Abstract

Efficient image classification of microscopic fluorescent spheres is demonstrated with a supervised backpropagation neural network (NN) that uses as inputs the major color histogram representation of the fluorescent image to be classified. Two techniques are tested for the major color search: (1) cluster mean (CM) and (2) Kohonen’s self-organizing feature map (SOFM). The method is shown to have higher recognition rates than Swain and Ballard’s Color Indexing by histogram intersection. Classification with SOFM-generated histograms as inputs to the classifier NN achieved the best recognition rate (90%) for cases of normal, scaled, defocused, photobleached, and combined images of AMCA (7-Amino-4-Methylcoumarin-3-Acetic Acid) and FITC (Fluorescein Isothiocynate)-stained microspheres.

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References

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  1. S. Andersson-Engels, J. Johansson, and S. Svanderg, "Multicolor fluorescence imaging systems for tissue diagnostics," Bioimaging Two-Dimensional Spectroscopy, Proc. SPIE 1205, 179-189 (1990).
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    [CrossRef] [PubMed]
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    [CrossRef] [PubMed]
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  5. C. Saloma, C. Palmes-Saloma, and H. Kondoh, "Site-specific confocal fluorescence imaging of biological microstructures in a turbid medium," Phys Med Bio 43, 1741-1759 (1998).
    [CrossRef]
  6. C. Palmes-Saloma and C. Saloma, "Long-depth imaging of specific gene expressions in wholemount mouse embryos with single photon excitation confocal fluorescence microscope and FISH," J. Structural Biology 131, 56-66 (2000).
    [CrossRef]
  7. M. Swain and D. Ballard, "Color indexing," International J. Computer Vision 7, 11-32 (1991).
    [CrossRef]
  8. B. Funt and G. Finlayson, "Color constant color indexing," IEEE Trans Pattern Analysis Machine Intelligence 17, 522-529 (1995).
    [CrossRef]
  9. D. Slater and G. Healey, "Global color constancy: recognition of objects by use of illumination-invariant properties of color distributions," J. Opt. Soc. Am. A 11, 3003-3010 (1994).
    [CrossRef]
  10. P. Ennesser and G. Medioni, "Finding Waldo, or focus of attention using local color information," IEEE Trans. Pattern Analysis and Machine Intelligence 17, 805-809 (1993).
    [CrossRef]
  11. B. Mel, "SEEMORE: Combining color, shape, and texture histogramming in a neurally inspired approach to visual object recognition," Neural Computation 9, 777-804 (1997).
    [CrossRef] [PubMed]
  12. J. Lampinen and S. Smolander, "Self-organizing feature extraction in recognition of wood surface defects and color images," Intl. J. Pattern Recognition and Artificial Intelligence, 10, 97-113 (1996).
    [CrossRef]
  13. C. Faloutsos, W. Equitz, M. Flickner et al., "Efficient and effective querying by image content," J. Intelligent Information Systems 3, 231-262 (1994).
    [CrossRef]
  14. B.-L. Yeo and B. Liu, "Rapid scene analysis on compressed video," IEEE Trans. Circuits and Systems for Video Technology 5, 533-544 (1995).
    [CrossRef]
  15. S. Inoue, Video Microscopy, (Plenum Press, New York 1986).
  16. B. Herman, Fluorescence Microscopy 2nd Ed. (Springer-Verlag, Singapore 1998).
  17. M. Soriano and C. Saloma, "Improved classification robustness for noisy cell images represented as principal-component projections in a hybrid recognition system," Appl. Opt. 37, 3628-3838 (1998).
    [CrossRef]

Other (17)

S. Andersson-Engels, J. Johansson, and S. Svanderg, "Multicolor fluorescence imaging systems for tissue diagnostics," Bioimaging Two-Dimensional Spectroscopy, Proc. SPIE 1205, 179-189 (1990).

M.R. Speicher, S. Gwyn Ballard, and D. Ward, "Karyotyping human chromosomes by combinatorial multi-fluor FISH," Nature Genetics 12, 368-375 (1996).
[CrossRef] [PubMed]

E. Schrock, S. du Manoir, T. Veldman, B. Schoell, J. Weinberg, M.A. Ferguson-Smith, Y. Ning, D.H. Ledbetter, I. Bar-Am, D. Soenksen, Y. Garini, T. Reid, "Multicolor spectral karyotyping of human chromosomes," Science 273, 494-498 (1996).
[CrossRef] [PubMed]

S. Abrams, "Fluorescent Markers: GFP Joins the Common Dyes," Biophotonics International 5, pp 48-54 (March/April 1998).

C. Saloma, C. Palmes-Saloma, and H. Kondoh, "Site-specific confocal fluorescence imaging of biological microstructures in a turbid medium," Phys Med Bio 43, 1741-1759 (1998).
[CrossRef]

C. Palmes-Saloma and C. Saloma, "Long-depth imaging of specific gene expressions in wholemount mouse embryos with single photon excitation confocal fluorescence microscope and FISH," J. Structural Biology 131, 56-66 (2000).
[CrossRef]

M. Swain and D. Ballard, "Color indexing," International J. Computer Vision 7, 11-32 (1991).
[CrossRef]

B. Funt and G. Finlayson, "Color constant color indexing," IEEE Trans Pattern Analysis Machine Intelligence 17, 522-529 (1995).
[CrossRef]

D. Slater and G. Healey, "Global color constancy: recognition of objects by use of illumination-invariant properties of color distributions," J. Opt. Soc. Am. A 11, 3003-3010 (1994).
[CrossRef]

P. Ennesser and G. Medioni, "Finding Waldo, or focus of attention using local color information," IEEE Trans. Pattern Analysis and Machine Intelligence 17, 805-809 (1993).
[CrossRef]

B. Mel, "SEEMORE: Combining color, shape, and texture histogramming in a neurally inspired approach to visual object recognition," Neural Computation 9, 777-804 (1997).
[CrossRef] [PubMed]

J. Lampinen and S. Smolander, "Self-organizing feature extraction in recognition of wood surface defects and color images," Intl. J. Pattern Recognition and Artificial Intelligence, 10, 97-113 (1996).
[CrossRef]

C. Faloutsos, W. Equitz, M. Flickner et al., "Efficient and effective querying by image content," J. Intelligent Information Systems 3, 231-262 (1994).
[CrossRef]

B.-L. Yeo and B. Liu, "Rapid scene analysis on compressed video," IEEE Trans. Circuits and Systems for Video Technology 5, 533-544 (1995).
[CrossRef]

S. Inoue, Video Microscopy, (Plenum Press, New York 1986).

B. Herman, Fluorescence Microscopy 2nd Ed. (Springer-Verlag, Singapore 1998).

M. Soriano and C. Saloma, "Improved classification robustness for noisy cell images represented as principal-component projections in a hybrid recognition system," Appl. Opt. 37, 3628-3838 (1998).
[CrossRef]

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Figures (2)

Figure 1.
Figure 1.

Fluorescent microsphere image classes used in recognition experiment. From left to right: R (FITC-red), G (FITC-green), B (AMCA-blue), Bk (background), Gb (FITC-green+AMCA-blue), Rg (FITC-red+FITC-green), Rb (FITC-red+FITC-blue), Rgb (FITC-red+FITC-green+AMCA-blue).

Figure 2.
Figure 2.

Histogram models in rg-space of the eight fluorescent image classes. From left to right, R, G, B, Bk, Gb, Rg, Rb, Rgb. Frequency of pixel occurrence is shown in a rainbow color map with red as the highest and blue the lowest.

Tables (6)

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Table 1. RGB components of the major colors computed using Cluster Mean.

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Table 2. RGB components of the major colors computed using SOFM

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Table 3. Confusion matrix and recognition rates (%RR) for Color Indexing(CI).

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Table 4. Confusion matrix and recognition rates (%RR) for Cluster Mean+Neural Network (CM+NN).

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Table 5. Confusion matrix and recognition rates (%RR) for SOFM+Neural Network (SOFM+NN).

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Table 6. Summary of recognition rates (%RR) for defocused, photobleached and scaled or magnified images.

Equations (3)

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D = b = 1 B min ( S b , M b ) b = 1 B M b
i ( x ) = arg j min x ( n ) w j
Δ w ( q + 1 ) = k ( dE q / d w ji ) + α Δ w ( q )

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