Abstract

In this study we use a multi-spectral digital microscope (MDM) to measure multi-spectral auto-fluorescence and reflectance images of the hamster cheek pouch model of DMBA (dimethylbenz[α]anthracene)-induced oral carcinogenesis. The multi-spectral images are analyzed both in the RGB (red, green, blue) color space as well as in the YCbCr (luminance, chromatic minus blue, chromatic minus red) color space. Mean image intensity, standard deviation, skewness, and kurtosis are selected as features to design a classification algorithm to discriminate normal mucosa from neoplastic tissue. The best diagnostic performance is achieved using features extracted from the YCbCr space, indicating the importance of chromatic information for classification. A sensitivity of 96% and a specificity of 84% were achieved in separating normal from abnormal cheek pouch lesions. The results of this study suggest that a simple and inexpensive MDM has the potential to provide a cost-effective and accurate alternative to standard white light endoscopy.

© 2005 Optical Society of America

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References

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    [CrossRef]
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    [CrossRef]
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    [CrossRef] [PubMed]

Ann Rev Phys. Chem.

E. Sevick-Muraca and R. Richards-Kortum, "Quantitative optical spectroscopy for tissue diagnosis, "Ann Rev Phys. Chem. 47, 555-606 (1996).
[CrossRef]

Arch. Otolaryngol.

A.R.Gillenwater, R. Jacob, R. Ganeshappa, B. Kemp, A.K. El-Naggar, J.L. Palmer, G. Clayman, M.Follen-Mitchell, and R. Richards-Kortum, "Noninvasive diagnosis of oral neoplasia based on fluorescence spectroscopy and native tissue autofluorescence," Arch. Otolaryngol., 1251-1258 (1998).

A. Gillenwater, R. Jacob, R. Ganeshappa, B. Kemp, A.K. El-Naggar, J.L. Palmer, G. Clayman, M.Follen-Mitchell, and R. Richards-Kortum, "Noninvasive diagnosis of oral neoplasia based on fluorescence spectroscopy and native tissue autoflurescence,�?? Arch. Otolaryngol. 124, 1251-1258 (1998).

Br. J. Cancer

N. Vengadesan, P. Aruna, and S. Ganesan, "Characterization of native fluorescence from DMBA-treated hamster cheek pouch buccal mucosa for measuring tissue transformation," Br. J. Cancer 77, 391-5 (1998).
[CrossRef] [PubMed]

Cancer

M.G. Muller, T.A. Valdez, I. Georgakoudi, V. Backman, C. Fuentes, S. Kabani, N. Laver, Z. Wang, C.W. Boone, R.R. Dasari, S.M. Shapshay, and M.S. Feld, "Spectroscopic detection and evaluation of morphologic and biochemical changes in early human oral carcinoma," Cancer 97, 1681-1692 (2003).
[CrossRef] [PubMed]

M. Inaguma, K. Hashimoto, "Porphyrin-like fluorescence in oral cancer," Cancer 86, 2201-2211 (1999).
[CrossRef] [PubMed]

Cancer Res.

I.B. Gimenez-Conti, D.M. Shin , A.B. Bianchi, D.R. Roop, W.K. Hong, C.J. Conti, T.J. Slaga, "Changes in keratin expression during 7,12-dimethylbenz[a]anthracene-induced hamster cheek pouch carcinogenesis, " Cancer Res. 50, 4441-4445 (1990).
[PubMed]

Econo. Letter

A. Bera, C. Jarque, �??Efficient tests for normality, heteroskedasticity and serial independence of regression residuals: Monte Carlo evidence,�?? Econo. Letter 7: 313-318 (1981).
[CrossRef]

Endoscopy

H. Stepp, R. Sroka, R. Baumgartner, "Fluorescence endoscopy of gastrointestinal diseases: basic principles, techniques, and clinical experience," Endoscopy 30, 379-86 (1998).
[CrossRef] [PubMed]

IEEE Trans. Biomed. Eng.

