Abstract

Multispectral fluorescence lifetime imaging (m-FLIM) can potentially allow identifying the endogenous fluorophores present in biological tissue. Quantitative description of such data requires estimating the number of components in the sample, their characteristic fluorescent decays, and their relative contributions or abundances. Unfortunately, this inverse problem usually requires prior knowledge about the data, which is seldom available in biomedical applications. This work presents a new methodology to estimate the number of potential endogenous fluorophores present in biological tissue samples from time-domain m-FLIM data. Furthermore, a completely blind linear unmixing algorithm is proposed. The method was validated using both synthetic and experimental m-FLIM data. The experimental m-FLIM data include in-vivo measurements from healthy and cancerous hamster cheek-pouch epithelial tissue, and ex-vivo measurements from human coronary atherosclerotic plaques. The analysis of m-FLIM data from in-vivo hamster oral mucosa identified healthy from precancerous lesions, based on the relative concentration of their characteristic fluorophores. The algorithm also provided a better description of atherosclerotic plaques in term of their endogenous fluorophores. These results demonstrate the potential of this methodology to provide quantitative description of tissue biochemical composition.

© 2014 Optical Society of America

Full Article  |  PDF Article

References

  • View by:
  • |
  • |
  • |

  1. N. Galletly, J. McGinty, C. Dunsby, F. Teixeira, J. Requejo-Isidro, I. Munro, D. Elson, M. Neil, A. Chu, P. French, G. Stamp, “Fluorescence lifetime imaging distinguishes basal cell carcinoma from surrounding uninvolved skin,” Br. J. Dermatol. 159, 152–161 (2008).
    [CrossRef] [PubMed]
  2. K. S. M. Mycek, N. Nishioka, “Colonic polyp differentiation using time-resolved autofluorescence spectroscopy,” Gastrointest. endosc. 48, 390–394 (1998).
    [CrossRef] [PubMed]
  3. L. Marcu, “Fluorescence lifetime in cardiovascular diagnostics,” J. Biomed. Opt. 15, 011106 (2010).
    [CrossRef] [PubMed]
  4. J. A. Jo, B. Applegate, J. Park, S. Shrestha, P. Pande, I. Gimenez-Conti, J. Brandon, “In vivo simultaneous morphological and biochemical optical imaging of oral epithelial cancer,” IEEE Trans. Biomed. Eng. 57, 2596–2599 (2010).
    [CrossRef] [PubMed]
  5. J. Bioucas-Dias, A. Plaza, N. Dobigeon, M. Parente, Q. Du, P. Gader, J. Chanussot, “Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches,” IEEE J. Sel. Topics Appl. Earth Observ. 5, 354–379 (2012).
    [CrossRef]
  6. H. Xu, B. W. Rice, “In-vivo fluorescence imaging with a multivariate curve resolution spectral unmixing technique,” J. Biomed. Opt. 14, 064011 (2009).
    [CrossRef]
  7. S. S. Vogel, P. S. Blank, S. V. Koushik, C. Thaler, “Spectral imaging and its use in the measurement of Förster resonance energy transfer in living cells,” in Fret and Flim Techniques. Laboratory Techniques in Biochemistry and Molecular Biology, T. Gadella, ed. 33, 351–394, (Elsevier, 2009).
    [CrossRef]
  8. J. G. Gert-JanKremers, Erik B. van Munster, J. Theodorus, W. J. Gadella, “Quantitative lifetime unmixing of multiexponentially decaying fluorophores using single-frequency fluorescence lifetime imaging microscopy,” Biophys. J. 95, 378–389 (2008).
    [CrossRef]
  9. O. Gutierrez-Navarro, D. Campos-Delgado, E. Arce-Santana, M. Mendez, J. Jo, “A fully constrained optimization method for time-resolved multi-spectral fluorescence lifetime imaging microscopy data unmixing,” IEEE Trans. Biomed. Eng. 60, 1711–1720 (2013).
    [CrossRef] [PubMed]
  10. O. Gutierrez-Navarro, D. Campos-Delgado, E. Arce-Santana, M. Mendez, J. Jo, “Blind end-member and abundance extraction for multi-spectral fluorescence lifetime imaging microscopy data,” IEEE J. Biomed. Health Inform. 18, 606–617 (2014).
    [CrossRef] [PubMed]
  11. J. Harsanyi, W. Farrand, C.-I. Chang, “Determining the number and identity of spectral endmembers: An integrated approach using Neyman-Pearson eigenthresholding and iterative constrained rms error minimization,” presented at the 9th Thematic Conference on Geological Remote Sensing, Pasadena, California, USA (1993).
  12. C.-I. Chang, Q. Du, “Estimation of number of spectrally distinct signal sources in hyperspectral imagery,” IEEE Trans. Geosci. Remote Sens. 42, 608–619 (2004).
    [CrossRef]
  13. J. Bioucas-Dias, J. Nascimento, “Hyperspectral subspace identification,” IEEE Trans. Geosci. Remote Sens. 46, 2435–2445 (2008).
    [CrossRef]
  14. H. Akaike, “A new look at the statistical model identification,” IEEE Trans. Automat. Contr. 19, 716–723 (1974).
    [CrossRef]
  15. J. Rissanen, “Modeling by shortest data description,” Automatica 14, 465–471 (1978).
    [CrossRef]
  16. R. Heylen, P. Scheunders, “Hyperspectral intrinsic dimensionality estimation with nearest-neighbor distance ratios,” IEEE J. Sel. Topics Appl. Earth Observ. 6, 570–579 (2013).
    [CrossRef]
  17. C. Andreou, V. Karathanassi, “Estimation of the number of endmembers using robust outlier detection method,” IEEE J. Sel. Topics Appl. Earth Observ. 7, 247–256 (2014).
    [CrossRef]
  18. A. Ambikapathi, T.-H. Chan, C.-Y. Chi, K. Keizer, “Hyperspectral data geometry-based estimation of number of endmembers using p -norm-based pure pixel identification algorithm,” IEEE Trans. Geosci. Remote Sens. 51, 2753–2769 (2013).
    [CrossRef]
  19. J. R. Lakowicz, Principles of Fluorescence Spectroscopy (Springer, 2006).
    [CrossRef]
  20. T. Zimmermann, “Spectral imaging and linear unmixing in light microscopy,” Molecular Biology 95, 245–265 (2005).
  21. P. Vallotton, A. Phatak, M. Berman, “Spectral Imaging and Unmixing,” in Fluorescence Applications in Biotechnology and Life Sciences, E. M. Goldys, ed. (Wiley-Blackwell, 2009).
  22. M. Craig, “Minimum-volume transforms for remotely sensed data,” IEEE Trans. Geosci. Remote Sens. 32, 542–552 (1994).
    [CrossRef]
  23. S. Boyd, L. Vandenberghe, Convex Optimization (Cambridge University Press, New York, 2004).
    [CrossRef]
  24. D. Manolakis, G. Shaw, “Detection algorithms for hyperspectral imaging applications,” IEEE Signal Process. Mag. 19, 29–43 (2002).
    [CrossRef]
  25. F. W. Young, J. de Leeuw, Y. Takane, “Regression with qualitative and quantitative variables: An alternating least squares method with optimal features,” Psychometrika 41, 505–526 (1976).
    [CrossRef]
  26. B. C. Levy, Principles of Signal Detection and Parameter Estimation (Springer Publishing Company, 2008).
    [CrossRef]
  27. B. K. C. Lee, J. Siegel, S. E. D. Webb, L. S. Fort, M. J. Cole, R. Jones, K. Dowling, M. J. Lever, P. M. W. French, “Application of the Stretched Exponential Function to Fluorescence Lifetime Imaging,” Biophys. J. 81, 1265–1274 (2001).
    [CrossRef] [PubMed]
  28. J. Park, P. Pande, S. Shrestha, F. Clubb, B. E. Applegate, J. A. Jo, “Biochemical characterization of atherosclerotic plaques by endogenous multispectral fluorescence lifetime imaging microscopy,” Atherosclerosis 220, 394–401 (2011).
    [CrossRef] [PubMed]
  29. J. M. Jabbour, S. Cheng, B. H. Malik, R. Cuenca, J. A. Jo, J. Wright, Y.-S. L. Cheng, K. C. Maitland, “Fluorescence lifetime imaging and reflectance confocal microscopy for multiscale imaging of oral precancer,” J. Biomed. Opt. 18, 046012 (2013).
    [CrossRef] [PubMed]
  30. Robert M. Clegg, A. Periasamy, FLIM Microscopy in Biology and Medicine (Chapman and Hall/CRC, 2009).
    [CrossRef]
  31. P. Pande, J. A. Jo, “Automated analysis of fluorescence lifetime imaging microscopy (flim) data based on the Laguerre deconvolution method,” IEEE Trans. Biomed. Eng. 58, 172–181 (2011).
    [CrossRef]
  32. J. Nocedal, S. J. Wright, Numerical Optimization (Springer, 2000).
  33. K. Arakawa, K. Isoda, T. Ito, K. Nakajima, T. Shibuya, F. Ohsuzu, “Fluorescence analysis of biochemical constituents identifies atherosclerotic plaque with a thin fibrous cap,” Arterioscler. Thromb. Vasc. 22, 1002–1007 (2002).
    [CrossRef]

