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

Authors:
Michael Häfner;Alfred Gangl;Roland Kwitt;Andreas Uhl;Andreas Vécsei;Friedrich Wrba
Affiliations:
Dept. of Gastroenterology & Hepatology, Medical University of Vienna, Austria;Dept. of Gastroenterology & Hepatology, Medical University of Vienna, Austria;Dept. of Computer Science, University of Salzburg, Austria;Dept. of Computer Science, University of Salzburg, Austria;St. Anna Children's Hospital, Vienna, Austria;Dept. of Clinical Pathology, Medical University of Vienna, Austria
Venue:
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Year:
2009

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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.