This paper presents a study on non-invasive detection of two common epithelial cancers (skin and esophagus) based on oblique incidence diffuse reflectance spectroscopy (OIDRS). An OIDRS measurement system, which combines fiber optics and MEMS technologies, was developed. In our pilot studies, a total number of 137 cases have been measured in-vivo for skin cancer detection and a total number of 20 biopsy samples have been measured ex-vivo for esophageal cancer detection. To automatically differentiate the cancerous cases from benign ones, a statistical software classification program was also developed. An overall classification accuracy of 90% and 100% has been achieved for skin and esophageal cancer classification, respectively

Balareddy, Karthik C.

Duvic, Madeleine

Garcia-Uribe, Alejandro

Wang, Kenneth K.

Wang, Lihong V.

Zou, Jun

Caltech Authors - Main

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Epithelial cancer detection by
oblique-incidence optical
spectroscopy
Alejandro  Garcia-Uribe, Karthik C. Balareddy, Jun  Zou,
Kenneth K. Wang, Madeleine  Duvic, et al.
Alejandro  Garcia-Uribe, Karthik C. Balareddy, Jun  Zou, Kenneth K. Wang,
Madeleine  Duvic, Lihong V. Wang, "Epithelial cancer detection by oblique-
incidence optical spectroscopy," Proc. SPIE 7169, Advanced Biomedical and
Clinical Diagnostic Systems VII, 71690A (23 February 2009); doi:
10.1117/12.808576
Event: SPIE BiOS, 2009, San Jose, California, United States
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Epithelial Cancer Detection by Oblique-Incidence  
Optical Spectroscopy 
 
Alejandro Garcia-Uribe1, Karthik C. Balareddy1, Jun Zou1, Kenneth K. Wang2,  
Madeleine Duvic3, Lihong V. Wang4 
 
1Department of Electrical Engineering, Texas A&M University,  
3128 TAMU, College Station, Texas, USA 
2Mayo Clinic, Division of Gastroenterology and Hepatology,  
Rochester, Minnesota, USA 
3Department of Dermatology, the University of Texas MD Anderson Cancer Center,   
1515 Holcombe Blvd., Houston, Texas, USA 
4Department of Biomedical Engineering, Washington University in St. Louis, 
One Brookings Drive, St. Louis, Missouri, USA 
 
ABSTRACT 
 
This paper presents a study on non-invasive detection of two common epithelial cancers (skin and esophagus) based on 
oblique incidence diffuse reflectance spectroscopy (OIDRS). An OIDRS measurement system, which combines fiber 
optics and MEMS technologies, was developed. In our pilot studies, a total number of 137 cases have been measured in-
vivo for skin cancer detection and a total number of 20 biopsy samples have been measured ex-vivo for esophageal 
cancer detection. To automatically differentiate the cancerous cases from benign ones, a statistical software 
classification program was also developed. An overall classification accuracy of 90% and 100% has been achieved for 
skin and esophageal cancer classification, respectively. 
 
Keywords: oblique incidence, spectroscopy, MEMS, cancer detection. 
 
1. INTRODUCTION 
 
Melanoma is the most serious and a 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 disease. And there were be about 
15,560 new cases of esophageal cancers with about 13,940 deaths in 20071. Esophageal cancer is 3 to 4 times more 
common among men than among women and 50% more common among African Americans than among whites.  
Several different optical methodologies have been proposed to determinate the malignancy of tumors2-4. Wallace et al.5 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.6 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.7 used multispectral imaging to discriminate between melanoma and non-
melanoma lesions with 85% accuracy.  Sigurdson et al.8 employed Raman spectroscopy to categorize skin lesions, 
including melanoma with a classification performance of 80.5%. 
 
2. EXPERIMENTAL SETUP 
 
The schematic of the system is illustrated in Figure 1. A halogen lamp is used as the light source. The optical 
multiplexer allows the light delivery through only one source fiber at a time to the area of interest.  Once delivered to 
the skin, the incident light interacts with the medium (skin lesion), whose diffuse reflectance is then collected by the 
collection fibers.  The collection fibers are coupled with the imaging spectrograph (Newport, Oriel MS 257) that 
generates an optical spectrum for each fiber. The CCD camera (Andor 412DV) collects the spectral-images from the 
wavelength range of 455 to 765 nm.  The spectral images represent the steady-state diffuse reflectance spectra from 
each collection fiber. 
 
Advanced Biomedical and Clinical Diagnostic Systems VII, edited by Anita Mahadevan-Jansen,
Tuan Vo-Dinh, Warren S. Grundfest, Proc. of SPIE Vol. 7169, 71690A
© 2009 SPIE · CCC code: 1605-7422/09/$18 · doi: 10.1117/12.808576
Proc. of SPIE Vol. 7169  71690A-1
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Figure 1: System Setup. 
 
