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Related Experiment Video

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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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Directional wavelet based features for colonic polyp classification.

Georg Wimmer¹, Toru Tamaki², J J W Tischendorf³

¹University of Salzburg, Department of Computer Sciences, Jakob Haringerstrasse 2, 5020 Salzburg, Austria.

Medical Image Analysis

|March 8, 2016

Summary

This summary is machine-generated.

Weibull features extracted from dual-tree complex wavelet, Gabor wavelet, and Shearlet transforms significantly improve automated colonic polyp classification, outperforming state-of-the-art methods on diverse endoscopic images.

Keywords:

Contourlet Curvelet Polyp classification Shearlet Wavelet

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Area of Science:

Medical Imaging
Computer-Aided Diagnosis
Signal Processing

Background:

Automated classification of colonic polyps is crucial for early cancer detection.
Wavelet-based methods have shown promise, but newer transforms like curvelets, contourlets, and shearlets remain underexplored for this application.

Purpose of the Study:

To evaluate and compare the performance of various wavelet-based methods, including novel curvelet, contourlet, and shearlet transforms, for automated colonic polyp classification.
To assess the effectiveness of different feature extraction techniques, particularly Weibull distribution parameters, in enhancing classification accuracy.

Main Methods:

Application of discrete wavelet transform, dual-tree complex wavelet transform, Gabor wavelet transform, curvelets, contourlets, and shearlets to 11 diverse endoscopic image databases.
Extraction of features using 25 different techniques, including three novel Weibull feature extraction approaches for curvelets, contourlets, and shearlets.
Comparison with 5 state-of-the-art non-wavelet based methods.

Main Results:

The combination of dual-tree complex wavelet transform, Gabor wavelet transform, or Shearlet transform with Weibull features achieved high classification results.
These three advanced wavelet-based approaches outperformed state-of-the-art methods on most tested databases.
Weibull distribution parameters proved more effective in modeling subband coefficients than Gaussian or generalized Gaussian distributions.

Conclusions:

Wavelet-based methods, particularly dual-tree complex wavelet, Gabor wavelet, and Shearlet transforms utilizing Weibull features, offer superior performance for automated colonic polyp classification.
The findings highlight the potential of these advanced transforms and feature extraction techniques for improving diagnostic accuracy in gastrointestinal endoscopy.