Classifier ensemble for an effective cytological image analysis

Pattern Recognition Letters

Volumn 34, Issue 14, 2013, Pages 1748-1757

  Filipczuk, Paweł ^b   Krawczyk, Bartosz ^a   Woźniak, Michał ^a  

^a WROCLAW UNIVERSITY OF TECHNOLOGY   (Poland)

^b UNIVERSITY OF ZIELONA GÓRA   (Poland)

Author keywords

Classifier ensemble; Image analysis; Machine learning; Multiple classifier system; Pattern recognition; Trained fuser

Indexed keywords

BIOPSY; CLASSIFICATION (OF INFORMATION); DISEASES; FEATURE EXTRACTION; IMAGE ANALYSIS; IMAGE SEGMENTATION; LEARNING SYSTEMS; MEDICAL IMAGING; PATTERN RECOGNITION; PATTERN RECOGNITION SYSTEMS;

CLASSIFICATION METHODS; CLASSIFIER ENSEMBLES; FINE NEEDLE BIOPSY; HYBRID SEGMENTATION; INDIVIDUAL CLASSIFIERS; LINEAR COMBINATIONS; MULTIPLE CLASSIFIER SYSTEMS; TRAINED FUSER;

IMAGE CLASSIFICATION;

EID: 84885645688     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2013.04.030     Document Type: Article

References (54)