3. PROBE DESIGN AND FABRICATION 
 
The probe for skin was designed with a front view configuration (Fig. 2a). A probe for esophageal cancer needs to be 
much smaller in size and more importantly side viewing (Fig. 2b). The fiber optic probe for skin lesion consists of five 
source fibers and 24 collection fibers.  The outer casing was made from aluminum as it is easy to machine.  The 200 μm 
plastic-clad silica source fibers are positioned linearly as shown in Fig. 3.  When the probe is placed perpendicular to 
the area of interest, the center source fiber is normal to the surface of incidence.  The outer source fibers are situated at 
45 degrees and the remaining source fibers were placed at 25 degrees. Two linear arrays, each consisting of twelve 
silica-silica 100 μm act as collection fibers. The Fig. 4 shows the schematic design of the probe for the ex-vivo 
esophageal lesion study.  An oblique incidence source fiber is used to deliver white light on the tissue surface of 
interest, which is precisely bent and positioned using a micromachined guide structure. To achieve the “side viewing” 
capability, a linear array of ten SU-8 waveguides (with a 90o turn) is microfabricated to collect the spatially resolved 
diffuse reflectance spectra.  To minimize light leakage and cross-talk between adjacent collection channels, the SU-8 
waveguides are coated with 3000Å of aluminum.  Ten interconnection optical fibers are used to interface the SU-8 
collection waveguides to the OIDRS measurement system with the assistance of another micromachined guide 
structure. 9 
 
 
CCD camera 
Imaging 
spectrograph 
Halogen 
light 
source 
Optical 
multiplexer 
OIDRS 
Probe Collection 
fibers 
Incidence 
fibers 
Computer 
Contact 
Surface 
Front view Side view 
(a) (b) 
Contact 
Surface 
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07
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Figure 2. Schematic of OIDRS probe configuration: (a) front viewing and (b) side viewing. 
 
Figure 3. Skin probe description 
 
 
 
 
Figure 4. Side view probe description. 
 
 
4. CLASSIFICATION OF SKIN LESION 
 
Skin data collection 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. An example of a spectral image is shown in Fig. 5. The spectral images were collected from five 
source fibers at four probe positions resulting in 20 spatio-spectral images from each lesion and corresponding 
surrounding healthy tissue. The 137 lesions included in data analysis include common nevi (CN), mildly dysplastic nevi 
(DN1), moderately dysplastic nevi (DN2), severely dysplastic nevi (DN3), melanoma (M), actinic keratosis (AK), 
seborrheic keratosis (SK). The data set for skin lesion is described in Table 1. 
2 mm 
Oblique incidence  
source fiber  
(200 m) 
1 mm 
SU-8 Waveguides 
Optical 
fibers 
SU-8 Waveguides 
Source fiber 
Portioning device 
Portioning device 
2 mm 
4.5 mm 
Oblique incidence  
source fiber  
(200 μm) 
2 mm 
5 mm 
1 mm 
Collection fibers (100 μm) 
Proc. of SPIE Vol. 7169  71690A-3
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Figure 5. A example of a spatio-spectral data collected from skin. 
 
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. Fig. 6 illustrates the classification order followed for pigmented lesions. As describe in 
ref. 10 the continuous wavelet transform (CWT) was employed to extract the best features per fiber in the two classes 
under analysis.  The most effective features for were chosen using the Fisher distance 2
2
2
121 / σσμμ +−=FD  as a 
measure of separability between classes. Where 1μ  and 2μ  denote class means and 1σ  and 2σ  denote class standard 
deviations.  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. The final feature vectors were processed 
to remove their correlations and reduce their dimensionality to one dimension. These values are used to design and test 
a statistical Bayes classifier11 to separates melanoma from actinic keratosis, seborrheic keratosis, common nevi, and 
dysplastic nevi. The classification rates for this classifier are 92% for the cancerous lesions and 90% for the benign and 
dysplastic ones (Table 2).  
 
 
Table 1. Skin data description. 
Common nevi 25 
Actinic keratosis 4 
Seborrheic keratosis 13 
Mild Dysplastic nevi 25 
Moderate Dysplastic nevi 51 
Severe Dysplastic nevi 6 
Melanoma 13 
                                                Total 137 
 
 
24681012
500
600
700
1000
2000
3000
4000
5000
6000
7000
Collection fiber (Side A)
Wavelength (nm)
R
e
la
tiv
e
 
v
a
lu
e
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Fig. 6. Breakdown of skin lesions. 
 
Table 2. Classification results for skin lesions. 
  