2014 (2)

O. Gutierrez-Navarro, D. Campos-Delgado, E. Arce-Santana, M. Mendez, J. Jo, “Blind end-member and abundance extraction for multi-spectral fluorescence lifetime imaging microscopy data,” IEEE J. Biomed. Health Inform. 18, 606–617 (2014).
[CrossRef] [PubMed]

C. Andreou, V. Karathanassi, “Estimation of the number of endmembers using robust outlier detection method,” IEEE J. Sel. Topics Appl. Earth Observ. 7, 247–256 (2014).
[CrossRef]

2013 (4)

A. Ambikapathi, T.-H. Chan, C.-Y. Chi, K. Keizer, “Hyperspectral data geometry-based estimation of number of endmembers using p -norm-based pure pixel identification algorithm,” IEEE Trans. Geosci. Remote Sens. 51, 2753–2769 (2013).
[CrossRef]

O. Gutierrez-Navarro, D. Campos-Delgado, E. Arce-Santana, M. Mendez, J. Jo, “A fully constrained optimization method for time-resolved multi-spectral fluorescence lifetime imaging microscopy data unmixing,” IEEE Trans. Biomed. Eng. 60, 1711–1720 (2013).
[CrossRef] [PubMed]

R. Heylen, P. Scheunders, “Hyperspectral intrinsic dimensionality estimation with nearest-neighbor distance ratios,” IEEE J. Sel. Topics Appl. Earth Observ. 6, 570–579 (2013).
[CrossRef]

J. M. Jabbour, S. Cheng, B. H. Malik, R. Cuenca, J. A. Jo, J. Wright, Y.-S. L. Cheng, K. C. Maitland, “Fluorescence lifetime imaging and reflectance confocal microscopy for multiscale imaging of oral precancer,” J. Biomed. Opt. 18, 046012 (2013).
[CrossRef] [PubMed]

2012 (1)

J. Bioucas-Dias, A. Plaza, N. Dobigeon, M. Parente, Q. Du, P. Gader, J. Chanussot, “Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches,” IEEE J. Sel. Topics Appl. Earth Observ. 5, 354–379 (2012).
[CrossRef]

2011 (2)

P. Pande, J. A. Jo, “Automated analysis of fluorescence lifetime imaging microscopy (flim) data based on the Laguerre deconvolution method,” IEEE Trans. Biomed. Eng. 58, 172–181 (2011).
[CrossRef]

J. Park, P. Pande, S. Shrestha, F. Clubb, B. E. Applegate, J. A. Jo, “Biochemical characterization of atherosclerotic plaques by endogenous multispectral fluorescence lifetime imaging microscopy,” Atherosclerosis 220, 394–401 (2011).
[CrossRef] [PubMed]

2010 (2)

L. Marcu, “Fluorescence lifetime in cardiovascular diagnostics,” J. Biomed. Opt. 15, 011106 (2010).
[CrossRef] [PubMed]

J. A. Jo, B. Applegate, J. Park, S. Shrestha, P. Pande, I. Gimenez-Conti, J. Brandon, “In vivo simultaneous morphological and biochemical optical imaging of oral epithelial cancer,” IEEE Trans. Biomed. Eng. 57, 2596–2599 (2010).
[CrossRef] [PubMed]

2009 (1)

H. Xu, B. W. Rice, “In-vivo fluorescence imaging with a multivariate curve resolution spectral unmixing technique,” J. Biomed. Opt. 14, 064011 (2009).
[CrossRef]

2008 (3)

J. G. Gert-JanKremers, Erik B. van Munster, J. Theodorus, W. J. Gadella, “Quantitative lifetime unmixing of multiexponentially decaying fluorophores using single-frequency fluorescence lifetime imaging microscopy,” Biophys. J. 95, 378–389 (2008).
[CrossRef]

N. Galletly, J. McGinty, C. Dunsby, F. Teixeira, J. Requejo-Isidro, I. Munro, D. Elson, M. Neil, A. Chu, P. French, G. Stamp, “Fluorescence lifetime imaging distinguishes basal cell carcinoma from surrounding uninvolved skin,” Br. J. Dermatol. 159, 152–161 (2008).
[CrossRef] [PubMed]

J. Bioucas-Dias, J. Nascimento, “Hyperspectral subspace identification,” IEEE Trans. Geosci. Remote Sens. 46, 2435–2445 (2008).
[CrossRef]

2005 (1)

T. Zimmermann, “Spectral imaging and linear unmixing in light microscopy,” Molecular Biology 95, 245–265 (2005).

2004 (1)

C.-I. Chang, Q. Du, “Estimation of number of spectrally distinct signal sources in hyperspectral imagery,” IEEE Trans. Geosci. Remote Sens. 42, 608–619 (2004).
[CrossRef]

2002 (2)

K. Arakawa, K. Isoda, T. Ito, K. Nakajima, T. Shibuya, F. Ohsuzu, “Fluorescence analysis of biochemical constituents identifies atherosclerotic plaque with a thin fibrous cap,” Arterioscler. Thromb. Vasc. 22, 1002–1007 (2002).
[CrossRef]

D. Manolakis, G. Shaw, “Detection algorithms for hyperspectral imaging applications,” IEEE Signal Process. Mag. 19, 29–43 (2002).
[CrossRef]

2001 (1)

B. K. C. Lee, J. Siegel, S. E. D. Webb, L. S. Fort, M. J. Cole, R. Jones, K. Dowling, M. J. Lever, P. M. W. French, “Application of the Stretched Exponential Function to Fluorescence Lifetime Imaging,” Biophys. J. 81, 1265–1274 (2001).
[CrossRef] [PubMed]

1998 (1)

K. S. M. Mycek, N. Nishioka, “Colonic polyp differentiation using time-resolved autofluorescence spectroscopy,” Gastrointest. endosc. 48, 390–394 (1998).
[CrossRef] [PubMed]

1994 (1)

M. Craig, “Minimum-volume transforms for remotely sensed data,” IEEE Trans. Geosci. Remote Sens. 32, 542–552 (1994).
[CrossRef]

1978 (1)

J. Rissanen, “Modeling by shortest data description,” Automatica 14, 465–471 (1978).
[CrossRef]

1976 (1)

F. W. Young, J. de Leeuw, Y. Takane, “Regression with qualitative and quantitative variables: An alternating least squares method with optimal features,” Psychometrika 41, 505–526 (1976).
[CrossRef]

1974 (1)

H. Akaike, “A new look at the statistical model identification,” IEEE Trans. Automat. Contr. 19, 716–723 (1974).
[CrossRef]

Akaike, H.

H. Akaike, “A new look at the statistical model identification,” IEEE Trans. Automat. Contr. 19, 716–723 (1974).
[CrossRef]

Ambikapathi, A.

