the dilatation parameter. The inverse of this parameter 1/ a can be regarded as a 
measure of frequency. The one dimensional data, intensity as a function of wavelength, is converted into two 
dimensional information, wavelet transformation as a function of b and a.  CWT features are extracted from the two 
dimensional data yielding mean, standard deviation, skewness, and kurtosis values for varying wavelength ranges.  
The five most effective features for each fiber were chosen using the Fisher distance (FD) as a measure of 
separability between classes. The Fisher distance is defined as  2
2
2
121 / σσµµ +−=FD , where 1µ  and 2µ  denote 
class means and 1σ  and 2σ  denote class standard deviations.  Redundant features were removed from the list and 
those remaining were narrowed down to the best twenty also using the Fisher distance. These twenty features form a 
feature vector that represents each lesion. Before feeding the feature vectors into a classifier the number of feature is 
optimized using a genetic algorithm as described in ref. 6. The final feature vectors were processed to remove their 
correlations and reduce their dimensionality to one dimension. This final value represents each lesion and is called 
combined image feature CIF. The CIF values are user to design and test three statistical Bayes classifiers.6,8 
 
The first classifier separates malignant melanoma, melanoma in situ and severe dysplastic nevi from actinic 
keratosis, seborrheic keratosis, common nevi, mild dysplastic nevi and moderate dysplastic nevi. This classifier 
performs with 100% sensitivity and 91% specificity. The classification rates are 93% and 95% for the training and 
testing sets, respectively.  
 
 
Tanning set 
 
Testing set 
 
 
Figure 5. Scatter plot first classifier  
 
The second classifier distinguishes actinic and seborrheic keratosis from common nevi, mild dysplastic nevi and 
moderate dysplastic nevi. This classifier uses fifty five lesions for training and forty one lesions for testing. The 
classification rate is 95% and 97% for the training and testing sets, respectively. 
 
 
 
 
 
 
 
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Testing set 
 
 
Figure 6. Scatter plot second classifier 
 
Finally a third classifier is used to separate common nevi from mild and moderate dysplastic nevi. This classifier 
uses forty seven lesions for training and thirty one lesions for testing. The classification rate for this classifier is 98% 
and 94 % for the training and testing sets, respectively. The Figure 5, 6 and 7 show the scatter plots for the training 
and testing sets for each classifier. 
 
 
Training set 
 
Testing set 
 
 
Figure 7.  Scatter plot third classifier 
 
The overall performance for the combination of the three classifiers is 91% and 87% for the tanning and testing sets 
respectively. The overall confusion matrices for the training and testing sets are presented in Tables 1 and 2. 
 
Table 2. Confusion matrix for the training set 
  DN3, M1, M2 AK, SK CN DN1, DN2 Hit rate 
DN3, M1, M2 12    100% 
AK, SK  8   100% 
CN 1  10  91% 
DN1, DN2 4  1 31 86% 
     91% 
 
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Table 3. Confusion matrix for the testing set 
  DN3, M1, M2 AK, SK CN DN1, DN2 Hit rate 
DN3, M1, M2 3    100% 
AK, SK  5  2 71% 
CN  1 7  88% 
DN1, DN2 1 1 1 23 88% 
     87% 
 
5. CONCLUSIONS 
 
This paper represents a study classifying pigmented skin lesions using oblique-incidence diffuse reflectance 
spectroscopy. The classifier that separates malignant from benign lesions achieves a sensitivity of 100% with a 
specificity of 91%.  The overall performance for the combination of the three classifiers is 91% and 87% for the 
tanning and testing sets respectively. The presented classification scheme does not require assumptions to produce a 
successful classification outcome. These results indicate that the approach presented can potentially be used by for 
computer-assisted classification technique of pigmented skin lesions. 
 
6.  ACKNOWLEDGMENT 
 
Thanks to Dr. Mays, Dr. Hymens and Dr. Mansfield for their help during the data collection. This project is 
sponsored by NIH grant R01 CA106728.  Wang’s email is lhwang@seas.wustl.edu. 
 
 
                                                 
1  American Cancer Society website. Melanoma and Non-melanoma Skin Cancer. http://www.cancer.org 
2. V. P. Wallace, J. C. Bamber, D. C. Crawford, R. J. Ott, and P. S. Mortimer, "Classification of reflectance 
spectra from pigmented skin lesions, a comparison of multivariate discriminant analysis and artificial neural 
networks," Physics in Medicine and Biology 45, 2859-71 (2000). 
3. L. M. McIntosh, R. Summers, M. Jackson, H. H. Mantsch, J. R. Mansfield, M. Howlett, A. N. Crowson, J. W. 
Toole, "Towards non-invasive screening of skin lesions by near-infrared spectroscopy", Journal of Investigative 
Dermatology 116 (1), 175-81 (2001). 
4. S. Tomatis, M. Carrra, A. Bono, C. Bartoli, M. Lualdi, G. Tragni, A. Colombo, R. Marchesini, “Automated 
melanoma detection with a novel multispectral imaging system: results of a prospective study”, Physics in 
Medicine and Biology 50, 1675-1687 (2005).  
5. S. Sigurdsson, P. A. Philipsen, L. K. Hansen, J. Larsen, M. Gniadecka, and H. C. Wulf, “Detection of Skin 
Cancer by Classification of Raman Spectra”, IEEE Transactions on Biomedical Engineering, no 10, vol. 51, 
Oct. (2004). 
6  A. Garcia-Uribe, N. Kehtarnavaz, G. Marquez, V. Prieto, M. Duvic, and L. V. Wang, "Skin Cancer Detection 
by Spectroscopic Oblique-Incidence Reflectometry: Classification and Physiological Origins," Appl. Opt. 43, 
2643-2650 (2004) 
7. J. Groswami and A. Chan, Fundamentals of Wavelets, Theory, Algorithms and Application, 1st ed. Wiley 
Interscience, New York 1999. 
8.  R. O. Duda, P .E. Hart, D. G. Stork, Pattern Classification, Wiley Interscience Publication, second edition, New 
York  2000. 
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White-light oblique-incidence diffuse reflectance spectroscopy for classification of in-vivo pigmented skin lesions

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White-light oblique-incidence diffuse reflectance spectroscopy for classification of in-vivo pigmented skin lesions

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