Feature extraction from multi-directional multi-resolution image transformations for the classification of zoom-endoscopy images

  • Michael Häfner
  • Roland KwittEmail author
  • Andreas Uhl
  • Alfred Gangl
  • Friedrich Wrba
  • Andreas Vécsei
Theoretical Advances


In this article, we discuss the discriminative power of a set of image features, extracted from detail subbands of the Gabor wavelet transform and the dual-tree complex wavelet transform for the purpose of computer-assisted zoom-endoscopy image classification. We incorporate color channel information into the classification process and show that this leads to superior classification results, compared to luminance-channel-only-based image analysis.


Zoom-endoscopy Wavelet transform Classification Texture analysis 



This work is funded by the Austrian Science Fund (FWF) under Project No. L366-N15.


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Copyright information

© Springer-Verlag London Limited 2008

Authors and Affiliations

  • Michael Häfner
    • 1
  • Roland Kwitt
    • 2
    Email author
  • Andreas Uhl
    • 2
  • Alfred Gangl
    • 1
  • Friedrich Wrba
    • 3
  • Andreas Vécsei
    • 4
  1. 1.Department of Clinical PathologyVienna Medical UniversityViennaAustria
  2. 2.Department of Computer SciencesUniversity of SalzburgSalzburgAustria
  3. 3.Department of Gastroenterology and HepatologyVienna Medical UniversityViennaAustria
  4. 4.St. Anna Children’s HospitalViennaAustria

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