1. Al-Kofahi, Y., Lassoued, W., Lee, W., Roysam, B., 2010. Improved automatic detection and segmentation of cell nuclei in histopathology images. IEEE Trans. Biomed. Eng. 57 (4), 841-852.
2. Alpaydin, E., 2010. Introduction to Machine Learning, second ed. The MIT Press.
3. Bezdek, D., 1981. Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York.
4. Biggio, B., Fumera, G., Roli, F., 2007. Bayesian analysis of linear combiners. In: Proceedings of the Seventh International Conference on Multiple Classifier Systems, MCS'07, Springer-Verlag, Berlin, Heidelberg, pp. 292-301. (Pubitemid 350270626)
5. Bray, F., Ren, J., Masuyer, E., Ferlay, J., 2012. Estimates of global cancer prevalence for 27 sites in the adult population in 2008. Int. J. Cancer. http://dx.doi.org/ 10.1002/ijc.27711.
6. Britton, P., Duffy, S., Sinnatamby, R., Wallis, M., Barter, S., Gaskarth, M., O'Neill, A., Caldas, C., Brenton, J., Forouhi, P., Wishart, G., 2009. One-stop diagnostic breast clinics: how often are breast cancers missed?, Br. J. Cancer.
7. Brown, G., Kuncheva, L.I., 2010. Good and bad diversity in majority vote ensembles. In: MCS, pp. 124-133.
8. Clocksin, W.F., 2003. Automatic segmentation of overlapping nuclei with high background variation using robust estimation and flexible contour models. In: Proc. 12th Int. Conf. Image Analysis and Processing, ICIAP03, pp. 682-687.
9. Cloppet, F., Boucher, A., 2008. Segmentation of overlapping/aggregating nuclei cells in biological images. In: Proc. ICPR, IEEE, pp. 1-4.
10. Dempster, A.P., Laird, N.M., Rubin, D.B., 1977. Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B 39 (1), 1-38.
11. Dong, G., Xie, M., 2005. Color clustering and learning for image segmentation based on neural networks. IEEE Trans. Neural Networks 16 (4), 925-936. (Pubitemid 41064323)
12. Duin, R., 2002. The combining classifier: to train or not to train?, In: Proceedings of the 16th International Conference on Pattern Recognition, vol. 2, pp. 765-770. http://dx.doi.org/10.1109/ICPR.2002.1048415. (Pubitemid 44844850)
13. Ferlay, J., Shin, H., Bray, F., Forman, D., Mathers, C., Parkin, D., 2010. Globocan 2008 v2.0, cancer incidence and mortality worldwide: Iarc cancerbase no. 10. (accessed on 30.08.2012).
14. Filipczuk, P., Kowal, M., Obuchowicz, A., 2011a. Automatic breast cancer diagnosis based on k-means clustering and adaptive thresholding hybrid segmentation. In: Choraś, Ryszard S. (Ed.), Image Processing and Communications Challenges 3. Advances in Intelligent and Soft Computing, vol. 102. Springer-Verlag, Berlin/ Heidelberg, pp. 295-303.
15. Filipczuk, P., Kowal, M., Obuchowicz, A., 2011b. Fuzzy clustering and adaptive thresholding based segmentation method for breast cancer diagnosis. In: Red, M.W., Burduk, R., Kurzyński, M., Zołnierek, A. (Eds.), Computer Recognition Systems 4. Advances in Intelligent and Soft Computing, vol. 95. Springer-Verlag, Berlin/Heidelberg, pp. 613-622.
16. Gonzalez, R.C., Woods, R.E., 2001. Digital Image Processing. Prentice Hall, New Jersey.
17. Gurcan, M.N., Boucheron, L.E., Can, A., Madabhushi, A., Rajpoot, N.M., Yener, B., 2009. Histopathological image analysis: a review. IEEE Rev. Biomed. Eng. 2, 147-171.
18. Hassan, M.R., Hossain, M.M., Begg, R.K., Ramamohanarao, K., Morsi, Y., 2010. Breastcancer identification using HMM-fuzzy approach. Comput. Biol. Med. 40, 240-251.
19. Ho, T.K., 1998. The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20, 832-844. (Pubitemid 128741345)
20. Hrebień, M., Steć, P., Obuchowicz, A., Nieczkowski, T., 2010. Segmentation of breast cancer fine needle biopsy cytological images. Int. J. Appl. Math. Comput. Sci. 18 (2), 159-170.
21. Jackowski, K., Krawczyk, B., Woźniak, M., 2012. Cost-sensitive splitting and selection method for medical decision support system. In: Yin, H., Costa, J., Barreto, G. (Eds.), Intelligent Data Engineering and Automated Learning-IDEAL. Lecture Notes in Computer Science, vol. 7435. Springer, Berlin, Heidelberg, pp. 850-857.
22. Jacobs, R.A., 1995. Methods for combining experts' probability assessments. Neural Comput. 7 (5), 867-888.
23. Jain, A., Duin, R., Mao, J., 2000. Statistical pattern recognition: a review. IEEE Trans. Pattern Anal. Mach. Intell. 22 (1), 4-37. http://dx.doi.org/10.1109/34.824819. (Pubitemid 32132953)
24. Jeleń, L., Fevens, T., Krzyzak, A., 2010. Classification of breast cancer malignancy using cytological images of fine needle aspiration biopsies. Int. J. Appl. Math. Comput. Sci. 18 (1), 75-83. (Pubitemid 351444623)
25. Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y., 2002. An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24, 881-892. (Pubitemid 34835464)
26. Kowal, M., Filipczuk, P., Obuchowicz, A., Korbicz, J., 2011a. Computer-aided diagnosis of breast cancer using Gaussian mixture cytological image segmentation. J. Med. Inform. Technol. 17, 257-262.
