Statistical and structural wavelet packet features for pit pattern classification in zoom-endoscopic colon images

Authors:
Michael Liedlgruber;Andreas Uhl
Affiliations:
University of Salzburg, Department of Computer Sciences, Salzburg, Austria;University of Salzburg, Department of Computer Sciences, Salzburg, Austria
Venue:
WAMUS'07 Proceedings of the 7th WSEAS international conference on Wavelet analysis & multirate systems
Year:
2007

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Abstract

We discuss features extracted from a wavelet packet decomposition for image classification. Statistical features computed from wavelet packet coefficients are compared to structural features which are derived from an image dependent wavelet packet decomposition subband structure. Primary application area is the classification of pit pattern structures in zoom-endoscopic colon imagery, while results are also compared to the outcome of a classical texture classification application.