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

Authors:
Michael Häfner;Roland Kwitt;Andreas Uhl;Alfred Gangl;Friedrich Wrba;Andreas Vécsei
Affiliations:
Vienna Medical University, Department of Clinical Pathology, 1090, Vienna, Austria;University of Salzburg, Department of Computer Sciences, 5020, Salzburg, Austria;University of Salzburg, Department of Computer Sciences, 5020, Salzburg, Austria;Vienna Medical University, Department of Clinical Pathology, 1090, Vienna, Austria;Vienna Medical University, Department of Gastroenterology and Hepatology, 1090, Vienna, Austria;St. Anna Children’s Hospital, 1090, Vienna, Austria
Venue:
Pattern Analysis & Applications
Year:
2009

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Abstract

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.