A. Ambikapathi, T.-H. Chan, C.-Y. Chi, K. Keizer, “Hyperspectral data geometry-based estimation of number of endmembers using p -norm-based pure pixel identification algorithm,” IEEE Trans. Geosci. Remote Sens. 51, 2753–2769 (2013).
[CrossRef]

Andreou, C.

C. Andreou, V. Karathanassi, “Estimation of the number of endmembers using robust outlier detection method,” IEEE J. Sel. Topics Appl. Earth Observ. 7, 247–256 (2014).
[CrossRef]

Applegate, B.

J. A. Jo, B. Applegate, J. Park, S. Shrestha, P. Pande, I. Gimenez-Conti, J. Brandon, “In vivo simultaneous morphological and biochemical optical imaging of oral epithelial cancer,” IEEE Trans. Biomed. Eng. 57, 2596–2599 (2010).
[CrossRef] [PubMed]

Applegate, B. E.

J. Park, P. Pande, S. Shrestha, F. Clubb, B. E. Applegate, J. A. Jo, “Biochemical characterization of atherosclerotic plaques by endogenous multispectral fluorescence lifetime imaging microscopy,” Atherosclerosis 220, 394–401 (2011).
[CrossRef] [PubMed]

Arakawa, K.

K. Arakawa, K. Isoda, T. Ito, K. Nakajima, T. Shibuya, F. Ohsuzu, “Fluorescence analysis of biochemical constituents identifies atherosclerotic plaque with a thin fibrous cap,” Arterioscler. Thromb. Vasc. 22, 1002–1007 (2002).
[CrossRef]

Arce-Santana, E.

O. Gutierrez-Navarro, D. Campos-Delgado, E. Arce-Santana, M. Mendez, J. Jo, “Blind end-member and abundance extraction for multi-spectral fluorescence lifetime imaging microscopy data,” IEEE J. Biomed. Health Inform. 18, 606–617 (2014).
[CrossRef] [PubMed]

O. Gutierrez-Navarro, D. Campos-Delgado, E. Arce-Santana, M. Mendez, J. Jo, “A fully constrained optimization method for time-resolved multi-spectral fluorescence lifetime imaging microscopy data unmixing,” IEEE Trans. Biomed. Eng. 60, 1711–1720 (2013).
[CrossRef] [PubMed]

Berman, M.

P. Vallotton, A. Phatak, M. Berman, “Spectral Imaging and Unmixing,” in Fluorescence Applications in Biotechnology and Life Sciences, E. M. Goldys, ed. (Wiley-Blackwell, 2009).

Bioucas-Dias, J.

J. Bioucas-Dias, A. Plaza, N. Dobigeon, M. Parente, Q. Du, P. Gader, J. Chanussot, “Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches,” IEEE J. Sel. Topics Appl. Earth Observ. 5, 354–379 (2012).
[CrossRef]

J. Bioucas-Dias, J. Nascimento, “Hyperspectral subspace identification,” IEEE Trans. Geosci. Remote Sens. 46, 2435–2445 (2008).
[CrossRef]

Blank, P. S.

S. S. Vogel, P. S. Blank, S. V. Koushik, C. Thaler, “Spectral imaging and its use in the measurement of Förster resonance energy transfer in living cells,” in Fret and Flim Techniques. Laboratory Techniques in Biochemistry and Molecular Biology, T. Gadella, ed. 33, 351–394, (Elsevier, 2009).
[CrossRef]

Boyd, S.

S. Boyd, L. Vandenberghe, Convex Optimization (Cambridge University Press, New York, 2004).
[CrossRef]

Brandon, J.

J. A. Jo, B. Applegate, J. Park, S. Shrestha, P. Pande, I. Gimenez-Conti, J. Brandon, “In vivo simultaneous morphological and biochemical optical imaging of oral epithelial cancer,” IEEE Trans. Biomed. Eng. 57, 2596–2599 (2010).
[CrossRef] [PubMed]

Campos-Delgado, D.

O. Gutierrez-Navarro, D. Campos-Delgado, E. Arce-Santana, M. Mendez, J. Jo, “Blind end-member and abundance extraction for multi-spectral fluorescence lifetime imaging microscopy data,” IEEE J. Biomed. Health Inform. 18, 606–617 (2014).
[CrossRef] [PubMed]

O. Gutierrez-Navarro, D. Campos-Delgado, E. Arce-Santana, M. Mendez, J. Jo, “A fully constrained optimization method for time-resolved multi-spectral fluorescence lifetime imaging microscopy data unmixing,” IEEE Trans. Biomed. Eng. 60, 1711–1720 (2013).
[CrossRef] [PubMed]

Chan, T.-H.

A. Ambikapathi, T.-H. Chan, C.-Y. Chi, K. Keizer, “Hyperspectral data geometry-based estimation of number of endmembers using p -norm-based pure pixel identification algorithm,” IEEE Trans. Geosci. Remote Sens. 51, 2753–2769 (2013).
[CrossRef]

Chang, C.-I.

C.-I. Chang, Q. Du, “Estimation of number of spectrally distinct signal sources in hyperspectral imagery,” IEEE Trans. Geosci. Remote Sens. 42, 608–619 (2004).
[CrossRef]

J. Harsanyi, W. Farrand, C.-I. Chang, “Determining the number and identity of spectral endmembers: An integrated approach using Neyman-Pearson eigenthresholding and iterative constrained rms error minimization,” presented at the 9th Thematic Conference on Geological Remote Sensing, Pasadena, California, USA (1993).

Chanussot, J.

J. Bioucas-Dias, A. Plaza, N. Dobigeon, M. Parente, Q. Du, P. Gader, J. Chanussot, “Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches,” IEEE J. Sel. Topics Appl. Earth Observ. 5, 354–379 (2012).
[CrossRef]

Cheng, S.

J. M. Jabbour, S. Cheng, B. H. Malik, R. Cuenca, J. A. Jo, J. Wright, Y.-S. L. Cheng, K. C. Maitland, “Fluorescence lifetime imaging and reflectance confocal microscopy for multiscale imaging of oral precancer,” J. Biomed. Opt. 18, 046012 (2013).
[CrossRef] [PubMed]

Cheng, Y.-S. L.

J. M. Jabbour, S. Cheng, B. H. Malik, R. Cuenca, J. A. Jo, J. Wright, Y.-S. L. Cheng, K. C. Maitland, “Fluorescence lifetime imaging and reflectance confocal microscopy for multiscale imaging of oral precancer,” J. Biomed. Opt. 18, 046012 (2013).
[CrossRef] [PubMed]

Chi, C.-Y.

A. Ambikapathi, T.-H. Chan, C.-Y. Chi, K. Keizer, “Hyperspectral data geometry-based estimation of number of endmembers using p -norm-based pure pixel identification algorithm,” IEEE Trans. Geosci. Remote Sens. 51, 2753–2769 (2013).
[CrossRef]

Chu, A.

N. Galletly, J. McGinty, C. Dunsby, F. Teixeira, J. Requejo-Isidro, I. Munro, D. Elson, M. Neil, A. Chu, P. French, G. Stamp, “Fluorescence lifetime imaging distinguishes basal cell carcinoma from surrounding uninvolved skin,” Br. J. Dermatol. 159, 152–161 (2008).
[CrossRef] [PubMed]

Clegg, Robert M.

Robert M. Clegg, A. Periasamy, FLIM Microscopy in Biology and Medicine (Chapman and Hall/CRC, 2009).
[CrossRef]

Clubb, F.

J. Park, P. Pande, S. Shrestha, F. Clubb, B. E. Applegate, J. A. Jo, “Biochemical characterization of atherosclerotic plaques by endogenous multispectral fluorescence lifetime imaging microscopy,” Atherosclerosis 220, 394–401 (2011).
[CrossRef] [PubMed]

Cole, M. J.

B. K. C. Lee, J. Siegel, S. E. D. Webb, L. S. Fort, M. J. Cole, R. Jones, K. Dowling, M. J. Lever, P. M. W. French, “Application of the Stretched Exponential Function to Fluorescence Lifetime Imaging,” Biophys. J. 81, 1265–1274 (2001).
[CrossRef] [PubMed]

Craig, M.

