Abstract

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

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  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).

2001 (3)

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]

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]

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]

2000 (1)

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]

1999 (2)

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]

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]

1998 (2)

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]

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]

1997 (1)

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]

1996 (2)

R. Richards-Kortum, E. Sevick-Muraca, “Quantitative optical spectroscopy for tissue diagnosis,” Annu. Rev. Phys. Chem. 47, 555–606 (1996).
[CrossRef] [PubMed]

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]

1989 (1)

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]

Andersson-Engels, S.

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]

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]

Atkinson, N.

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]

Aust, J. F.

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]

Chang, H.

Chen, C. T.

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]

Chiang, C. P.

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]

Chiang, H. K.

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]

Chow, S. N.

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]

Cutruzzola, F. W.

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]

Dasari, R. R.

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]

Dean, W. W.

A. J. Richard, W. W. Dean, Applied Multivariate Statistical Analysis (Prentice-Hall, Englewood Cliffs, N.J., 1998).

Deckelbarm, L. L.

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]

Dobrowolski, J. A.

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]

Eastwood, D.

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]

Eker, C.

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]

Feld, M. S.

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]

Ferrante, R. J.

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]

Gemperline, P.

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]

Gindi, G. R.

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]

Gmitro, A. F.

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]

Hanlon, E. B.

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]

Ho, W. K.

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]

Huang, Z. J.

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]

Itzkan, I.

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]

Jackson, J. E.

J. E. Jackson, A User’s Guide to Principal Components (Wiley, New York, 1991).

Karunamuni, J.

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]

Klinteberg, C.

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]

Kowall, N. W.

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]

Kwong, D.

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]

Laifer, L. I.

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]

Lathi, D.

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]

Lee, S. J.

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]

Li, H.

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]

Mahadevan, A.

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]

Malpica, A.

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]

McKee, A. C.

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]

Mitchell, M. F.

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]

Myrick, M. L.

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]

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]

Nelson, M. P.

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]

O’Brien, K. M.

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]

Qu, J. Y.

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]

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]

Ramanujam, N.

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]

Richard, A. J.

A. J. Richard, W. W. Dean, Applied Multivariate Statistical Analysis (Prentice-Hall, Englewood Cliffs, N.J., 1998).

Richards-Kortum, R.

R. Richards-Kortum, E. Sevick-Muraca, “Quantitative optical spectroscopy for tissue diagnosis,” Annu. Rev. Phys. Chem. 47, 555–606 (1996).
[CrossRef] [PubMed]

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]

Rydell, R.

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]

Sevick-Muraca, E.

R. Richards-Kortum, E. Sevick-Muraca, “Quantitative optical spectroscopy for tissue diagnosis,” Annu. Rev. Phys. Chem. 47, 555–606 (1996).
[CrossRef] [PubMed]

Shan, J.

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]

Silverman, B. W.

B. W. Silverman, Density Estimation for Statistics and Data Analysis (Chapman & Hall, New York, 1986).

Soyemi, O.

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]

Star, W. M.

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]

Stetz, M. L.

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]

Svanberg, K.

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]

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]

Svanberg, S.

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]

Synowicki, R. A.

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]

Thomsen, S.

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]

Verly, P. G.

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]

Wagnieres, G. A.

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]

Wang, C. Y.

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]

Wei, W. I.

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]

Wilson, B. C.

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]

Wright, T.

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]

Xiong, S.

Young, S. T.

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]

Yuen, P. W.

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]

Zhang, L.

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]

Anal. Chem. (2)

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]

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Figures (7)

Fig. 1
Fig. 1

Typical raw and preprocessed LIF spectra. Before preprocessing: (A) normal tissue and (B) carcinoma. After preprocessing: (C) normal tissue and (D) carcinoma.

Fig. 2
Fig. 2

Spectra of the first four principal component loadings. Solid curve, PC1; dotted curve, PC2; dashed curve, PC3; dashed–dotted curve, PC4.

Fig. 3
Fig. 3

Transmission curves of designed and calculated PC filters. Solid curves, designed filters; dotted curves, calculated filters; dashed curves, differential transmissions between designed filters and calculated filters.

Fig. 4
Fig. 4

Posterior probability of a given tissue belonging to the carcinoma group on the basis of PC1 and PC2 scores.

Fig. 5
Fig. 5

Scatter plot of the PC1 score versus the PC2 score.

Fig. 6
Fig. 6

Transmissions of the designed PC filters at different incident angles. Solid curves, 0 deg; dotted curves, 3 deg; dashed curves, 6 deg; dashed–dotted curves, 10 deg.

Fig. 7
Fig. 7

Typical spectra with added Gausian random noise.

Tables (3)

Tables Icon

Table 1 Sensitivity/Specificity (%) of the Probability-Based Algorithm and the Threshold-Based Algorithm for Self-Calibration in Training Sets and the Prediction of Kept-Out Samples

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Table 2 Sensitivity/Specificity (%) of the Spectral Classification by Use of PC Filters with Different Transmission Shifts at Different Angles

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Table 3 Accuracy of the Spectral Classification at Different Noise Levels

Equations (6)

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Fi(λ)=[αiPCi(λ)+βi]S-1(λ),subjectto0Fi(λ)1,
Qi=PCi(λ)I(λ)dλ=1αi I(λ)Fi(λ)S(λ)dλ-βiαik I(λ)Fs(λ)S(λ)dλ.
P(C|xi)=P(xi|C)×P(C)×mP(xi|C)×P(C)×m+P(xi|N)×P(N)×m¯,
f(x)=I[(2π)p|Σ|]1/2 exp-12(x-u)Σ-1(x-u),
Qi=1αi kI(λ)Fi(λ)S(λ)dλI(λ)Fs(λ)S(λ)dλ-βi.
Qi=1αi Fi(λ)I(λ)dλI(λ)dλ-βi.

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