Feature extraction from multi-directional multi-resolution image transformations for the classification of zoom-endoscopy images | Pattern Analysis and Applications Skip to main content
Springer Nature Link
Log in

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

  • Theoretical Advances
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. http://www.cancer.org.

  2. the process of removing polyps.

References

  1. Fukunaga K (1990) Introduction to statistical pattern recognition. Morgan Kaufmann, Menlo Park

  2. Häfner M, Brunauer L, Payer H, Resch R, Wrba F, Gangl A, Vécsei A, Uhl A (2007) Pit pattern classification of zoom-endoscopic colon images using DCT and FFT. In: Kokol P, Podgorelec V, Micetic-Turk D, Zorman M, Verlic M (eds) Proceedings of the 20th IEEE International Symposium on Computer-Based Medical Systems (CBMS 2007), Maribor, June 2007. IEEE Computer Society CPS, New York, pp 159–164

  3. Häfner M, Brunauer L, Payer H, Resch R, Wrba F, Gangl A, Vécsei A, Uhl A (2007) Pit pattern classification of zoom-endoscopical colon images using evolved Fourier feature vectors. In: Diamantaras K, Adali T, Pitas I, Larsen J, Papadimitriou T, Douglas S (eds) Proceedings of the 2007 IEEE Machine Learning for Signal Processing Workshop (MLSP’07), Thessaloniki, August 2007. IEEE, New York, pp 99–104

  4. Häfner M, Kendlbacher C, Mann W, Taferl W, Wrba F, Gangl A, Vecsei A, Uhl A (2006) Pit pattern classification of zoom-endoscopic colon images using histogram techniques. In: Proceedings of the 7th Nordic Signal Processing Symposium (NORSIG’06), pp 58–61, Reykjavik

  5. Häfner M, Liedlgruber M, Wrba F, Gangl A, Vecsei A, Uhl A (2006) Pit pattern classification of zoom-endoscopic colon images using wavelet texture features. In: Proceedings of the 3rd International Conference on Advances in Medical Signal and Image Processing (MEDSIP’06), Glasgow

  6. Haralick RM (1973) Textural features for image classification. IEEE Trans Syst Men Cybern 3(6):610–621

    Article  MathSciNet  Google Scholar 

  7. Hatipoglu S, Mitra N, Kingsbury S (1999) Texture classification using dual-tree complex wavelet transform. In: Proceedings of the 7th International Conference on Image Processing and Its Applications, pp 344–347, Brisbane

  8. Hurlstone DP (2002) High-resolution magnification chromoendoscopy: common problems encountered in pit-pattern interpretation and correct classification of flat colorectal lesions. Am J Gastroenterol 97(4):1069–1070

    Google Scholar 

  9. Karkanis SA, Iakovids DK, Maroulis DE (2003) Computer-aided tumor detection in endoscopic video using color wavelet features. IEEE Trans Inform Technol Biomed 7(3):141–152

    Article  Google Scholar 

  10. Kingsbury N (1998) The dual-tree complex wavelet transform: a new technique for shift-invariance and directional filters. In: Proceedings of the 8th IEEE DSP Workshop, pp 9–12, Bryce Canyon, Utah

  11. Kingsbury N (2001) Complex wavelets for shift-invariant analysis and filtering of signals. J Appl Comput Harmonic Anal 10(3):234–253

    Article  MATH  MathSciNet  Google Scholar 

  12. Kittler J, Hatef M, Duin RPW, and Matas J (1998) On combining classifiers. IEEE Trans Pattern Anal Mach Intell 20(3):226–239

    Article  Google Scholar 

  13. Kudo S (1994) Colorectal tumours and pit pattern. J Clin Pathol 47:880–885

    Article  Google Scholar 

  14. Kudo S, Tamura S, Nakajima T, Yamano H, Kusaka H, Watanabe H (1996) Diagnosis of colorectal tumorous lesions by magnifying endoscopy. Gastrointest Endosc 44(1):8–14

    Article  Google Scholar 

  15. Kwitt R, Uhl A (2007) Modeling the marginal distributions of complex wavelet coefficient magnitudes for the classification of zoom-endoscopy images. In: Proceedings of the IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA’07), Rio de Janeiro

  16. Manjunath BS and Ma WY (1996) Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell 18(8):837–842

    Article  Google Scholar 

  17. Meining A (2004) Inter- and intra-observer variability of magnification chromoendoscopy for detecting specialized intestinal metaplasia at the gastroesophageal junction. Endoscopy 36(2):160–164

    Article  Google Scholar 

  18. Palm C (2004) Color texture classification by integrative cooccurrence matrices. Pattern Recognit 37(5):965–976

    Article  Google Scholar 

  19. Saito N, Coifman R (1994) Local discriminant bases. Math Imaging Wavelet Appl Signal Image Process II 2303:2–14

    Google Scholar 

  20. Selesnick I, Baraniuk R, and Kingsbury N (2005) The dual-tree complex wavelet transform. IEEE Signal Process Mag 22(6):123–151

    Article  Google Scholar 

  21. Van de Wouwer G, Livens S, Scheunders P, Van Dyck D (1997) Color texture classification by wavelet energy correlation signatures. In: Proceedings of the 9th International Conference on Image Analysis and Processing (ICIAP’97). Springer, Berlin, pp 327–334

  22. Zuiderveld K (2004) Contrast limited adaptive histogram equalization, Chap. VIII.5, pp 474–485. Graphics GEMS IV. Morgan Kaufmann, Menlo Park

Download references

Acknowledgments

This work is funded by the Austrian Science Fund (FWF) under Project No. L366-N15.

Author information

Authors and Affiliations

  1. Department of Clinical Pathology, Vienna Medical University, 1090, Vienna, Austria

    Michael Häfner & Alfred Gangl

  2. Department of Computer Sciences, University of Salzburg, 5020, Salzburg, Austria

    Roland Kwitt & Andreas Uhl

  3. Department of Gastroenterology and Hepatology, Vienna Medical University, 1090, Vienna, Austria

    Friedrich Wrba

  4. St. Anna Children’s Hospital, 1090, Vienna, Austria

    Andreas Vécsei

Authors
  1. Michael Häfner
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Roland Kwitt
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Andreas Uhl
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Alfred Gangl
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Friedrich Wrba
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Andreas Vécsei
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Roland Kwitt.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Häfner, M., Kwitt, R., Uhl, A. et al. Feature extraction from multi-directional multi-resolution image transformations for the classification of zoom-endoscopy images. Pattern Anal Applic 12, 407–413 (2009). https://doi.org/10.1007/s10044-008-0136-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-008-0136-8

Keywords