M. Craig, “Minimum-volume transforms for remotely sensed data,” IEEE Trans. Geosci. Remote Sens. 32, 542–552 (1994).
[CrossRef]

Cuenca, R.

J. M. Jabbour, S. Cheng, B. H. Malik, R. Cuenca, J. A. Jo, J. Wright, Y.-S. L. Cheng, K. C. Maitland, “Fluorescence lifetime imaging and reflectance confocal microscopy for multiscale imaging of oral precancer,” J. Biomed. Opt. 18, 046012 (2013).
[CrossRef] [PubMed]

de Leeuw, J.

F. W. Young, J. de Leeuw, Y. Takane, “Regression with qualitative and quantitative variables: An alternating least squares method with optimal features,” Psychometrika 41, 505–526 (1976).
[CrossRef]

Dobigeon, N.

J. Bioucas-Dias, A. Plaza, N. Dobigeon, M. Parente, Q. Du, P. Gader, J. Chanussot, “Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches,” IEEE J. Sel. Topics Appl. Earth Observ. 5, 354–379 (2012).
[CrossRef]

Dowling, K.

B. K. C. Lee, J. Siegel, S. E. D. Webb, L. S. Fort, M. J. Cole, R. Jones, K. Dowling, M. J. Lever, P. M. W. French, “Application of the Stretched Exponential Function to Fluorescence Lifetime Imaging,” Biophys. J. 81, 1265–1274 (2001).
[CrossRef] [PubMed]

Du, Q.

J. Bioucas-Dias, A. Plaza, N. Dobigeon, M. Parente, Q. Du, P. Gader, J. Chanussot, “Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches,” IEEE J. Sel. Topics Appl. Earth Observ. 5, 354–379 (2012).
[CrossRef]

C.-I. Chang, Q. Du, “Estimation of number of spectrally distinct signal sources in hyperspectral imagery,” IEEE Trans. Geosci. Remote Sens. 42, 608–619 (2004).
[CrossRef]

Dunsby, C.

N. Galletly, J. McGinty, C. Dunsby, F. Teixeira, J. Requejo-Isidro, I. Munro, D. Elson, M. Neil, A. Chu, P. French, G. Stamp, “Fluorescence lifetime imaging distinguishes basal cell carcinoma from surrounding uninvolved skin,” Br. J. Dermatol. 159, 152–161 (2008).
[CrossRef] [PubMed]

Elson, D.

N. Galletly, J. McGinty, C. Dunsby, F. Teixeira, J. Requejo-Isidro, I. Munro, D. Elson, M. Neil, A. Chu, P. French, G. Stamp, “Fluorescence lifetime imaging distinguishes basal cell carcinoma from surrounding uninvolved skin,” Br. J. Dermatol. 159, 152–161 (2008).
[CrossRef] [PubMed]

Farrand, W.

J. Harsanyi, W. Farrand, C.-I. Chang, “Determining the number and identity of spectral endmembers: An integrated approach using Neyman-Pearson eigenthresholding and iterative constrained rms error minimization,” presented at the 9th Thematic Conference on Geological Remote Sensing, Pasadena, California, USA (1993).

Fort, L. S.

B. K. C. Lee, J. Siegel, S. E. D. Webb, L. S. Fort, M. J. Cole, R. Jones, K. Dowling, M. J. Lever, P. M. W. French, “Application of the Stretched Exponential Function to Fluorescence Lifetime Imaging,” Biophys. J. 81, 1265–1274 (2001).
[CrossRef] [PubMed]

French, P.

N. Galletly, J. McGinty, C. Dunsby, F. Teixeira, J. Requejo-Isidro, I. Munro, D. Elson, M. Neil, A. Chu, P. French, G. Stamp, “Fluorescence lifetime imaging distinguishes basal cell carcinoma from surrounding uninvolved skin,” Br. J. Dermatol. 159, 152–161 (2008).
[CrossRef] [PubMed]

French, P. M. W.

B. K. C. Lee, J. Siegel, S. E. D. Webb, L. S. Fort, M. J. Cole, R. Jones, K. Dowling, M. J. Lever, P. M. W. French, “Application of the Stretched Exponential Function to Fluorescence Lifetime Imaging,” Biophys. J. 81, 1265–1274 (2001).
[CrossRef] [PubMed]

Gadella, W. J.

J. G. Gert-JanKremers, Erik B. van Munster, J. Theodorus, W. J. Gadella, “Quantitative lifetime unmixing of multiexponentially decaying fluorophores using single-frequency fluorescence lifetime imaging microscopy,” Biophys. J. 95, 378–389 (2008).
[CrossRef]

Gader, P.

J. Bioucas-Dias, A. Plaza, N. Dobigeon, M. Parente, Q. Du, P. Gader, J. Chanussot, “Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches,” IEEE J. Sel. Topics Appl. Earth Observ. 5, 354–379 (2012).
[CrossRef]

Galletly, N.

N. Galletly, J. McGinty, C. Dunsby, F. Teixeira, J. Requejo-Isidro, I. Munro, D. Elson, M. Neil, A. Chu, P. French, G. Stamp, “Fluorescence lifetime imaging distinguishes basal cell carcinoma from surrounding uninvolved skin,” Br. J. Dermatol. 159, 152–161 (2008).
[CrossRef] [PubMed]

Gert-JanKremers, J. G.

J. G. Gert-JanKremers, Erik B. van Munster, J. Theodorus, W. J. Gadella, “Quantitative lifetime unmixing of multiexponentially decaying fluorophores using single-frequency fluorescence lifetime imaging microscopy,” Biophys. J. 95, 378–389 (2008).
[CrossRef]

Gimenez-Conti, I.

J. A. Jo, B. Applegate, J. Park, S. Shrestha, P. Pande, I. Gimenez-Conti, J. Brandon, “In vivo simultaneous morphological and biochemical optical imaging of oral epithelial cancer,” IEEE Trans. Biomed. Eng. 57, 2596–2599 (2010).
[CrossRef] [PubMed]

Gutierrez-Navarro, O.

O. Gutierrez-Navarro, D. Campos-Delgado, E. Arce-Santana, M. Mendez, J. Jo, “Blind end-member and abundance extraction for multi-spectral fluorescence lifetime imaging microscopy data,” IEEE J. Biomed. Health Inform. 18, 606–617 (2014).
[CrossRef] [PubMed]

O. Gutierrez-Navarro, D. Campos-Delgado, E. Arce-Santana, M. Mendez, J. Jo, “A fully constrained optimization method for time-resolved multi-spectral fluorescence lifetime imaging microscopy data unmixing,” IEEE Trans. Biomed. Eng. 60, 1711–1720 (2013).
[CrossRef] [PubMed]

Harsanyi, J.

J. Harsanyi, W. Farrand, C.-I. Chang, “Determining the number and identity of spectral endmembers: An integrated approach using Neyman-Pearson eigenthresholding and iterative constrained rms error minimization,” presented at the 9th Thematic Conference on Geological Remote Sensing, Pasadena, California, USA (1993).

Heylen, R.

R. Heylen, P. Scheunders, “Hyperspectral intrinsic dimensionality estimation with nearest-neighbor distance ratios,” IEEE J. Sel. Topics Appl. Earth Observ. 6, 570–579 (2013).
[CrossRef]

Isoda, K.

K. Arakawa, K. Isoda, T. Ito, K. Nakajima, T. Shibuya, F. Ohsuzu, “Fluorescence analysis of biochemical constituents identifies atherosclerotic plaque with a thin fibrous cap,” Arterioscler. Thromb. Vasc. 22, 1002–1007 (2002).
[CrossRef]

Ito, T.

K. Arakawa, K. Isoda, T. Ito, K. Nakajima, T. Shibuya, F. Ohsuzu, “Fluorescence analysis of biochemical constituents identifies atherosclerotic plaque with a thin fibrous cap,” Arterioscler. Thromb. Vasc. 22, 1002–1007 (2002).
[CrossRef]

Jabbour, J. M.

