Selection/Extraction of Spectral Regions for Autofluorescence Spectra Measured in the Oral Cavity

  • Marina Skurichina
  • Pavel Paclík
  • Robert P. W. Duin
  • Diana de Veld
  • Henricus J. C. M. Sterenborg
  • Max J. H. Witjes
  • Jan L. N. Roodenburg
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3138)

Abstract

Recently a number of successful algorithms to select/extract discriminative spectral regions was introduced. These methods may be more beneficial than the standard feature selection/extraction methods for spectral classification. In this paper, on the example of autofluorescence spectra measured in the oral cavity, we intend to get deeper understanding what might be the best way to select informative spectral regions and what factors may influence the success of this approach.

Keywords

Spectral Region Linear Discriminant Analysis Spectral Band Mahalanobis Distance Linear Classifier 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Marina Skurichina
    • 1
  • Pavel Paclík
    • 1
  • Robert P. W. Duin
    • 1
  • Diana de Veld
    • 2
  • Henricus J. C. M. Sterenborg
    • 2
  • Max J. H. Witjes
    • 3
  • Jan L. N. Roodenburg
    • 3
  1. 1.Information and Communication Theory Group, Faculty of Electrical Engineering, Mathematics and Computer ScienceDelft University of TechnologyDelftThe Netherlands
  2. 2.Department of Radiation Oncology, Erasmus MCPhotodynamic Therapy and Optical Spectroscopy ProgrammeRotterdamThe Netherlands
  3. 3.Department of Oral and Maxillofacial SurgeryUniversity Hospital GroningenThe Netherlands

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