S.K. Chang, M. Follen, A. Malpica, U. Utzinger, S. Gtaerkel, D. Cox, N. Atkinson, C. MacAulay, R. Richards-Kortum, �??Optimal excitation wavelengths for discrimination of cervical neoplasia.," IEEE Trans. Biomed. Eng. 49, 1102-1111 (2002).
[CrossRef] [PubMed]

IEEE Trans. Med. Imaging

F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, P. Seutens, �??Multimodality image registration by maximization of mutual information,�?? IEEE Trans. Med. Imaging 16, 187-198 (1997).
[CrossRef] [PubMed]

IEEE Trans. Nucl, Science

B.W. Fei , Z.H. Lee, D.T. Boll, J.L. Duerk, D.B. Sodee, J.S. Lewin, D.L. Wilson, " Registration and fusion of SPECT, high-resolution MRI, and interventional MRI for thermal ablation of prostate cancer ," IEEE Trans. Nucl, Science 51: 177-183 Part 1(2004).
[CrossRef]

IEEE Trans. Patt. Anal. Mach. Intell.

R. Hsu, M. Abdel-Mottaleb, and A. K. Jain, �??Face detection in color images,�?? IEEE Trans. Patt. Anal. Mach. Intell. 24: 696-706 (2002).
[CrossRef]

J. Oral Pathol. Med.

C.T. Chen, H.K. Chiang, S.N. Chow, C.Y. Wang, Y.S. Lee, J.C. Tsai, C.P. Chiang, "Autofluorescence in normal and malignant human oral tissues and in DMBA-induced hamster buccal pouch carcinogenesis," J. Oral Pathol. Med. 27, 470-474 (1998).
[CrossRef] [PubMed]

C.Y. Wang, T. Tsai, H.C. Chen, S.C. Chang, C.T. Chen, and C.P. Chiang, "Autofluorescence spectroscopy for in vivo diagnosis of DMBA-induced hamster buccal pouch pre-cancers and cancers," J. Oral Pathol. Med. 32, 18�??24 (2003).
[CrossRef] [PubMed]

Laryngoscope

J.K. Dhingra, X. Zahng, K. McMillan, S. Kabani, R. Manoharan, I. Itzkan, M.S. Feld, S.M. Sharpshay,"Diagnosis of head and neck precancerous lesions in an animal model using fluorescence spectroscopy,"Laryngoscope 108, 471-475 (1998).
[CrossRef] [PubMed]

Lasers Surg. Med.

A. Agrawal, U. Utzinger, C. Brookner, C. Pitris, M. Follen-Mitchell, R. Richards-Kortum, "Fluorescence spectroscopy of the cervix: influence of acetic acid, cervical mucus, and vaginal medications," Lasers Surg. Med., 25, 237-49 (1999).
[CrossRef] [PubMed]

L. Coghlan, U. Utzinger, R. Richards-Kortum, C. Brookner, A. Zuluaga, I. Gimenez-Conti, M. Follen-Mitchell, "Fluorescence spectroscopy of epithelial tissue throughtout the dysplasia-carcinoma sequence in an animal model: spectroscopic changes precede morphologic changes, " Lasers Surg. Med. 29,1-10 (2001).
[CrossRef] [PubMed]

Mol. Carcinog.

J.C. Zenklusen , S.L. Stockman, S.M. Fischer SM, C.J. Conti, I.B. Gimenez-Conti, " Transforming growth factor-beta 1 expression in Syrian hamster cheek pouch carcinogenesis," Mol. Carcinog. 9, 10-16 (1994).
[CrossRef] [PubMed]

Obstet. Gynecol.

S.B. Cantor, M. Follen-Mitchell, G. Tortolero-Luna, C. Bratka, D. Bodurka, R. Richards-Kortum, �??Cost-effectiveness analysis of diagnosis and management of cervical squamous intraepithelial lesions,�?? Obstet. Gynecol. 91, 270-277 (1998).
[CrossRef] [PubMed]

Opt. Express

Photochem. Photobiol

N. Ramanujam, M. Follen-Mitchell, A. Mahadevan-Jansen, S. Thomsen, G. Staerkel, A. Malpica, T. Wright, N. Atkinson and R. Richards-Kortum, "Cervical precancer detection using multivariate statistical algorithm based on laser-induced fluorescence spectra at multiple excitation wavelengths," Photochem. Photobiol. 64, 720-735(1996).
[CrossRef] [PubMed]

Photochem. Photobiol.