J. M. Jabbour, S. Cheng, B. H. Malik, R. Cuenca, J. A. Jo, J. Wright, Y.-S. L. Cheng, K. C. Maitland, “Fluorescence lifetime imaging and reflectance confocal microscopy for multiscale imaging of oral precancer,” J. Biomed. Opt. 18, 046012 (2013).
[CrossRef] [PubMed]

Jo, J.

O. Gutierrez-Navarro, D. Campos-Delgado, E. Arce-Santana, M. Mendez, J. Jo, “Blind end-member and abundance extraction for multi-spectral fluorescence lifetime imaging microscopy data,” IEEE J. Biomed. Health Inform. 18, 606–617 (2014).
[CrossRef] [PubMed]

O. Gutierrez-Navarro, D. Campos-Delgado, E. Arce-Santana, M. Mendez, J. Jo, “A fully constrained optimization method for time-resolved multi-spectral fluorescence lifetime imaging microscopy data unmixing,” IEEE Trans. Biomed. Eng. 60, 1711–1720 (2013).
[CrossRef] [PubMed]

Jo, J. A.

J. M. Jabbour, S. Cheng, B. H. Malik, R. Cuenca, J. A. Jo, J. Wright, Y.-S. L. Cheng, K. C. Maitland, “Fluorescence lifetime imaging and reflectance confocal microscopy for multiscale imaging of oral precancer,” J. Biomed. Opt. 18, 046012 (2013).
[CrossRef] [PubMed]

J. Park, P. Pande, S. Shrestha, F. Clubb, B. E. Applegate, J. A. Jo, “Biochemical characterization of atherosclerotic plaques by endogenous multispectral fluorescence lifetime imaging microscopy,” Atherosclerosis 220, 394–401 (2011).
[CrossRef] [PubMed]

P. Pande, J. A. Jo, “Automated analysis of fluorescence lifetime imaging microscopy (flim) data based on the Laguerre deconvolution method,” IEEE Trans. Biomed. Eng. 58, 172–181 (2011).
[CrossRef]

J. A. Jo, B. Applegate, J. Park, S. Shrestha, P. Pande, I. Gimenez-Conti, J. Brandon, “In vivo simultaneous morphological and biochemical optical imaging of oral epithelial cancer,” IEEE Trans. Biomed. Eng. 57, 2596–2599 (2010).
[CrossRef] [PubMed]

Jones, R.

B. K. C. Lee, J. Siegel, S. E. D. Webb, L. S. Fort, M. J. Cole, R. Jones, K. Dowling, M. J. Lever, P. M. W. French, “Application of the Stretched Exponential Function to Fluorescence Lifetime Imaging,” Biophys. J. 81, 1265–1274 (2001).
[CrossRef] [PubMed]

Karathanassi, V.

C. Andreou, V. Karathanassi, “Estimation of the number of endmembers using robust outlier detection method,” IEEE J. Sel. Topics Appl. Earth Observ. 7, 247–256 (2014).
[CrossRef]

Keizer, K.

A. Ambikapathi, T.-H. Chan, C.-Y. Chi, K. Keizer, “Hyperspectral data geometry-based estimation of number of endmembers using p -norm-based pure pixel identification algorithm,” IEEE Trans. Geosci. Remote Sens. 51, 2753–2769 (2013).
[CrossRef]

Koushik, S. V.

S. S. Vogel, P. S. Blank, S. V. Koushik, C. Thaler, “Spectral imaging and its use in the measurement of Förster resonance energy transfer in living cells,” in Fret and Flim Techniques. Laboratory Techniques in Biochemistry and Molecular Biology, T. Gadella, ed. 33, 351–394, (Elsevier, 2009).
[CrossRef]

Lakowicz, J. R.

J. R. Lakowicz, Principles of Fluorescence Spectroscopy (Springer, 2006).
[CrossRef]

Lee, B. K. C.

B. K. C. Lee, J. Siegel, S. E. D. Webb, L. S. Fort, M. J. Cole, R. Jones, K. Dowling, M. J. Lever, P. M. W. French, “Application of the Stretched Exponential Function to Fluorescence Lifetime Imaging,” Biophys. J. 81, 1265–1274 (2001).
[CrossRef] [PubMed]

Lever, M. J.

B. K. C. Lee, J. Siegel, S. E. D. Webb, L. S. Fort, M. J. Cole, R. Jones, K. Dowling, M. J. Lever, P. M. W. French, “Application of the Stretched Exponential Function to Fluorescence Lifetime Imaging,” Biophys. J. 81, 1265–1274 (2001).
[CrossRef] [PubMed]

Levy, B. C.

B. C. Levy, Principles of Signal Detection and Parameter Estimation (Springer Publishing Company, 2008).
[CrossRef]

Maitland, K. C.

J. M. Jabbour, S. Cheng, B. H. Malik, R. Cuenca, J. A. Jo, J. Wright, Y.-S. L. Cheng, K. C. Maitland, “Fluorescence lifetime imaging and reflectance confocal microscopy for multiscale imaging of oral precancer,” J. Biomed. Opt. 18, 046012 (2013).
[CrossRef] [PubMed]

Malik, B. H.

J. M. Jabbour, S. Cheng, B. H. Malik, R. Cuenca, J. A. Jo, J. Wright, Y.-S. L. Cheng, K. C. Maitland, “Fluorescence lifetime imaging and reflectance confocal microscopy for multiscale imaging of oral precancer,” J. Biomed. Opt. 18, 046012 (2013).
[CrossRef] [PubMed]

Manolakis, D.

D. Manolakis, G. Shaw, “Detection algorithms for hyperspectral imaging applications,” IEEE Signal Process. Mag. 19, 29–43 (2002).
[CrossRef]

Marcu, L.

L. Marcu, “Fluorescence lifetime in cardiovascular diagnostics,” J. Biomed. Opt. 15, 011106 (2010).
[CrossRef] [PubMed]

McGinty, J.

N. Galletly, J. McGinty, C. Dunsby, F. Teixeira, J. Requejo-Isidro, I. Munro, D. Elson, M. Neil, A. Chu, P. French, G. Stamp, “Fluorescence lifetime imaging distinguishes basal cell carcinoma from surrounding uninvolved skin,” Br. J. Dermatol. 159, 152–161 (2008).
[CrossRef] [PubMed]

Mendez, M.

O. Gutierrez-Navarro, D. Campos-Delgado, E. Arce-Santana, M. Mendez, J. Jo, “Blind end-member and abundance extraction for multi-spectral fluorescence lifetime imaging microscopy data,” IEEE J. Biomed. Health Inform. 18, 606–617 (2014).
[CrossRef] [PubMed]

O. Gutierrez-Navarro, D. Campos-Delgado, E. Arce-Santana, M. Mendez, J. Jo, “A fully constrained optimization method for time-resolved multi-spectral fluorescence lifetime imaging microscopy data unmixing,” IEEE Trans. Biomed. Eng. 60, 1711–1720 (2013).
[CrossRef] [PubMed]

Munro, I.

N. Galletly, J. McGinty, C. Dunsby, F. Teixeira, J. Requejo-Isidro, I. Munro, D. Elson, M. Neil, A. Chu, P. French, G. Stamp, “Fluorescence lifetime imaging distinguishes basal cell carcinoma from surrounding uninvolved skin,” Br. J. Dermatol. 159, 152–161 (2008).
[CrossRef] [PubMed]

Mycek, K. S. M.

K. S. M. Mycek, N. Nishioka, “Colonic polyp differentiation using time-resolved autofluorescence spectroscopy,” Gastrointest. endosc. 48, 390–394 (1998).
[CrossRef] [PubMed]

Nakajima, K.

K. Arakawa, K. Isoda, T. Ito, K. Nakajima, T. Shibuya, F. Ohsuzu, “Fluorescence analysis of biochemical constituents identifies atherosclerotic plaque with a thin fibrous cap,” Arterioscler. Thromb. Vasc. 22, 1002–1007 (2002).
[CrossRef]

Nascimento, J.

J. Bioucas-Dias, J. Nascimento, “Hyperspectral subspace identification,” IEEE Trans. Geosci. Remote Sens. 46, 2435–2445 (2008).
[CrossRef]

Neil, M.

