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Pit Pattern Classification Using Multichannel Features and Multiclassification
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Author(s): Michael Haefner (Medical University of Vienna, Austria), Alfred Gangl (Medical University of Vienna, Austria), Michael Liedlgruber (Salzburg University, Austria), A. Uhl (Salzburg University, Austria), Andreas Vecsei (St. Anna Children’s Hospital, Austria)and Friedrich Wrba (Medical University of Vienna, Austria)
Copyright: 2009
Pages: 16
Source title:
Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications
Source Author(s)/Editor(s): Themis P. Exarchos (University of Ioannina, Greece ), Athanasios Papadopoulos (University of Ioannina, Greece )and Dimitrios I. Fotiadis (University of Ioannina, Greece )
DOI: 10.4018/978-1-60566-314-2.ch022
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Abstract
Wavelet-, Fourier-, and spatial domain-based texture classification methods have been used successfully for classifying zoom-endoscopic colon images according to the pit pattern classification scheme. Regarding the wavelet-based methods, statistical features based on the wavelet coefficients as well as structural features based on the wavelet packet decomposition structures of the images have been used. In the case of the Fourier-based method, statistical features based on the Fourier-coefficients in ring filter domains are computed. In the spatial domain, histogram-based techniques are used. After reviewing the various methods employed we start by extracting the feature vectors for the methods from one color channel only. To enhance the classification results the methods are then extended to utilize multichannel features obtained from all three color channels of the respective color model used. Finally, these methods are combined into one multiclassifier to stabilize classification results across the image classes.
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