Abstract

An inexpensive method to convert a microscope into an imaging spectrometer is presented. Unlike current microscope-based spectrometers which use specialized optics or scanning mechanisms, our system only requires at most two image captures with a 3-chip CCD camera and a lightly-tinted color filter to output the color signal of a sample at each pixel. Basis spectra are obtained by principal components analysis applied to an ensemble of color signals of commercially-available dyes observed with different dichroic mirrors. A transformation matrix from channel values to spectral coefficients is derived. Minimum negativity constraint is applied to eliminate negative parts of the reconstructed fluorescence spectrum. The technique is demonstrated on fluorescence microspheres (fluorospheres) and chlorophyll from plant leaf.

©2002 Optical Society of America

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References

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  1. S. Kawata, K. Sasaki, and S. Minami’ “Component Analysis of Spatial and Spectral Patterns in Multispectral Images,” J Opt Soc Am A. 4, 2101–2106 (1987).
    [Crossref] [PubMed]
  2. E. Schrock et al, “Multicolor Spectral Karyotyping of Human Chromosomes,” Science 273, 494–497 (1996).
    [Crossref] [PubMed]
  3. D. Youvan et al. “Fluorescence Imaging Micro-Spectrophotometer (FIMS),” Biotechnology et alia,  1, 1–16, (1997).
  4. B. Ford, M. R. Descour, and R. M. Lynch, “Large-image-format computed tomography imaging spectrometer for fluorescence microscopy,” Opt. Express 9, 444–453 (2001). http://www.opticsexpress.org/abstract.cfm?URI=OPEX-9-9-444
    [Crossref] [PubMed]
  5. F. Imai and R. Berns. “Spectral Estimation Using Trichromatic Digital Cameras,” .Proc. of the International Symposium on Multispectral Imaging and Color Reproduction for Digital Archives, 42–49, 1999.
  6. P. Herring, “The Spectral Characteristics of Luminous Marine Organisms,” Proc Roy Soc Lond B220, 183–217 (1993).
  7. R. Haugland, “Handbook of Fluorescent Probes and Research Chemicals, 6th ed.,” Molecular Probes, Inc., 1996.

2001 (1)

1997 (1)

D. Youvan et al. “Fluorescence Imaging Micro-Spectrophotometer (FIMS),” Biotechnology et alia,  1, 1–16, (1997).

1996 (1)

E. Schrock et al, “Multicolor Spectral Karyotyping of Human Chromosomes,” Science 273, 494–497 (1996).
[Crossref] [PubMed]

1993 (1)

P. Herring, “The Spectral Characteristics of Luminous Marine Organisms,” Proc Roy Soc Lond B220, 183–217 (1993).

1987 (1)

S. Kawata, K. Sasaki, and S. Minami’ “Component Analysis of Spatial and Spectral Patterns in Multispectral Images,” J Opt Soc Am A. 4, 2101–2106 (1987).
[Crossref] [PubMed]

Berns, R.

F. Imai and R. Berns. “Spectral Estimation Using Trichromatic Digital Cameras,” .Proc. of the International Symposium on Multispectral Imaging and Color Reproduction for Digital Archives, 42–49, 1999.

Descour, M. R.

Ford, B.

Haugland, R.

R. Haugland, “Handbook of Fluorescent Probes and Research Chemicals, 6th ed.,” Molecular Probes, Inc., 1996.

Herring, P.

P. Herring, “The Spectral Characteristics of Luminous Marine Organisms,” Proc Roy Soc Lond B220, 183–217 (1993).

Imai, F.

F. Imai and R. Berns. “Spectral Estimation Using Trichromatic Digital Cameras,” .Proc. of the International Symposium on Multispectral Imaging and Color Reproduction for Digital Archives, 42–49, 1999.

Kawata, S.

S. Kawata, K. Sasaki, and S. Minami’ “Component Analysis of Spatial and Spectral Patterns in Multispectral Images,” J Opt Soc Am A. 4, 2101–2106 (1987).
[Crossref] [PubMed]

Lynch, R. M.

Minami’, S.

S. Kawata, K. Sasaki, and S. Minami’ “Component Analysis of Spatial and Spectral Patterns in Multispectral Images,” J Opt Soc Am A. 4, 2101–2106 (1987).
[Crossref] [PubMed]

Sasaki, K.

S. Kawata, K. Sasaki, and S. Minami’ “Component Analysis of Spatial and Spectral Patterns in Multispectral Images,” J Opt Soc Am A. 4, 2101–2106 (1987).
[Crossref] [PubMed]

Schrock, E.

E. Schrock et al, “Multicolor Spectral Karyotyping of Human Chromosomes,” Science 273, 494–497 (1996).
[Crossref] [PubMed]

Youvan, D.

