Round-Robin sequential forward selection algorithm for prostate cancer classification and diagnosis using multispectral imagery

Machine Vision and Applications

Volumn 22, Issue 5, 2011, Pages 865-878

  Bouatmane, Sabrina ^a   Roula, Mohammed Ali ^b   Bouridane, Ahmed ^c,d   Al Maadeed, Somaya ^e  

^a UNIVERSITY OF JIJEL   (Algeria)

^b UNIVERSITY OF GLAMORGAN   (United Kingdom)

^c NORTHUMBRIA UNIVERSITY   (United Kingdom)

^d KING SAUD UNIVERSITY   (Saudi Arabia)

^e Qatar University   (Qatar)

Author keywords

Feature selection; Prostate cancer classification; ROC curve; Round Robin classification; Texture features

Indexed keywords

AUTOMATIC CLASSIFICATION SYSTEMS; BENIGN PROSTATIC HYPERPLASIA; BINARY CLASSIFIERS; CLASSICAL METHODS; CLASSIFICATION ACCURACY; CLASSIFICATION TASKS; ENSEMBLE METHODS; FEATURE SETS; FEATURE VECTORS; MULTI-CLASS; MULTI-CLASS PROBLEMS; MULTI-SPECTRAL IMAGERY; NEEDLE BIOPSY; PROSTATE CANCERS; PROSTATIC CARCINOMA; PROSTATIC TISSUES; RECOGNITION RATES; ROC CURVES; ROUND ROBIN; SEQUENTIAL FORWARD SELECTION; SPECTRAL BAND; STRUCTURAL PATTERN; TEXTURE FEATURES; VISIBLE SPECTRA;

BIOPSY; COMPUTATIONAL COMPLEXITY; DECISION TREES; FEATURE EXTRACTION; REMOTE SENSING; ROUTERS; TEXTURES; TISSUE;

DISEASES;

EID: 80053917878     PISSN: 09328092     EISSN: 14321769     Source Type: Journal    
DOI: 10.1007/s00138-010-0292-x     Document Type: Article

References (35)