G.A. Wagnieres, W.M. Star, and B.C. Wilson, "In vivo fluorescence spectroscopy and imaging for oncological applications," Photochem. Photobiol. 68, 603-32 (1998).
[PubMed]

D.L. Heintzelman, U. Utzinger, et al., "Optimal excitation wavelengths for in vivo detection of oral neoplasia using fluorescence spectroscopy," Photochem. Photobiol. 72, 103-113 (2000).
[CrossRef] [PubMed]

Other

V. Bhaskaran, K. Konstantinides, Image and video compression standards algorithms and architectures (Kluwer Academic Publishers, USA, 1999).

O.D. Richard, E.H. Peter, G.S. David, Pattern Classification (John Wiley & Sons, Inc., 2001).

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Figures (8)

Fig. 1.
Fig. 1.

(a) System diagram and (b) a photograph of the MDM system

Table 1.
Table 1.

Overview of the study design. DMBA weekly treatment frequency is indicated by the number in ach cell; cells without a number indicate no DMBA treatment. Cells are color coded to indicate image acquisition and tissue classification. Different colors represent different tissue classifications: cyan for normal, green for intermediate, and red for neoplastic.

Fig. 2.
Fig. 2.

Time course of RGB images for a representative DMBA treated hamster : 1st week, b) 5th week, c) 7th week d) 11th week, and e) 13th week (left: white light reflectance images, middle: 345 nm excited fluorescence images and right: 440 nm excited autofluorescence images).

Fig. 3.
Fig. 3.

Relative frequency histograms of the mean intensity of pixels randomly chosen from fluorescence images at (a) 345 nm and (b) 440 nm excitation in the normal and neoplastic groups. The left column shows data from the red channel, the middle column from the green channel and the right column from the blue channel.

Fig. 4.
Fig. 4.

Two dimensional scatter plots of the statistical parameters from pixels randomly chosen from fluorescence images at 345 nm excitation vs the same parameters at 440 nm excitation, including (a) mean intensity, (b) standard deviation, (c) skewness and (d) kurtosis. The left column shows data from the red channel, the middle column from the green channel and the right column from the blue channel. A data point is shown for each hamster at each time point in the normal, intermediate and neoplastic groups.

Fig. 5.
Fig. 5.

Relative frequency histograms of the mean intensity of pixels randomly chosen from fluorescence images at (a) 345 nm and (b) 440 nm excitation in the normal and neoplastic groups. The left column shows data from the Y channel, the middle column from the Cb channel and the right column from the Cr channel.

Fig. 6.
Fig. 6.

Two dimensional scatter plots of the statistical parameters from pixels randomly chosen from fluorescence images at 345 nm excitation vs the same parameters at 440 nm excitation, including (a) mean intensity, (b) standard deviation, (c) skewness and (d) kurtosis. The left column shows data from the Y channel, the middle column from the Cb channel and the right column from the Cr channel. A data point is shown for each hamster at each time point in the normal, intermediate and neoplastic groups.

Table 2.
Table 2.

Classification results using the input data from images in the YCbCr color space for each hamster at each time point. Colored cells indicate time points at which images were acquired. The cells are color-coded to indicate which group the images were assigned to as in Table 1. The results of the classification algorithm are represented by the letter in the cell, where N indicates the measurement was classified as normal and A indicates the measurement was classified as neoplastic. Circled letters indicate misclassifications for hamsters in the normal and neoplastic groups.

Equations (3)

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Y = c 1 R + c 2 G + c 3 B ,
C b = B Y 2 2 c 3 ,
C r = R Y 2 2 c 1 ,

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