N. Galletly, J. McGinty, C. Dunsby, F. Teixeira, J. Requejo-Isidro, I. Munro, D. Elson, M. Neil, A. Chu, P. French, G. Stamp, “Fluorescence lifetime imaging distinguishes basal cell carcinoma from surrounding uninvolved skin,” Br. J. Dermatol. 159, 152–161 (2008).
[CrossRef] [PubMed]

Nishioka, N.

K. S. M. Mycek, N. Nishioka, “Colonic polyp differentiation using time-resolved autofluorescence spectroscopy,” Gastrointest. endosc. 48, 390–394 (1998).
[CrossRef] [PubMed]

Nocedal, J.

J. Nocedal, S. J. Wright, Numerical Optimization (Springer, 2000).

Ohsuzu, F.

K. Arakawa, K. Isoda, T. Ito, K. Nakajima, T. Shibuya, F. Ohsuzu, “Fluorescence analysis of biochemical constituents identifies atherosclerotic plaque with a thin fibrous cap,” Arterioscler. Thromb. Vasc. 22, 1002–1007 (2002).
[CrossRef]

Pande, P.

P. Pande, J. A. Jo, “Automated analysis of fluorescence lifetime imaging microscopy (flim) data based on the Laguerre deconvolution method,” IEEE Trans. Biomed. Eng. 58, 172–181 (2011).
[CrossRef]

J. Park, P. Pande, S. Shrestha, F. Clubb, B. E. Applegate, J. A. Jo, “Biochemical characterization of atherosclerotic plaques by endogenous multispectral fluorescence lifetime imaging microscopy,” Atherosclerosis 220, 394–401 (2011).
[CrossRef] [PubMed]

J. A. Jo, B. Applegate, J. Park, S. Shrestha, P. Pande, I. Gimenez-Conti, J. Brandon, “In vivo simultaneous morphological and biochemical optical imaging of oral epithelial cancer,” IEEE Trans. Biomed. Eng. 57, 2596–2599 (2010).
[CrossRef] [PubMed]

Parente, M.

J. Bioucas-Dias, A. Plaza, N. Dobigeon, M. Parente, Q. Du, P. Gader, J. Chanussot, “Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches,” IEEE J. Sel. Topics Appl. Earth Observ. 5, 354–379 (2012).
[CrossRef]

Park, J.

J. Park, P. Pande, S. Shrestha, F. Clubb, B. E. Applegate, J. A. Jo, “Biochemical characterization of atherosclerotic plaques by endogenous multispectral fluorescence lifetime imaging microscopy,” Atherosclerosis 220, 394–401 (2011).
[CrossRef] [PubMed]

J. A. Jo, B. Applegate, J. Park, S. Shrestha, P. Pande, I. Gimenez-Conti, J. Brandon, “In vivo simultaneous morphological and biochemical optical imaging of oral epithelial cancer,” IEEE Trans. Biomed. Eng. 57, 2596–2599 (2010).
[CrossRef] [PubMed]

Periasamy, A.

Robert M. Clegg, A. Periasamy, FLIM Microscopy in Biology and Medicine (Chapman and Hall/CRC, 2009).
[CrossRef]

Phatak, A.

P. Vallotton, A. Phatak, M. Berman, “Spectral Imaging and Unmixing,” in Fluorescence Applications in Biotechnology and Life Sciences, E. M. Goldys, ed. (Wiley-Blackwell, 2009).

Plaza, A.

J. Bioucas-Dias, A. Plaza, N. Dobigeon, M. Parente, Q. Du, P. Gader, J. Chanussot, “Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches,” IEEE J. Sel. Topics Appl. Earth Observ. 5, 354–379 (2012).
[CrossRef]

Requejo-Isidro, J.

N. Galletly, J. McGinty, C. Dunsby, F. Teixeira, J. Requejo-Isidro, I. Munro, D. Elson, M. Neil, A. Chu, P. French, G. Stamp, “Fluorescence lifetime imaging distinguishes basal cell carcinoma from surrounding uninvolved skin,” Br. J. Dermatol. 159, 152–161 (2008).
[CrossRef] [PubMed]

Rice, B. W.

H. Xu, B. W. Rice, “In-vivo fluorescence imaging with a multivariate curve resolution spectral unmixing technique,” J. Biomed. Opt. 14, 064011 (2009).
[CrossRef]

Rissanen, J.

J. Rissanen, “Modeling by shortest data description,” Automatica 14, 465–471 (1978).
[CrossRef]

Scheunders, P.

R. Heylen, P. Scheunders, “Hyperspectral intrinsic dimensionality estimation with nearest-neighbor distance ratios,” IEEE J. Sel. Topics Appl. Earth Observ. 6, 570–579 (2013).
[CrossRef]

Shaw, G.

D. Manolakis, G. Shaw, “Detection algorithms for hyperspectral imaging applications,” IEEE Signal Process. Mag. 19, 29–43 (2002).
[CrossRef]

Shibuya, T.

K. Arakawa, K. Isoda, T. Ito, K. Nakajima, T. Shibuya, F. Ohsuzu, “Fluorescence analysis of biochemical constituents identifies atherosclerotic plaque with a thin fibrous cap,” Arterioscler. Thromb. Vasc. 22, 1002–1007 (2002).
[CrossRef]

Shrestha, S.

J. Park, P. Pande, S. Shrestha, F. Clubb, B. E. Applegate, J. A. Jo, “Biochemical characterization of atherosclerotic plaques by endogenous multispectral fluorescence lifetime imaging microscopy,” Atherosclerosis 220, 394–401 (2011).
[CrossRef] [PubMed]

J. A. Jo, B. Applegate, J. Park, S. Shrestha, P. Pande, I. Gimenez-Conti, J. Brandon, “In vivo simultaneous morphological and biochemical optical imaging of oral epithelial cancer,” IEEE Trans. Biomed. Eng. 57, 2596–2599 (2010).
[CrossRef] [PubMed]

Siegel, J.

B. K. C. Lee, J. Siegel, S. E. D. Webb, L. S. Fort, M. J. Cole, R. Jones, K. Dowling, M. J. Lever, P. M. W. French, “Application of the Stretched Exponential Function to Fluorescence Lifetime Imaging,” Biophys. J. 81, 1265–1274 (2001).
[CrossRef] [PubMed]

Stamp, G.

N. Galletly, J. McGinty, C. Dunsby, F. Teixeira, J. Requejo-Isidro, I. Munro, D. Elson, M. Neil, A. Chu, P. French, G. Stamp, “Fluorescence lifetime imaging distinguishes basal cell carcinoma from surrounding uninvolved skin,” Br. J. Dermatol. 159, 152–161 (2008).
[CrossRef] [PubMed]

Takane, Y.

F. W. Young, J. de Leeuw, Y. Takane, “Regression with qualitative and quantitative variables: An alternating least squares method with optimal features,” Psychometrika 41, 505–526 (1976).
[CrossRef]

Teixeira, F.

N. Galletly, J. McGinty, C. Dunsby, F. Teixeira, J. Requejo-Isidro, I. Munro, D. Elson, M. Neil, A. Chu, P. French, G. Stamp, “Fluorescence lifetime imaging distinguishes basal cell carcinoma from surrounding uninvolved skin,” Br. J. Dermatol. 159, 152–161 (2008).
[CrossRef] [PubMed]

Thaler, C.

S. S. Vogel, P. S. Blank, S. V. Koushik, C. Thaler, “Spectral imaging and its use in the measurement of Förster resonance energy transfer in living cells,” in Fret and Flim Techniques. Laboratory Techniques in Biochemistry and Molecular Biology, T. Gadella, ed. 33, 351–394, (Elsevier, 2009).
[CrossRef]

Theodorus, J.

J. G. Gert-JanKremers, Erik B. van Munster, J. Theodorus, W. J. Gadella, “Quantitative lifetime unmixing of multiexponentially decaying fluorophores using single-frequency fluorescence lifetime imaging microscopy,” Biophys. J. 95, 378–389 (2008).
[CrossRef]

Vallotton, P.