1. Stewart, B.W., Kleihues, P.: World Cancer Report World Health Organization, International Agency for Research on Cancer (2003).
2. Kronz, J.D., Westra, W.H., Epstein, J.I.: Mandatory second opinion surgical pathology at a Large Referral Hospital. Cancer 86(11), 2426-2435 (1999).
3. Bartels, P.H. et al.: Nuclear chromatin texture in prostatic lesions: IPIN and adenocarcinoma. Anal. Quant. Cytol. Histol. 20(15), 389-396 (1998).
4. Clark, T.D., Askin, F.B., Bagnell, C.R.: Nuclear roundness factor: a quantitative approach to grading in prostate carcinoma. Reliability of needle biopsy tissue, and the effect of tumor stage on usefulness. Prostate 10(3), 199-206 (1987).
5. Gleason, D.F., Tannenbaum, M. (eds.): The veteran's administration cooperative urologic research group: histologic grading and clinical staging of prostatic carcinoma. In: Urologic Pathology: The Prostate, pp. 171-198, Lea Febiger, Philadephia (1977).
6. O'Dowd, G.J., Veltri, R.W., Miller, M.C., Strum, S.B.: The Gleason score: a significant biologic manifestation of prostate cancer aggressiveness on biopsy. Prostate Cancer Res. Inst. PCR Insights 4(1), 1-5 (2001).
7. Tabesh, A., Kumar, V., Pang, H., Verbel, D., Kotsianti, A., Teverovskiy, M., Saidi, O.: Automated prostate cancer diagnosis and Gleason grading of tissue microarrays. Proc. SPIEMed. Imaging 5747, 58-70 (2005).
8. Huang, P.W., Lee, C.H.: Automatic classification for pathological prostate images based on fractal analysis. IEEE Trans. Med. Imaging 28(7), 1037-1050 (2009).
9. Liu, Y., Zahoa, T., Zhang, J.: Learning multispectral texture features for cervical cancer detection. In: Proceedings of IEEE International Symposium on Biomedical Imaging, pp. 169-172, Washington, DC (2002).
10. Barshack, I., Kopolovic, J., Malik, Z., Rothmann, C.: Spectralmorphometric characterization of breast carcinoma cells. Br. J. Cancer 79(9-10), 1613-1619 (1999).
11. Larsh, P., Cheriboga, L., Yee, H., Diem, M.: Infrared spectroscopy of humans cells and tissue: detection of disease. Technol. Cancer Res. Treat. 1(1), 1-7 (2002).
12. Boucheron, L., Bi, Z., Harvey, N., Manjunath, B., Rimm, D.: Utility of multispectral imaging for nuclear classification of routine clinical histopathology imagery. BMC Cell Biol. 8(Suppl 1), S8 (2007).
13. Masood, K., Rajpoot, N.: Colon biopsy classification. Ann. BMVA 2008(4), 1-16 (2008).
14. Roula, M.A., Diamond, J., Bouridane, A., Miller, P., Amira, A.: A multispectral computer vision system for automatic grading of prostatic neoplasia. In: Proceedings of IEEE International Symposium on Biomedical lmaging, pp. 193-196 (2002).
15. Roula, M.A., Bouridane, A., Miller, P.: A quadratic classifier based on multispectral texture features for prostate cancer diagnosis. In: Proceedings of the Seventh International Symposium on Signal Processing Applications, pp. 37-40, Paris, France (2003).
16. Jain, A.K., Duin, R.P.W., Mao, J.: Statistical pattern recognition: a review. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 4-37 (2000).
17. Bouatmane, S., Nekhoul, B., Bouridane, A., Tanougast, C.: Classification of prostatic tissues using feature selectionmethods. IFMBE Proc. 16, 843-846 (2007).
18. Furnkranz, J.: Round Robin classification. J. Mach. Learn. Res. 2, 721-747 (2002).
19. Haralick, R.M.: Statistical and structural approaches to texture. Proc. IEEE 67, 786-804 (1979).
20. Jimenez, L.O., Landgrebe, D.A.: Supervised classification in high dimensional space: geometrical, statistical, and asymptotical properties of multivariate data. IEEE Trans. Syst.Man, Cybern. C Appl. Rev. 28(1), 39-54 (1998).
21. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, Hoboken (2001).
22. Kudo, M., Sklansky, J.: Comparison of algorithms that select features for pattern classifiers. Pattern Recognit. 33, 25-41 (2000).
23. Davies, S., Russell, S.: NP-completeness of searches for smallest possible feature sets. In: Proceedings of AAAI Fall Symposium Relevance, pp. 37-39 (1994).
24. Dash, M., Liu, H.: Feature selection for classification. Intell. Data Anal. Int. J. 1(3), 131-156 (1997).
25. Duin, R.P.W., Tax, D.M.J.: Classifier conditional posterior probabilities. In: Lecture Notes in Computer Science, vol. 1451, pp. 611-619, Springer, Berlin (1998).
26. Grimaldi, M., Cunningham, P., Kokaram, A.: An evaluation of alternative feature selection strategies and ensemble techniques of classifyingmusic. Workshop in Multimedia Discovery and Mining at ECML/PKDD (2003).
27. Raymer, M.L. et al.: Dimensionality reduction using genetic algorithms. IEEE Trans. Evol. Comput. 4(2), 164-171 (2000).
28. Tahir, M.A., Bouridane, A.: Novel Round-Robin Tabu search algorithm for prostate cancer classification and diagnosis using multispectral imagery. IEEE Trans. Inf. Technol. Biomed. 10(4), (2006).
29. Montgomery, D.: Design and analysis of experiments, 4th edn. Wiley, New York (1997).
30. Fawcett, T.: ROC graphs: notes and practical considerations for researchers. Technical Report HPL-2003-4, HP Laboratories (2003).
31. Breiman, L.:Bagging predictors.Mach. Learn. 24, 123-140 (1996).
32. Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Machine Learning: Proceedings of the Thirteenth International Conference, pp. 325-332 (1996).
33. Quinlan, J.R.: Bagging, boosting, and C4.5. In: Proceedings of the Thirteenth National Conference on Artificial Intelligence, pp. 725-730 (1996).
34. Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993).
35. Yongguang, B., Ishii, N., Du, X.: Combining multiple knearest neighbour classifiers using different distance functions. In: Lectures Notes in Computer Science (LNCS 3177), 5th International Conference on Intelligence Data Engineering and Automation Learning (IDEAL 2004). Exeter, UK.