D. Youvan et al. “Fluorescence Imaging Micro-Spectrophotometer (FIMS),” Biotechnology et alia,  1, 1–16, (1997).

Biotechnology et alia (1)

D. Youvan et al. “Fluorescence Imaging Micro-Spectrophotometer (FIMS),” Biotechnology et alia,  1, 1–16, (1997).

J Opt Soc Am A. (1)

S. Kawata, K. Sasaki, and S. Minami’ “Component Analysis of Spatial and Spectral Patterns in Multispectral Images,” J Opt Soc Am A. 4, 2101–2106 (1987).
[Crossref] [PubMed]

Opt. Express (1)

Proc Roy Soc Lond (1)

P. Herring, “The Spectral Characteristics of Luminous Marine Organisms,” Proc Roy Soc Lond B220, 183–217 (1993).

Science (1)

E. Schrock et al, “Multicolor Spectral Karyotyping of Human Chromosomes,” Science 273, 494–497 (1996).
[Crossref] [PubMed]

Other (2)

F. Imai and R. Berns. “Spectral Estimation Using Trichromatic Digital Cameras,” .Proc. of the International Symposium on Multispectral Imaging and Color Reproduction for Digital Archives, 42–49, 1999.

R. Haugland, “Handbook of Fluorescent Probes and Research Chemicals, 6th ed.,” Molecular Probes, Inc., 1996.

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Figures (3)

Fig. 1.
Fig. 1. Experimental setup for obtaining fluorescence color signal from image color. A microsocope is fitted with a 3CCD camera with a lightly-tinted color filter. Camera output is digitized by a computer.

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Fi.g 2.
Fi.g 2. Fluorescence color signal reconstruction of (a) Chlorophyll b; (b) fluorospheres. In images on the right column box indicates position where spectrum is recovered. Graphs on the left column are actual spectra (black), reconstructed spectra using 5 coefficients (blue), improved spectra using 10 coefficients recovered using minimum negativity constraint (red).

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Fig. 3.
Fig. 3. Quality measures. (a) Negative values in Crec (b) μ and (c) NMSE vs number of recovered coefficients for leaf chlorophyll, (squares) and fluorospheres (circles).

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Equations (14)

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C ( λ ) = E ( λ ) F eff ( λ )
F eff ( λ ) = F DM ( λ ) F B ( λ ) .
Q m = ∫ C ( λ ) S m ( λ ) dλ ,
C ˜ ( λ ) = ∑ i N a i e i ( λ ) + C mean ( λ )
a i = ∑ λ ( C ( λ ) − C mean ( λ ) ) e i ( λ ) .
[ Q 1 Q 2 ⋮ Q M ] = [ ∫ e 1 S 1 dλ ∫ e 2 S 1 dλ … ∫ e M S 1 dλ ∫ e 1 S 2 dλ ∫ e 2 S 2 dλ … ∫ e M S 2 dλ ⋮ ⋮ … ⋮ ∫ e 1 S η dλ ∫ e 2 S η dλ … ∫ e N S M dλ ] [ a 1 a 2 ⋮ a N ] + [ ∫ C mean S 1 dλ ∫ C mean S 2 dλ ⋮ ∫ C mean S η dλ ]
T = [ ∫ e 1 S 1 dλ ∫ e 2 S 1 dλ … ∫ e N S 1 dλ ∫ e 1 S 2 dλ ∫ e 2 S 2 dλ … ∫ e N S 2 dλ ⋮ ⋮ … ⋮ ∫ e 1 S η dλ ∫ e 2 S M dλ … ∫ e N S M dλ ] .
a = T − 1 ( Q − Q mean )
f = ∑ λ { H ( λ ) [ C ˜ ( λ ) + ∑ i = 1 m α i e i ( λ ) ] } 2
H ( λ k ) = { 0 if C ˜ ( λ k ) ≥ 0 1 if C ˜ ( λ k ) < 0 .
∂ ∂ α j ∑ λ { H ( λ ) [ C ˜ ( λ ) + ∑ i = 1 m α i e i ( λ ) ] } 2 = 0
[ e 1 · e 1 e 2 · e 1 ⋯ e n · e 1 e 1 · e 2 e 2 · e 2 ⋯ e n · e 2 ⋮ ⋮ e 1 · e n e 2 · e n ⋯ e n · e n ] [ α 1 α 2 ⋮ α n ] = [ − C ˜ · e 1 − C ˜ · e 2 ⋮ − C ˜ · e n ]
α = B − 1 M .
C rec ( λ ) = C ˜ + ∑ i = 1 n α i e i ( λ ) .

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