Melanoma DN1, DN2, DN3 CN, AK, SK Hit rate 
Melanoma 12 1 92 
DN1, DN2, DN3 
CN, AK, SK 13 111 90 
 
5. CLASSIFICATION OF ESOPHAGEAL LESION 
 
The OIDRS system was used to conduct ex-vivo measurement of twenty fresh esophageal samples obtained from 
patients undergoing endoscopic screening for esophageal diseases at the Barrett’s Esophagus Endoscopy Unit of Mayo 
Clinic (Rochester, MN).  The esophageal samples were extracted using endoscopic mucosal resection (EMR) of the 
esophageal epithelium. The tissue removed averages about 1 cm in diameter with a depth of 0.4 cm. The pathologist can 
provide information regarding the depth of penetration of the lesion, the size of the lesion, and the margins of resection. 
After the measurements, the samples were later histopathologically analyzed following the standard procedure at Mayo 
Clinic.  The 20 esophageal samples were found to consist of 8 benign, 6 low dysplastic, 4 highly dysplastic and 2 
cancerous lesions (Table 3).  
 
Table 3. Esophageal lesion description. 
Benign 6 
Low dysplastic 8 
High dysplastic 4 
Cancerous 2 
 
A bootstrap-based Bayes classifier was designed for esophageal lesion using previously develop algorithm for skin 
lesion. This classifier separates the benign and low dysplastic from the high dysplastic and cancerous lesion. The 
classifier generated a classification rate of 100% (100% sensitivity and specificity) (Table 4). 
 
Table 4. Classification results for esophageal lesion. 
  
Cancerous and 
high dysplastic 
Benign and low 
dysplastic Hit rate 
Cancerous and high 
dysplastic 6 0 100 
Benign and low 
dysplastic 0 14 100 
Common nevi  
Actinic keratosis  
Seborrheic keratosis  
Mild Dysplastic nevi  
Moderate Dysplastic nevi  
Severe Dysplastic nevi  
Melanoma 
Melanoma in situ 
 
Melanoma 
Actinic keratosis  
Seborrheic keratosis  
Common nevi  
Mild Dysplastic nevi  
Moderate Dysplastic nevi 
Severe Dysplastic nevi  
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6. CONCLUSIONS 
 
This paper presents a study for epithelial cancer detection using oblique-incidence diffuse reflectance spectroscopy. The 
classifier that distinguishes cutaneous melanoma from benign and dysplastic skin lesions achieves a sensitivity of 92% 
with a specificity of 90%.  The second classifier design to separate esophageal lesions presented a classification rate of 
100%. We should notice that the results from the study for classification of ex-vivo esophageal lesions was limited by 
its small sample size, but these results indicate that the proposed approach can potentially be used by for computer-
assisted classification technique of different types of epithelial lesions. 
 
7.  ACKNOWLEDGMENT 
 
We thank the staff of the Skin Cancer and Melanoma Center (UTMDACC) and the Division of Gastroenterology and 
Hepatology (Mayo Clinic) for their help during the data collection. This project is sponsored by NIH grant R01 
CA106728. 
 
8.  REFERENCES 
 
1. American Cancer Society, Cancer Facts & Figures 2007, Available at http://www.cancer.org. 
2. P. R. Bargo, S. A. Prahl, T. T. Goodell, R. A. Sleven, G. Koval,  G. Blair, S. L.  Jacques, “In vivo determination of 
optical properties of normal and tumor tissue with white light reflectance and an empirical light transport model 
during endoscopy,” Journal of Biomedical Optics 10, 034018 (2005). 
3. S. Brand, J. M. Poneros , B. E. Bouma , G. J. Tearney C. C. Compton , N. S. Nishioka, “Optical Coherence 
Tomography in the Gastrointestinal Tract,” Endoscopy 32, 796-803  (2000) 
4. Messmann H, Knüchel R, Bäumler W, Holstege A, Schölmerich J. “Endoscopic fluorescence detection of dysplasia 
in patients with Barrett's esophagus, ulcerative colitis, or adenomatous polyps after 5-aminolevulinic acid-induced 
protoporphyrin IX sensitization,” Gastrointestinal Endoscopy, 49(1),  97-101 (1999). 
5. 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, 2859-2871 (2000). 
6. 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), 175-181 (2001). 
7. 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). 
8. 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 51, 1784-1793 (2004). 
9. A. Garcia-Uribe, K. C. Balareddy, J. Zou, A. K. Wojcik, K. K. Wang and L. V. Wang, “Micromachined Side-
Viewing Optical Sensor Probe for Detection of Esophageal Cancers,” Journal of Sensors and Actuators A: 
Physical, doi:10.1016/j.sna.2008.11.008 (2008). 
10. 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,” Applied Optics 43, 2643-
2650 (2004). 
11.  R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd ed., New York: Wiley Interscience, (2000). 
Proc. of SPIE Vol. 7169  71690A-6
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Epithelial cancer detection by oblique-incidence optical spectroscopy

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Epithelial cancer detection by oblique-incidence optical spectroscopy

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