27. Kowal, M., Filipczuk, P., Korbicz, J., 2011b. Hybrid cytological image segmentation method based on competitive neural network and adaptive thresholding. Pomiary, Automatyka, Kontrola 57 (11), 1448-1451.
28. Krawczyk, B., Filipczuk, P., Woźniak, M., 2012a. Adaptive splitting and selection algorithm for classification of breast cytology images. In: Nguyen, N.-T., Hoang, K., Jedrzejowicz, P. (Eds.), Computational Collective Intelligence. Technologies and Applications. Lecture Notes in Computer Science, vol. 7653. Springer, Berlin, Heidelberg, pp. 475-484.
29. Krawczyk, B., Schaefer, G., Woźniak, M., 2012b. Breast thermogram analysis using a cost-sensitive multiple classifier system. In: Proceedings of the IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI 2012), pp. 507-510.
30. Kuncheva, L.I., 2004. Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience.
31. Kurzynski, M., Woźniak, M., 2012. Combining classifiers under probabilistic models: experimental comparative analysis of methods. Expert Syst. 29 (4), 374-393.
32. Lloyd, S.P., 1982. Least squares quantization in PCM. IEEE Trans. Inform. Theory 28 (2), 129-137.
33. MacKay, D., 2003. Information Theory, Inference and Learning Algorithms. Cambridge University Press.
34. Malek, J., Sebri, A., Mabrouk, S., Torki, K., Tourki, R., 2009. Automated breast cancer diagnosis based on GVF-Snake segmentation, wavelet features extraction and fuzzy classification. J. Signal Process. Syst. 55, 49-66.
35. Martinez-Munoz, G., Hernandez-Lobato, D., Suarez, A., 2009. An analysis of ensemble pruning techniques based on ordered aggregation. IEEE Trans. Pattern Anal. Mach. Intell. 31 (2), 245-259.
36. National Cancer Registry in Poland, 2012. (accessed on 19.10.2012).
37. Naz, S., Majeed, H., Irshad, H., 2010. Image segmentation using fuzzy clustering: a survey. In: Proc. Sixth Int. Conf. Emerging Technologies, ICET 2010, pp. 181-186.
38. Nguyen, K., Sabata, B., Jain, A., 2012. Prostate cancer grading: gland segmentation and structural features. Pattern Recognition Lett. 33 (7), 951-961.
39. Niwas, I.S., Palanisamy, P., Sujathan, K., 2010. Wavelet based feature extraction method for breast cancer cytology images. In: 2010 IEEE Symp. on Industrial Electronics & Applications (ISIEA), pp. 686-690.
40. Obuchowicz, A., Hrebień, M., Nieczkowski, T., Marciniak, A., 2008. Computational intelligence techniques in image segmentation for cytopathology. In: Smoliński, T.G., Milanova, M.G., Hassanien, A.-G. (Eds.), Computational Intelligence in Biomedicine and Bioinformatics. Springer-Verlag, Berlin, pp. 169-199.
41. Peng, Y., Park, M., Xu, M., Luo, S., Jin, J.S., Cui, Y., Wong, F.W.S., Santos, L.D., 2010. Clustering nuclei using machine learning techniques. In: Proc. Int. IEEE/ICME Conf. on Complex Medical, Engineering, pp. 52-57.
42. Plissiti, M., Nikou, C., Charchanti, A., 2011. Combining shape, texture and intensity features for cell nuclei extraction in pap smear images. Pattern Recognition Lett. 32 (6), 838-853.
43. Ruta, D., Gabrys, B., 2005. Classifier selection for majority voting. Inform. Fusion 6 (1), 63-81.
44. Śmietański, J., Tadeusiewicz, R., Łuczyńska, E., 2010. Texture analysis in perfusion images of prostate cancer-a case study. Int. J. Appl. Math. Comput. Sci. 20 (1), 149-156.
45. Suri, J.S., Setarhdan, K., Singh, S., 2002. Advanced Algorithmic Approaches to Medical Image Segmentation. Springer-Verlag, London.
46. Team, R.D.C., 2008. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
47. Underwood, J.C.E., 1987. Introduction to Biopsy Interpretation and Surgical Pathology. Springer-Verlag, London.
48. van Erp, M., Vuurpijl, L., Schomaker, L., 2002. An overview and comparison of voting methods for pattern recognition. In: Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition, pp. 195-200.
49. Wolpert, D.H., 2001. The supervised learning no-free-lunch theorems. In: Proc. Sixth Online World Conference on Soft Computing in Industrial Applications, pp. 25-42.
50. Woods, K.S., Kegelmeyer, W.P., Bowyer, K.W., 1997. Combination of multiple classifiers using local accuracy estimates. IEEE Trans. Pattern Anal. Mach. Intell. 19 (4), 405-410.
51. Woźniak, M., Jackowski, K., 2009. Some remarks on chosen methods of classifier fusion based on weighted voting. In: Corchado, E., Wu, X., Oja, E., Herrero, A., Baruque, B. (Eds.), Hybrid Artificial Intelligence Systems. Lecture Notes in Computer Science, vol. 5572. Springer, Berlin/Heidelberg, pp. 541-548.
52. Woźniak, M., Krawczyk, B., 2012. Combined classifier based on feature space partitioning. J. Appl. Math. Comput. Sci. 22 (4), 855-866.
53. Woźniak, M., Zmyslony, M., 2010. Combining classifiers using trained fuser-analytical and experimental results. Neural Network World 13 (7), 925-934.
54. Xiong, X., Kim, Y., Baek, Y., Rhee, D.W., Kim, S.-H., 2005. Analysis of breast cancer using data mining & statistical techniques. In: Proc. Sixth Int. Conf. on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS Int. Worksh. on Self-Assembling, Wireless Networks, pp. 82-87. (Pubitemid 44499641)