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Improving Pit–Pattern Classification of Endoscopy Images by a Combination of Experts

  • Michael Häfner
  • Alfred Gangl
  • Roland Kwitt
  • Andreas Uhl
  • Andreas Vécsei
  • Friedrich Wrba
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5761)

Abstract

The diagnosis of colorectal cancer is usually supported by a staging system, such as the Duke or TNM system. In this work we discuss computer–aided pit–pattern classification of surface structures observed during high–magnification colonoscopy in order to support dignity assessment of colonic polyps. This is considered a quite promising approach because it allows in vivo staging of colorectal lesions. Since recent research work has shown that the characteristic surface structures of the colon mucosa exhibit texture characteristics, we employ a set of texture image features in the wavelet-domain and propose a novel classifier combination approach which is similar to a combination of experts. The experimental results of our work show superior classification performance compared to previous approaches on both a two-class (non-neoplastic vs. neoplastic) and a more complicated six-class (pit–pattern) classification problem.

Keywords

Feature Subset Endoscopy Image Colorectal Lesion Feature Extraction Approach Predict Class Label 
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 2009

Authors and Affiliations

  • Michael Häfner
    • 1
  • Alfred Gangl
    • 1
  • Roland Kwitt
    • 2
  • Andreas Uhl
    • 2
  • Andreas Vécsei
    • 4
  • Friedrich Wrba
    • 3
  1. 1.Dept. of Gastroenterology & HepatologyMedical University of ViennaAustria
  2. 2.Dept. of Computer ScienceUniversity of SalzburgAustria
  3. 3.Dept. of Clinical PathologyMedical University of ViennaAustria
  4. 4.St. Anna Children’s HospitalViennaAustria

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