OSA's Digital Library

Journal of the Optical Society of America A

Journal of the Optical Society of America A


  • Vol. 19, Iss. 9 — Sep. 1, 2002
  • pp: 1823–1831

Fluorescence spectral imaging for characterization of tissue based on multivariate statistical analysis

Jianan Y. Qu, Hanpeng Chang, and Shengming Xiong  »View Author Affiliations

JOSA A, Vol. 19, Issue 9, pp. 1823-1831 (2002)

View Full Text Article

Enhanced HTML    Acrobat PDF (321 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



A novel spectral imaging method for the classification of light-induced autofluorescence spectra based on principal component analysis (PCA), a multivariate statistical analysis technique commonly used for studying the statistical characteristics of spectral data, is proposed and investigated. A set of optical spectral filters related to the diagnostically relevant principal components is proposed to process autofluorescence signals optically and generate principal component score images of the examined tissue simultaneously. A diagnostic image is then formed on the basis of an algorithm that relates the principal component scores to tissue pathology. With autofluorescence spectral data collected from nasopharyngeal tissue in vivo, a set of principal component filters was designed to process the autofluorescence signal, and the PCA-based diagnostic algorithms were developed to classify the spectral signal. Simulation results demonstrate that the proposed spectral imaging system can differentiate carcinoma lesions from normal tissue with a sensitivity of 95% and specificity of 93%. The optimal design of principal filters and the optimal selection of PCA-based algorithms were investigated to improve the diagnostic accuracy. The robustness of the spectral imaging method against noise in the autofluorescence signal was studied as well.

© 2002 Optical Society of America

OCIS Codes
(110.7050) Imaging systems : Turbid media
(170.0170) Medical optics and biotechnology : Medical optics and biotechnology
(170.3880) Medical optics and biotechnology : Medical and biological imaging
(170.4580) Medical optics and biotechnology : Optical diagnostics for medicine
(170.6510) Medical optics and biotechnology : Spectroscopy, tissue diagnostics

Original Manuscript: February 1, 2002
Revised Manuscript: March 29, 2002
Manuscript Accepted: April 9, 2002
Published: September 1, 2002

Jianan Y. Qu, Hanpeng Chang, and Shengming Xiong, "Fluorescence spectral imaging for characterization of tissue based on multivariate statistical analysis," J. Opt. Soc. Am. A 19, 1823-1831 (2002)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. R. Richards-Kortum, E. Sevick-Muraca, “Quantitative optical spectroscopy for tissue diagnosis,” Annu. Rev. Phys. Chem. 47, 555–606 (1996). [CrossRef] [PubMed]
  2. S. Andersson-Engels, C. Klinteberg, K. Svanberg, S. Svanberg, “In vivo fluorescence imaging for tissue diagnostics,” Phys. Med. Biol. 42, 815–824 (1997). [CrossRef] [PubMed]
  3. G. A. Wagnieres, W. M. Star, B. C. Wilson, “In vivo fluorescence spectroscopy and imaging for oncological applications,” Photochem. Photobiol. 68, 603–632 (1998). [CrossRef]
  4. K. M. O’Brien, A. F. Gmitro, G. R. Gindi, M. L. Stetz, F. W. Cutruzzola, L. I. Laifer, L. L. Deckelbarm, “Development and evaluation of classification algorithms for fluorescence guided laser angioplasty,” IEEE Trans. Biomed. Eng. 36, 424–431 (1989). [CrossRef]
  5. C. Eker, R. Rydell, K. Svanberg, S. Andersson-Engels, “Multivariate analysis of laryngeal fluorescence spectra recorded in vivo,” Lasers Surg. Med. 28, 259–266 (2001). [CrossRef] [PubMed]
  6. N. Ramanujam, M. F. Mitchell, A. Mahadevan, S. Thomsen, A. Malpica, T. Wright, N. Atkinson, R. Richards-Kortum, “Development of a multivariate statistical algorithm to analyze human cervical tissue,” Lasers Surg. Med. 19, 46–62 (1996). [CrossRef]
  7. C. Y. Wang, C. T. Chen, C. P. Chiang, S. T. Young, S. N. Chow, H. K. Chiang, “A probability-based multivariate statistical algorithm for autofluorescence spectroscopic identification of oral carcinogenesis,” Photochem. Photobiol. 69, 471–477 (1999). [CrossRef] [PubMed]
  8. E. B. Hanlon, I. Itzkan, R. R. Dasari, M. S. Feld, R. J. Ferrante, A. C. McKee, D. Lathi, N. W. Kowall, “Near-infrared fluorescence spectroscopy detects Alzheimer’s disease in vitro,” Photochem. Photobiol. 70, 236–242 (1999). [PubMed]
  9. M. P. Nelson, J. F. Aust, J. A. Dobrowolski, P. G. Verly, M. L. Myrick, “Multivariate optical computation for predictive spectroscopy,” Anal. Chem. 70, 73–78 (1998). [CrossRef] [PubMed]
  10. O. Soyemi, D. Eastwood, L. Zhang, H. Li, J. Karunamuni, P. Gemperline, R. A. Synowicki, M. L. Myrick, “Design and testing of a multivariate optical element: the first demonstration of multivariate optical computing for predictive spectroscopy,” Anal. Chem. 73, 1069–1079 (2001). [CrossRef]
  11. J. Y. Qu, H. Chang, S. Xiong, “Optical processing of light induced autofluorescence for characterization of tissue pathology,” Opt. Lett. 26, 1268–1270 (2001). [CrossRef]
  12. J. E. Jackson, A User’s Guide to Principal Components (Wiley, New York, 1991).
  13. J. Y. Qu, P. W. Yuen, Z. J. Huang, D. Kwong, J. Shan, S. J. Lee, W. K. Ho, W. I. Wei, “Preliminary study of in vivo autofluorescence of nasopharyngeal carcinoma and normal tissue,” Lasers Surg. Med. 26, 432–440 (2000). [CrossRef] [PubMed]
  14. B. W. Silverman, Density Estimation for Statistics and Data Analysis (Chapman & Hall, New York, 1986).
  15. A. J. Richard, W. W. Dean, Applied Multivariate Statistical Analysis (Prentice-Hall, Englewood Cliffs, N.J., 1998).

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited