Advertisement

Tissue Characterization Using Dimensionality Reduction and Fluorescence Imaging

  • Karim Lekadir
  • Daniel S. Elson
  • Jose Requejo-Isidro
  • Christopher Dunsby
  • James McGinty
  • Neil Galletly
  • Gordon Stamp
  • Paul M. W. French
  • Guang-Zhong Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)

Abstract

Multidimensional fluorescence imaging is a powerful molecular imaging modality that is emerging as an important tool in the study of biological tissues. Due to the large volume of multi-spectral data associated with the technique, it is often difficult to find the best combination of parameters to maximize the contrast between different tissue types. This paper presents a novel framework for the characterization of tissue compositions based on the use of time resolved fluorescence imaging without the explicit modeling of the decays. The composition is characterized through soft clustering based on manifold embedding for reducing the dimensionality of the datasets and obtaining a consistent differentiation scheme for determining intrinsic constituents of the tissue. The proposed technique has the benefit of being fully automatic, which could have significant advantages for automated histopathology and increasing the speed of intraoperative decisions. Validation of the technique is carried out with both phantom data and tissue samples of the human pancreas.

Keywords

Geodesic Distance Tissue Characterization Human Pancreas Fluorescence Lifetime Imaging Microscopy Pancreas Tissue 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Bastiaens, P.I.H., Squire, A.: Fluorescence lifetime imaging microscopy: spatial resolution of biochemical processes in the cell. Trends in Cell Biology 9, 48–52 (1999)CrossRefGoogle Scholar
  2. 2.
    Mizeret, J., Wagnieres, G., Stepinac, T., Bergh, H.V.D.: Endoscopic tissue characterization by frequency-domain fluorescence lifetime imaging (FD-FLIM). Lasers in Medical Science 12, 209–217 (1997)CrossRefGoogle Scholar
  3. 3.
    Cubeddu, R., Comelli, D., D’Andrea, C., Taroni, P., Valentini, G.: Time-resolved fluorescence imaging in biology and medicine. Journal of Physics D: Applied Physics 35, R61–R76 (2002)Google Scholar
  4. 4.
    Lee, K.C.B., Siegel, J., Webb, S.E.D., Leveque-Fort, S., Cole, M.J., Jones, R., Dowling, K., Lever, M.J., French, P.M.W.: Application of the stretched exponential function to fluorescence lifetime imaging. Biophysical Journal 81, 1265–1274 (2001)CrossRefGoogle Scholar
  5. 5.
    Jo, J.A., Fang, Q.Y., Papaioannou, T., Marcu, L.: Fast model-free deconvolution of fluorescence decay for analysis of biological systems. Journal of Biomedical Optics 9, 743–752 (2004)CrossRefGoogle Scholar
  6. 6.
    Tenenbaum, J.B., Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319–2323 (2000)CrossRefGoogle Scholar
  7. 7.
    Cox, T.F., Cox, M.A.A.: Multidimensional scaling. Chapman & Hall, London (1994)MATHGoogle Scholar
  8. 8.
    Sammon, J.W.: A nonlinear mapping algorithm for data structure analysis. IEEE Transactions Computers 18, 401–409 (1969)CrossRefGoogle Scholar
  9. 9.
    McConnell, G.: Confocal laser scanning fluorescence microscopy with a visible continuum source. Optics Express 12, 2844–2850 (2004)CrossRefGoogle Scholar
  10. 10.
    Siegel, J., Elson, D.S., Webb, S.E.D., Lee, K.C.B., Vlanclas, A., Gambaruto, G.L., Leveque-Fort, S., Lever, M.J., Tadrous, P.J., Stamp, G.W.H., Wallace, A.L., Sandison, A., Watson, T.F., Alvarez, F., French, P.M.W.: Studying biological tissue with fluorescence lifetime imaging: microscopy, endoscopy, and complex decay profiles. Journal/Applied Optics 42, 2995–3004 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Karim Lekadir
    • 1
  • Daniel S. Elson
    • 2
  • Jose Requejo-Isidro
    • 2
  • Christopher Dunsby
    • 2
  • James McGinty
    • 2
  • Neil Galletly
    • 3
  • Gordon Stamp
    • 3
  • Paul M. W. French
    • 2
  • Guang-Zhong Yang
    • 1
  1. 1.Visual Information Processing Group, Department of Computing 
  2. 2.Department of Physics 
  3. 3.Division of Investigative SciencesImperial College LondonUnited Kingdom

Personalised recommendations