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New Characteristics for the Classification of Burns: Experimental Study

  • Irene Fondón
  • Begoña Acha
  • Carmen Serrano
  • Manuel Sosa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4142)

Abstract

In this paper a new approach to the subject of burn image classification is presented. When the physicians determine that a certain burn is superficial or deep, they are, unconsciously, examining some features of the burn difficult to translate into a physical measure by them. To implement a CAD tool, we need to identify these features in order to perform the same operation in an automatic way. To this aim, we have designed an experiment following the Recommendation ITU-R BT. 500-10, in which we ask 8 experts in burn images to assess the similarities of a group of selected images. Afterwards a mathematical analysis, based in the multidimensional scaling (MDS), has let us identify the numerical features that have lead experts to this classification. This suggests a promising way to automatically classify burn images.

Keywords

Multidimensional Scaling Color Blindness Burn Unit Hierarchical Cluster Technique Burnt Skin 
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.

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References

  1. 1.
    Clarke, J.A.: A Colour Atlas of Burn Injuries. Chapman & Hall Medical, London (1992)Google Scholar
  2. 2.
    Roa, L.M., Gómez-Cía, T., Acha, B., Serrano, C.: Digital Imaging in Remote Diagnosis of Burns. Burns 7, 617–624 (1999)CrossRefGoogle Scholar
  3. 3.
    Acha, B., Serrano, C., Acha, J.I., Roa, L.M.: CAD Tool for Burn Diagnosis. In: Taylor, C.J., Noble, J.A. (eds.) IPMI 2003. LNCS, vol. 2732, pp. 294–305. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Mojsilovic, A., Kovǎcević, J., Hu, J., Safranek, R. J., Kicha Ganapathy, S.: Matching and Retrieval Based on the Vocabulary and Grammar of Color Patterns. IEEE Transactions on Image Processing 9(1), 38–54 (2000)Google Scholar
  5. 5.
    Mojsilovic, A., Kovǎcević, J., Kall, D., Safranek, R. J., Kicha Ganapathy, S.: The Vocabulary and Grammar of Color Patterns. IEEE Transactions on Image Processsing 9(3), 417–431 (2000) Google Scholar
  6. 6.
    Rec. UIT-R BT.500-10. Methodology for the subjective evaluation of the quality of television images Google Scholar
  7. 7.
    Han, B., Luo, M.R. , Kirchner E.J.J.: Assessing colour differences for automobile coatings using CRT colours. In: Part I: Evaluating Colour Difference of Solid Colours. AIC 2005, pp. 579–582 (2005)Google Scholar
  8. 8.
    Han, B., Luo, M.R. , Kirchner E.J.J.: Assessing colour differences for automobile coat-ings using CRT colours. In: Part II: Evaluating Colour Difference of Textured Colours. AIC 2005, pp. 583-586 (2005)Google Scholar
  9. 9.
    Huertas, R., Melgosa, M., Hita, E.: Parametric factors for colour differences of samples with simulated texture. In: AIC 2005, pp. 587–590 (2005)Google Scholar
  10. 10.
    Kruskal, J.B., Wish, M.: Multidimensional Scaling. Bell Telephone Laboratories, Inc. (1978)Google Scholar
  11. 11.
    Figueras, S., Gallardo, P.: Análisis Exploratorio de Datos (2003) Google Scholar
  12. 12.
    Young, F.W., Hamer, R.M.: Multidimensional Scaling: History, Theory and Applications. Erlbaum, New York (1987)Google Scholar
  13. 13.
    Maulik, U., Bandyopadhyay, S.: Performance evaluation of some clustering algorithms and validity indices. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(12), 1650–1654 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Irene Fondón
    • 1
  • Begoña Acha
    • 1
  • Carmen Serrano
    • 1
  • Manuel Sosa
    • 1
  1. 1.Departamento de Teoría de la Señal y Comunicaciones, Escuela Superior de IngenierosUniversity of SevilleSevilleSpain

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