Utilization of Multi-spectral Images in Photodynamic Diagnosis

  • Andrzej Zacher
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6375)


This paper introduces multi-spectral images for healthy and cancerous parts of human skin. It compares light spectrum calculated from those images with spectrum obtained from simulation. First the mathematical model of tissue and Monte Carlo algorithm of light propagation in turbid media is presented. This theory was then extended to imitate the fluorescence phenomenon, necessary for cancer recognition. Then the processing method of non-normalized multi-spectral images was described. Finally both results were compared to confirm that the assumed model is correct. Having all those information it will be possible to simulate such environment, which applied into reality, would make the cancer diagnosis much faster.


Human Skin Tissue Surface Monte Carlo Algorithm Exit Angle Turbid Medium 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bradford, P.T.: Skin Cancer in Skin of Color. Dermatology nursing 21(4), 170–206 (2009)Google Scholar
  2. 2.
    Yavari, N.: Optical spectroscopy for tissue diagnostics and treatment control. Doctoral Thesis, Department of Physics and Technology University of Bergen (2006)Google Scholar
  3. 3.
    Jacques, S.L.: Light Distributions from Point, Line and Plane Sources for Photochemical Reactions and Fluorescence in Turbid Biological Tissues. Photochemistry and Photobiology 67(1), 23–32 (1998)CrossRefGoogle Scholar
  4. 4.
    Long, R., McShane, M.J.: Modeling of Selective Photon Capture for Collection of Fluorescence Emitted from Dermally-Implanted Microparticle Sensors. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 21(4), pp. 2972–2975 (2007)Google Scholar
  5. 5.
    Zeng, H., MacAulay, C., McLean, D.I., Palcic, B.: Reconstruction of in vivo skin autofluorescence spectrum from microscopic properties by Monte Carlo simulation. Journal of Photochemistry and Photobiology B 38(4), 234–240 (1997)CrossRefGoogle Scholar
  6. 6.
    Sokolov, K., Follen, M., Richards-Kortum, R.: Optical spectroscopy for detection of neoplasia. Current Opinion in Chemical Biology 6(5), 651–658 (2002)CrossRefGoogle Scholar
  7. 7.
    Li, Y., Li, M., Xu, T.: Quantitative Time-Resolved Fluorescence Spectrum of the Cortical Sarcoma and the Adjacent Normal Tissue. Journal of fluorescence 17(6), 643–648 (2007)CrossRefGoogle Scholar
  8. 8.
    Perelman, L.T., Wu, J., Itzkan, I., Feld, M.S.: Photon Migration in Turbid Media Using Path Integrals. Physical Review Letters 72(9), 1341–1344 (1994)CrossRefGoogle Scholar
  9. 9.
    Wang, L., Jacques, S.L.: Monte Carlo Modeling of Light Transport in Multi-layered Tissues in Standard C. University of Texas M. D. Anderson Cancer Center (1992)Google Scholar
  10. 10.
    Pharr, M., Humphereys, G.: Physically based rendering. From theory to implementation. Morgan Kaufmann, San Francisco (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Andrzej Zacher
    • 1
  1. 1.Institute of InformaticsThe Silesian University of TechnologyGliwicePoland

Personalised recommendations