Computer-assisted pit-pattern classification in different wavelet domains for supporting dignity assessment of colonic polyps

Pattern Recognition

Volumn 42, Issue 6, 2009, Pages 1180-1191

  Häfner, Michael ^b   Kwitt, Roland ^a   Uhl, Andreas ^a   Wrba, Friedrich ^b   Gangl, Alfred ^b   Vécsei, Andreas ^c  

^a UNIVERSITY OF SALZBURG   (Austria)

^b MEDICAL UNIVERSITY OF VIENNA   (Austria)

^c ST ANNA CHILDREN S HOSPITAL   (Austria)

Author keywords

Color texture analysis; Colorectal cancer; Computer assisted pit pattern classification; Wavelet transformation

Indexed keywords

COLOR; COMPUTER AIDED ANALYSIS; FOURIER TRANSFORMS; IMAGE ANALYSIS; IMAGE CLASSIFICATION; MATHEMATICAL TRANSFORMATIONS; MEDICAL IMAGING; TEXTURES;

CHANNEL INFORMATIONS; CO-OCCURRENCE MATRICES; COLONIC POLYPS; COLOR CHANNELS; COLOR MODELS; COLOR-TEXTURE ANALYSIS; COLORECTAL CANCER; COMPUTER-ASSISTED PIT-PATTERN CLASSIFICATION; FEATURE SELECTIONS; FEATURE VECTORS; GRAY-SCALE; IMAGE FEATURES; K-NEAREST NEIGHBOR CLASSIFICATIONS; MEDICAL IMAGE CLASSIFICATIONS; WAVELET DOMAINS; WAVELET TRANSFORMATION;

WAVELET TRANSFORMS;

EID: 59149094561     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2008.07.012     Document Type: Article

References (35)

