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

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.

[1]  R. Kiesslich,et al.  Inter- and Intra-Observer Variability of Magnification Chromoendoscopy for Detecting Specialized Intestinal Metaplasia at the Gastroesophageal Junction , 2004, Endoscopy.

[2]  M. Hafner,et al.  Pit Pattern Classification of Zoom-Endoscopic Colon Images using Histogram Techniques , 2006, Proceedings of the 7th Nordic Signal Processing Symposium - NORSIG 2006.

[3]  Paul Scheunders,et al.  Color Texture Classification by Wavelet Energy Correlation Signatures , 1997, ICIAP.

[4]  Karel J. Zuiderveld,et al.  Contrast Limited Adaptive Histogram Equalization , 1994, Graphics Gems.

[5]  Dimitris A. Karras,et al.  Computer-aided tumor detection in endoscopic video using color wavelet features , 2003, IEEE Transactions on Information Technology in Biomedicine.

[6]  Richard Baraniuk,et al.  The dual-tree complex wavelet transform , 2005, IEEE Signal Processing Magazine.

[7]  Andreas Uhl,et al.  Modeling the Marginal Distributions of Complex Wavelet Coefficient Magnitudes for the Classification of Zoom-Endoscopy Images , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[8]  S. Kudo,et al.  Diagnosis of colorectal tumorous lesions by magnifying endoscopy. , 1996, Gastrointestinal endoscopy.

[9]  DP Hurlstone,et al.  High-resolution magnification chromoendoscopy: common problems encountered in “pit pattern” interpretation and correct classification of flat colorectal lesions , 2002, American Journal of Gastroenterology.

[10]  Christoph Palm,et al.  Color texture classification by integrative Co-occurrence matrices , 2004, Pattern Recognit..

[11]  Pavel Pudil,et al.  Introduction to Statistical Pattern Recognition , 2006 .

[12]  M. Hafner,et al.  Pit Pattern Classification of Zoom-Endoscopical Colon Images using Evolved Fourier Feature Vectors , 2007, 2007 IEEE Workshop on Machine Learning for Signal Processing.

[13]  Andreas Uhl,et al.  Pit Pattern Classification of Zoom-Endoscopical Colon Images Using DCT and FFT , 2007, Twentieth IEEE International Symposium on Computer-Based Medical Systems (CBMS'07).

[14]  S. Kudo,et al.  Colorectal tumours and pit pattern. , 1994, Journal of clinical pathology.

[15]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[16]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  N. Kingsbury.  Complex Wavelets for Shift Invariant Analysis and Filtering of Signals , 2001 .

[18]  Paul S. Heckbert,et al.  Graphics gems IV , 1994 .

[19]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Richard Baraniuk,et al.  The Dual-tree Complex Wavelet Transform , 2007 .

[21]  Ronald R. Coifman,et al.  Local discriminant bases , 1994, Optics & Photonics.

[22]  Nick Kingsbury,et al.  The dual-tree complex wavelet transform: a new technique for shift invariance and directional filters , 1998 .

[23]  Andreas Uhl,et al.  Pit pattern classification of zoom-endoscopic colon images using wavelet texture features , 2006 .

[24]  S. Mitra,et al.  Texture classification using dual-tree complex wavelet transform , 1999 .