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Texture Description Using Dual Tree Complex Wavelet Packets

  • M. Liedlgruber
  • M. Häfner
  • J. Hämmerle-Uhl
  • A. UhlEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9916)

Abstract

In this work we extend several DWT-based wavelet and wavelet packet feature extraction methods to use the dual-tree complex wavelet transform. This way we aim at alleviating shortcomings of the different algorithms which stem from the use of the underlying DWT. We show that, while some methods benefit significantly from extending them to be based in the dual-tree complex wavelet transform domain (and also provide the best overall results), for other methods there is almost no impact of this extension.

Keywords

Feature Vector Discrete Wavelet Transform Filter Bank Wavelet Packet Feature Extraction Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work has been supported by the Austrian Science Fund (FWF) under Project No. TRP-206.

References

  1. 1.
    Selesnick, I.W., Baraniuk, R.G., Kingsbury, N.G.: The dual-tree complex wavelet transform - a coherent framework for multiscale signal and image processing. IEEE Sig. Process. Mag. 22(6), 123–151 (2005)CrossRefGoogle Scholar
  2. 2.
    Häfner, M., Kwitt, R., Uhl, A., Gangl, A., Wrba, F., Vécsei, A.: Feature-extraction from multi-directional multi-resolution image transformations for the classification of zoom-endoscopy images. Pattern Anal. Appl. 12(4), 407–413 (2009)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Bayram, İ., Selesnick, I.W.: On the dual-tree complex wavelet packet and m-band transforms. IEEE Trans. Sig. Process. 56(6), 2298 (2008)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Weickert, T., Kiencke, U.: Analytic wavelet packets - combining the dual-tree approach with wavelet packets for signal analysis and filtering. IEEE Trans. Sig. Process. 57(2), 493 (2009)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Liedlgruber, M., Uhl, A.: Statistical and structural wavelet packet features for pit pattern classification in zoom-endoscopic colon images. In: Dondon, P., Mladenov, V., Impedovo, S., Cepisca, S. (eds.) Proceedings of the 7th WSEAS International Conference on Wavelet Analysis & Multirate Systems (WAMUS 2007), Arcachon, France, pp. 147–152, October 2007Google Scholar
  6. 6.
    Coifman, R.R., Wickerhauser, M.V.: Entropy based methods for best basis selection. IEEE Trans. Inf. Theor. 38(2), 719–746 (1992)CrossRefzbMATHGoogle Scholar
  7. 7.
    Häfner, M., Liedlgruber, M., Wrba, F., Gangl, A., Vécsei, A., Uhl, A.: Pit pattern classification of zoom-endoscopic colon images using wavelet texture features. In: Sandham, W., Hamilton, D., James, C. (eds.) Proceedings of the International Conference on Advances in Medical Signal and Image Processing (MEDSIP 2006), Glasgow, Scotland, UK, pp. 1–4, July 2006Google Scholar
  8. 8.
    Saito, N., Coifman, R.R.: Local discriminant bases. In: SPIE’s 1994 International Symposium on Optics, Imaging, and Instrumentation, International Society for Optics and Photonics, pp. 2–14 (1994)Google Scholar
  9. 9.
    Kylberg, G.: The Kylberg texture dataset v. 1.0. External report (Blue series) 35, Centre for Image Analysis, Swedish University of Agricultural Sciences and Uppsala University, Uppsala, Sweden, September 2011Google Scholar
  10. 10.
    Kato, S., Fu, K.I., Sano, Y., Fujii, T., Saito, Y., Matsuda, T., Koba, I., Yoshida, S., Fujimori, T.: Magnifying colonoscopy as a non-biopsy technique for differential diagnosis of non-neoplastic and neoplastic lesions. World J. Gastroenterol. 12(9), 1416–1420 (2006)CrossRefGoogle Scholar
  11. 11.
    Häfner, M., Liedlgruber, M., Uhl, A.: Colonic polyp classification in high- definition video using complex wavelet-packets. In: Proceedings of Bildverarbeitung für die Medizin 2015 (BVM 2015), pp. 365–370, March 2015Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • M. Liedlgruber
    • 1
  • M. Häfner
    • 2
  • J. Hämmerle-Uhl
    • 1
  • A. Uhl
    • 1
    Email author
  1. 1.Visual Computing and Security Lab (VISEL), Department of Computer SciencesUniversity of SalzburgSalzburgAustria
  2. 2.Department for Internal MedicineSt. Elisabeth HospitalViennaAustria

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