1. Kudo S. Colorectal tumours and pit pattern. J. Clin. Pathol. 47 10 (1994) 880-885
2. Kato S., Fujii T., Koba I., Sano Y., Fu K.I., Parra-Blanco A., Tajiri H., Yoshida S., and Rembacken B. Assessment of colorectal lesions using magnifying colonoscopy and mucosal dye spraying: can significant lesions be distinguished?. Endoscopy 33 3 (2001) 306-311
3. Hurlstone D. High-resolution magnification chromoendoscopy: common problems encountered in pit-pattern interpretation and correct classification of flat colorectal lesions. Am. J. Gastroenterol. 97 4 (2002) 1069-1070
4. Kato S., Fu K., Sano Y., Fujii T., Saito Y., Matsuda T., Koba I., Yoshida S., and Fujimori T. Magnifying colonoscopy as a non-biopsy technique for differential diagnosis of non-neoplastic and neoplastic lesions. World Gastroenterol. 12 9 (2006) 1416-1420
5. Hurlstone D., Cross S., Adam I., Shorthouse A., Brown S., Sanders D., and Lobo A. Efficacy of high magnification chromoscopic colonoscopy for the diagnosis of neoplasia in flat and depressed lesions of the colorectum: a prospective analysis. Gut 53 (2004) 284-290
6. Fu K. Chromoendoscopy using indigo carmine dye spraying with magnifying observation is the most reliable method for differential diagnosis between non-neoplastic and neoplastic colorectal lesions. Endoscopy 36 12 (2004) 1089-1093
7. Meining A. Inter- and intra-observer variability of magnification chromoendoscopy for detecting specialized intestinal metaplasia at the gastroesophageal junction. Endoscopy 36 2 (2004) 160-164
8. Häfner M., Kendlbacher C., Mann W., Taferl W., Wrba F., Gangl A., Vecsei A., and Uhl A. Pit pattern classification of zoom-endoscopic colon images using histogram techniques. Proceedings of the 7th Nordic Signal Processing Symposium (NORSIG'06) (2006), Reykjavik, Iceland 58-61
9. Häfner M., Liedlgruber M., Wrba F., Gangl A., Vecsei A., and Uhl A. Pit pattern classification of zoom-endoscopic colon images using wavelet texture features. Proceedings of the 3rd International Conference on Advances in Medical Signal and Image Processing (MEDSIP'06) (2006), Glasgow, Scotland
10. Saito N., and Coifman R. Local discriminant bases. Mathematical Imaging: Wavelet Applications in Signal and Image Processing II 2303 (1994) 2-14
11. Häfner M., Brunauer L., Payer H., Resch R., Wrba F., Gangl A., Vécsei A., and Uhl A. Pit pattern classification of zoom-endoscopic colon images using DCT and FFT. In: Kokol P., Podgorelec V., Micetic-Turk D., Zorman M., and Verlic M. (Eds). Proceedings of the 20th IEEE International Symposium on Computer-Based Medical Systems (CBMS 2007) (2007), IEEE Computer Society CPS, Maribor, Slovenia 159-164
12. Häfner M., Brunauer L., Payer H., Resch R., Wrba F., Gangl A., Vécsei A., and Uhl A. Pit pattern classification of zoom-endoscopical colon images using evolved Fourier feature vectors. In: Diamantaras K., Adali T., Pitas I., Larsen J., Papadimitriou T., and Douglas S. (Eds). Proceedings of the 2007 IEEE Machine Learning for Signal Processing Workshop (MLSP'07) (2007), IEEE, Thessaloniki, Greece 99-104
13. Fukunaga K. Introduction to Statistical Pattern Recognition (1990), Morgan Kaufmann
14. R. Kwitt, A. Uhl, M. Häfner, F. Wrba, A. Gangl, A. Vécsei, Multi-directional multi-resolution transforms for zoom-endoscopy image classification, in: Advances in Soft Computing, vol. 45, Springer, Berlin, 2008.
15. Manjunath B., and Ma W. Texture features for browsing and retrieval of image data. IEEE Trans. Pattern Anal. Mach. Intell. 18 8 (1996) 837-842
16. Kingsbury N. The dual-tree complex wavelet transform: a new technique for shift-invariance and directional filters. Proceedings of the 8th IEEE DSP Workshop (1998), Bryce Canyon, Utah, USA 9-12
17. Kwitt R., and Uhl A. Modeling the marginal distributions of complex wavelet coefficient magnitudes for the classification of zoom-endoscopy images. Proceedings of the IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA'07) (2007), Rio de Janeiro, Brazil
18. Karkanis S., Iakovids D., and Maroulis D. Computer-aided tumor detection in endoscopic video using color wavelet features. IEEE Trans. Inform. Technol. Biomed. 7 3 (2003) 141-152
19. Haralick R. Textural features for image classification. IEEE Trans. Syst. Man Cybernet. 3 6 (1973) 610-621
20. Mallat S. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11 7 (1989) 674-693
21. Fliege N. Multirate Digital Signal Processing (1994), Wiley, New York
22. Selesnick I., Baraniuk R., and Kingsbury N. The dual-tree complex wavelet transform. IEEE Signal Process. Mag. 22 6 (2005) 123-151
23. Shensa M. The discrete wavelet transform: wedding the Á Trous and Mallat algorithms. IEEE Trans. Signal Process. 40 10 (1992) 2464-2482
24. G. Nason, B. Silverman, The stationary wavelet transform and some statistical applications, in: Lecture Notes in Statistics, vol. 103, 1995, pp. 281-300.
25. Selesnick I. The design of Hilbert transform pairs of wavelet bases. Signal Process. Lett. 8 6 (2001) 170-173
26. Bishop C. Neural Networks for Pattern Recognition (1995), Oxford University Press, Oxford
27. Van de Wouwer G., Scheunders P., and Van Dyck D. Statistical texture characterization from discrete wavelet representations. IEEE Trans. Image Process. 8 4 (1999) 592-598
28. Hatipoglu S., Mitra S., and Kingsbury N. Texture classification using dual-tree complex wavelet transform. Proceedings of the 7th International Conference on Image Processing and Its Applications (1999), Brisbane, Australia 344-347
29. Palm C. Color texture classification by integrative cooccurrence matrices. Pattern Recognition 37 5 (2004) 965-976
30. Kingsbury N. Complex wavelets for shift-invariant analysis and filtering of signals. J. Appl. Comput. Harmonic Anal. 10 3 (2001) 234-253
31. Gonzalez R., and Woods R. Digital a Image Processing. second ed. (2002), Prentice-Hall, Englewood Cliffs, NJ
32. Lukac R., and Plataniotis K. Color Image Processing-Methods and Applications (2006), CRC Press, Boca Raton, FL
33. B. Everitt, The Analysis of Contingency Tables, second ed., Monographs on Statistics and Applied Probability, vol. 45, Chapman & Hall/CRC, 1992.
34. Dietterich T. Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput. 10 7 (1998) 1895-1923
35. Zamolotskikh A., Delany S., and Cunningham P. A methodology for comparing classifiers that allow the control of bias. Proceedings of the 2006 ACM Symposium on Applied Computing (SAC'06) (2006), ACM, New York, NY, USA 582-587