P. Vallotton, A. Phatak, M. Berman, “Spectral Imaging and Unmixing,” in Fluorescence Applications in Biotechnology and Life Sciences, E. M. Goldys, ed. (Wiley-Blackwell, 2009).

van Munster, Erik B.

J. G. Gert-JanKremers, Erik B. van Munster, J. Theodorus, W. J. Gadella, “Quantitative lifetime unmixing of multiexponentially decaying fluorophores using single-frequency fluorescence lifetime imaging microscopy,” Biophys. J. 95, 378–389 (2008).
[CrossRef]

Vandenberghe, L.

S. Boyd, L. Vandenberghe, Convex Optimization (Cambridge University Press, New York, 2004).
[CrossRef]

Vogel, S. S.

S. S. Vogel, P. S. Blank, S. V. Koushik, C. Thaler, “Spectral imaging and its use in the measurement of Förster resonance energy transfer in living cells,” in Fret and Flim Techniques. Laboratory Techniques in Biochemistry and Molecular Biology, T. Gadella, ed. 33, 351–394, (Elsevier, 2009).
[CrossRef]

Webb, S. E. D.

B. K. C. Lee, J. Siegel, S. E. D. Webb, L. S. Fort, M. J. Cole, R. Jones, K. Dowling, M. J. Lever, P. M. W. French, “Application of the Stretched Exponential Function to Fluorescence Lifetime Imaging,” Biophys. J. 81, 1265–1274 (2001).
[CrossRef] [PubMed]

Wright, J.

J. M. Jabbour, S. Cheng, B. H. Malik, R. Cuenca, J. A. Jo, J. Wright, Y.-S. L. Cheng, K. C. Maitland, “Fluorescence lifetime imaging and reflectance confocal microscopy for multiscale imaging of oral precancer,” J. Biomed. Opt. 18, 046012 (2013).
[CrossRef] [PubMed]

Wright, S. J.

J. Nocedal, S. J. Wright, Numerical Optimization (Springer, 2000).

Xu, H.

H. Xu, B. W. Rice, “In-vivo fluorescence imaging with a multivariate curve resolution spectral unmixing technique,” J. Biomed. Opt. 14, 064011 (2009).
[CrossRef]

Young, F. W.

F. W. Young, J. de Leeuw, Y. Takane, “Regression with qualitative and quantitative variables: An alternating least squares method with optimal features,” Psychometrika 41, 505–526 (1976).
[CrossRef]

Zimmermann, T.

T. Zimmermann, “Spectral imaging and linear unmixing in light microscopy,” Molecular Biology 95, 245–265 (2005).

Arterioscler. Thromb. Vasc. (1)

K. Arakawa, K. Isoda, T. Ito, K. Nakajima, T. Shibuya, F. Ohsuzu, “Fluorescence analysis of biochemical constituents identifies atherosclerotic plaque with a thin fibrous cap,” Arterioscler. Thromb. Vasc. 22, 1002–1007 (2002).
[CrossRef]

Atherosclerosis (1)

J. Park, P. Pande, S. Shrestha, F. Clubb, B. E. Applegate, J. A. Jo, “Biochemical characterization of atherosclerotic plaques by endogenous multispectral fluorescence lifetime imaging microscopy,” Atherosclerosis 220, 394–401 (2011).
[CrossRef] [PubMed]

Automatica (1)

J. Rissanen, “Modeling by shortest data description,” Automatica 14, 465–471 (1978).
[CrossRef]

Biophys. J. (2)

J. G. Gert-JanKremers, Erik B. van Munster, J. Theodorus, W. J. Gadella, “Quantitative lifetime unmixing of multiexponentially decaying fluorophores using single-frequency fluorescence lifetime imaging microscopy,” Biophys. J. 95, 378–389 (2008).
[CrossRef]

B. K. C. Lee, J. Siegel, S. E. D. Webb, L. S. Fort, M. J. Cole, R. Jones, K. Dowling, M. J. Lever, P. M. W. French, “Application of the Stretched Exponential Function to Fluorescence Lifetime Imaging,” Biophys. J. 81, 1265–1274 (2001).
[CrossRef] [PubMed]

Br. J. Dermatol. (1)

N. Galletly, J. McGinty, C. Dunsby, F. Teixeira, J. Requejo-Isidro, I. Munro, D. Elson, M. Neil, A. Chu, P. French, G. Stamp, “Fluorescence lifetime imaging distinguishes basal cell carcinoma from surrounding uninvolved skin,” Br. J. Dermatol. 159, 152–161 (2008).
[CrossRef] [PubMed]

Gastrointest. endosc. (1)

K. S. M. Mycek, N. Nishioka, “Colonic polyp differentiation using time-resolved autofluorescence spectroscopy,” Gastrointest. endosc. 48, 390–394 (1998).
[CrossRef] [PubMed]

IEEE J. Biomed. Health Inform. (1)

O. Gutierrez-Navarro, D. Campos-Delgado, E. Arce-Santana, M. Mendez, J. Jo, “Blind end-member and abundance extraction for multi-spectral fluorescence lifetime imaging microscopy data,” IEEE J. Biomed. Health Inform. 18, 606–617 (2014).
[CrossRef] [PubMed]

IEEE J. Sel. Topics Appl. Earth Observ. (3)

J. Bioucas-Dias, A. Plaza, N. Dobigeon, M. Parente, Q. Du, P. Gader, J. Chanussot, “Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches,” IEEE J. Sel. Topics Appl. Earth Observ. 5, 354–379 (2012).
[CrossRef]

R. Heylen, P. Scheunders, “Hyperspectral intrinsic dimensionality estimation with nearest-neighbor distance ratios,” IEEE J. Sel. Topics Appl. Earth Observ. 6, 570–579 (2013).
[CrossRef]

C. Andreou, V. Karathanassi, “Estimation of the number of endmembers using robust outlier detection method,” IEEE J. Sel. Topics Appl. Earth Observ. 7, 247–256 (2014).
[CrossRef]

IEEE Signal Process. Mag. (1)

D. Manolakis, G. Shaw, “Detection algorithms for hyperspectral imaging applications,” IEEE Signal Process. Mag. 19, 29–43 (2002).
[CrossRef]

IEEE Trans. Automat. Contr. (1)

H. Akaike, “A new look at the statistical model identification,” IEEE Trans. Automat. Contr. 19, 716–723 (1974).
[CrossRef]

IEEE Trans. Biomed. Eng. (3)

J. A. Jo, B. Applegate, J. Park, S. Shrestha, P. Pande, I. Gimenez-Conti, J. Brandon, “In vivo simultaneous morphological and biochemical optical imaging of oral epithelial cancer,” IEEE Trans. Biomed. Eng. 57, 2596–2599 (2010).
[CrossRef] [PubMed]

O. Gutierrez-Navarro, D. Campos-Delgado, E. Arce-Santana, M. Mendez, J. Jo, “A fully constrained optimization method for time-resolved multi-spectral fluorescence lifetime imaging microscopy data unmixing,” IEEE Trans. Biomed. Eng. 60, 1711–1720 (2013).
[CrossRef] [PubMed]

P. Pande, J. A. Jo, “Automated analysis of fluorescence lifetime imaging microscopy (flim) data based on the Laguerre deconvolution method,” IEEE Trans. Biomed. Eng. 58, 172–181 (2011).
[CrossRef]

IEEE Trans. Geosci. Remote Sens. (4)

M. Craig, “Minimum-volume transforms for remotely sensed data,” IEEE Trans. Geosci. Remote Sens. 32, 542–552 (1994).
[CrossRef]

C.-I. Chang, Q. Du, “Estimation of number of spectrally distinct signal sources in hyperspectral imagery,” IEEE Trans. Geosci. Remote Sens. 42, 608–619 (2004).
[CrossRef]

J. Bioucas-Dias, J. Nascimento, “Hyperspectral subspace identification,” IEEE Trans. Geosci. Remote Sens. 46, 2435–2445 (2008).
[CrossRef]

A. Ambikapathi, T.-H. Chan, C.-Y. Chi, K. Keizer, “Hyperspectral data geometry-based estimation of number of endmembers using p -norm-based pure pixel identification algorithm,” IEEE Trans. Geosci. Remote Sens. 51, 2753–2769 (2013).
[CrossRef]

J. Biomed. Opt. (3)

H. Xu, B. W. Rice, “In-vivo fluorescence imaging with a multivariate curve resolution spectral unmixing technique,” J. Biomed. Opt. 14, 064011 (2009).
[CrossRef]

L. Marcu, “Fluorescence lifetime in cardiovascular diagnostics,” J. Biomed. Opt. 15, 011106 (2010).
[CrossRef] [PubMed]

J. M. Jabbour, S. Cheng, B. H. Malik, R. Cuenca, J. A. Jo, J. Wright, Y.-S. L. Cheng, K. C. Maitland, “Fluorescence lifetime imaging and reflectance confocal microscopy for multiscale imaging of oral precancer,” J. Biomed. Opt. 18, 046012 (2013).
[CrossRef] [PubMed]

Molecular Biology (1)

T. Zimmermann, “Spectral imaging and linear unmixing in light microscopy,” Molecular Biology 95, 245–265 (2005).

Psychometrika (1)

F. W. Young, J. de Leeuw, Y. Takane, “Regression with qualitative and quantitative variables: An alternating least squares method with optimal features,” Psychometrika 41, 505–526 (1976).
[CrossRef]

Other (8)

B. C. Levy, Principles of Signal Detection and Parameter Estimation (Springer Publishing Company, 2008).
[CrossRef]

Robert M. Clegg, A. Periasamy, FLIM Microscopy in Biology and Medicine (Chapman and Hall/CRC, 2009).
[CrossRef]

P. Vallotton, A. Phatak, M. Berman, “Spectral Imaging and Unmixing,” in Fluorescence Applications in Biotechnology and Life Sciences, E. M. Goldys, ed. (Wiley-Blackwell, 2009).

S. Boyd, L. Vandenberghe, Convex Optimization (Cambridge University Press, New York, 2004).
[CrossRef]

J. Nocedal, S. J. Wright, Numerical Optimization (Springer, 2000).

S. S. Vogel, P. S. Blank, S. V. Koushik, C. Thaler, “Spectral imaging and its use in the measurement of Förster resonance energy transfer in living cells,” in Fret and Flim Techniques. Laboratory Techniques in Biochemistry and Molecular Biology, T. Gadella, ed. 33, 351–394, (Elsevier, 2009).
[CrossRef]

J. Harsanyi, W. Farrand, C.-I. Chang, “Determining the number and identity of spectral endmembers: An integrated approach using Neyman-Pearson eigenthresholding and iterative constrained rms error minimization,” presented at the 9th Thematic Conference on Geological Remote Sensing, Pasadena, California, USA (1993).

J. R. Lakowicz, Principles of Fluorescence Spectroscopy (Springer, 2006).
[CrossRef]

Cited By

OSA participates in CrossRef's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (5)

Fig. 1
Fig. 1

The 5 synthetic end-members (a) and their abundance maps (b), with purity level ρ = 1, employed in the synthetic evaluation. The end-members of 510 samples represent the concatenated fluorescence intensity time-decays at three different wavelength bands. The x and y positions (75 × 75) in (b) correspond to spatial coordinates in the sample, while the color depicts the quantitative abundance values.

Fig. 2
Fig. 2

End-members extracted from the m-FLIM datasets of hamster pouches. The second end-member presents practically no emission fluorescence signal on the first two bands. The end-members were identified as Collagen, Porphyrin and NADH/FAD according to the lifetimes calculated in Table 2.

Fig. 3
Fig. 3

Abundance maps (400 × 400) corresponding to the end-members in Fig. 2 detected in the m-FLIM datasets of hamster pouches. The abundance maps shown in row (a) correspond to the quantitative description obtained from the healthy hamster pouch. Meanwhile, the diseased tissue produced the abundance maps shown in row (b).

Fig. 4
Fig. 4

End-members extracted from the m-FLIM datasets of atherosclerotic plaques, which were identified as Collagen, Elastin and LDL.

Fig. 5
Fig. 5

Abundance maps (60 × 60) for the m-FLIM datasets of atherosclerotic plaques, where each row correspond to a quantitative description. The m-FLIM datasets were identified through histopathology analysis as LCL, HC, HL and Mix, as indicated in the picture.

Tables (4)

Tables Icon

Algorithm 1 Estimation of the Number of Components and Linear Unmixing of m-FLIM Data.

Tables Icon

Table 1 Average number of end-members estimated ± the standard deviation from 50 simulations for each experiment using different PSNR levels. The real number of end-members present in the mixture samples is 5.

Tables Icon

Table 2 Average lifetimes and normalized intensities of the end-members extracted from the m-FLIM datasets of hamster pouches. The 2nd end-member presented no response in the first and second wavelength band.

Tables Icon

Table 3 Average Lifetime and Normalized Intensities of the end-members extracted from m-FLIM datasets of atherosclerotic plaques.

Equations (35)

Equations on this page are rendered with MathJax. Learn more.

y k = j = 1 N p j α k , j k [ 1 , K ]
Y = P N 𝒜 N
1 N α k = 1 k [ 1 , K ]
α k 0 ,
Ω p N conv { p 1 , , p N } = { y L | y = j = 1 N p j ω n s . t . j = 1 N ω j = 1 , ω j 0 j [ 1 , N ] }
1 L p j = 1 , j [ 1 , N ]
p j 0 .
𝒫 N ^ { p 1 , , p N ^ }
p ^ j = P j β j
β j = arg min β p j P j β 2 2
p j = p j o + w j j [ 1 , N ^ ] ,
e j = p j P j β j L ,
= [ p j o P j o β j μ j ] + [ w j W j o β j v j ] ,
κ = 1 + i = 1 N ^ 1 β j , i 2 .
e j ~ 𝒩 ( 0 , κ Σ ) .
e j ~ 𝒩 ( μ j , κ Σ ) .
r j I = e j ( κ Σ ) 1 e j 0 j [ 1 , N ^ ]
H 0 : r j I ~ χ 2 ( L 1 )
H 1 : r j I ~ χ 2 ( L 1 , μ j )
P F A I = η j f χ ( x , L 1 ) d x
Decide H 0 I if r j I < η j Decide H 1 I if r j I > η j
Decide H 0 I if r j f χ ( x , L 1 ) d x > P F A I Decide H 1 I if r j f χ ( x , L 1 ) d x < P F A I
d j I = r j I ( L 1 ) 2 ( L 1 ) ~ 𝒩 ( 0 , 1 ) .
Decide H 0 I if 1 2 π d j I e x 2 / 2 d x > P F A I Decide H 1 I if 1 2 π d j I e x 2 / 2 d x < P F A I
s j , i = { 1 if h j , i θ 0 if h j , i < θ i [ 1 , B ]
r j I I = b s j 1 B s j j [ 1 , N ^ ] .
r j I I ~ 𝒰 ( 0 , ω 1 )
r j I I ~ 𝒰 ( 0 , ω 2 )
Decide H 0 I I if r j I I f 𝒰 [ 0 , ω 1 ] ( x ) d x > P F A I I Decide H 1 I I if r j I I f 𝒰 [ 0 , ω 1 ] ( x ) d x < P F A I I
min P N , 𝒜 N L ( P N , 𝒜 N ) = min P N , 𝒜 N 1 2 Y P N 𝒜 N F 2 + ξ i = 1 N 1 j = i + 1 N p i p j 2 2
Y = P 5 𝒜 5 +
PSNR = 10 log 10 ( max i [ 1 , L ] y k , i σ 2 ) .
ρ = max k [ 1 , K ] α k , j 2
τ j λ i = t p ^ j λ i 1 L p ^ j λ i i , j [ 1 , 3 ]
I j λ i = 1 L p ^ j λ i i = 1 3 1 L p ^ j λ i i , j [ 1 , 3 ]

Metrics