Cytological image analysis with firefly nuclei detection and hybrid one-class classification decomposition

Engineering Applications of Artificial Intelligence

Volumn 31, Issue , 2014, Pages 126-135

  Krawczyk, Bartosz ^a   Filipczuk, Paweł ^b  

^a WROCLAW UNIVERSITY OF TECHNOLOGY   (Poland)

^b UNIVERSITY OF ZIELONA GÓRA   (Poland)

Author keywords

Classifier ensemble; Ensemble pruning; Firefly algorithm; Image analysis; Multi class decomposition; One class classification

Indexed keywords

ALGORITHMS; COMPUTER AIDED DIAGNOSIS; CYTOLOGY; HOSPITAL DATA PROCESSING; IMAGE ANALYSIS; IMAGE RECOGNITION;

BREAST CANCER DIAGNOSIS; CLASSIFIER ENSEMBLES; ENSEMBLE PRUNING; FIREFLY ALGORITHMS; MARKER-CONTROLLED WATERSHED SEGMENTATION; MEDICAL DECISION SUPPORTS; ONE-CLASS CLASSIFICATION; STATE-OF-THE-ART METHODS;

BIOLUMINESCENCE;

EID: 84899493558     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2013.09.017     Document Type: Article

References (57)

1. E. Alpaydin Introduction to Machine Learning second edition 2010 The MIT Press
2. Beucher, S.; Lantuéjoul, C.; 1979. Use of watersheds in contour detection. In: International Workshop on Image Processing: Real-Time Edge and Motion Detection/Estimation, Rennes, France.
3. C.M. Bishop Novelty detection and neural network validation IEE Proc. Vis. Image Signal Process. 141 4 1994 217 222
4. F. Bray, J.S. Ren, E. Masuyer, and J. Ferlay Global estimates of cancer prevalence for 27 sites in the adult population in 2008 Int. J. Cancer 132 5 2013 1133 1145
5. R. Burduk Classifier fusion with interval-valued weights Pattern Recognition Lett. 34 14 2013 1623 1629
6. C.J.C. Burges A tutorial on support vector machines for pattern recognition Data Mining Knowl. Discovery 2 2 1998 121 167
7. Cheplygina, V.; Tax, D.M.J.; 2011. Pruned random subspace method for one-class classifiers. In: Multiple Classifier Systems. Lecture Notes in Computer Science, vol. 6713, pp. 96-105.
8. Cohen, G.; Sax, H.; Geissbuhler, A.; 2008. Novelty detection using one-class parzen density estimator. An application to surveillance of nosocomial infections. In: Studies in Health Technology and Informatics, vol. 136, pp. 21-26.
9. B. Cyganek One-class support vector ensembles for image segmentation and classification J. Math. Imaging Vision 42 2-3 2012 103 117
10. E. Czogala, and J. Leski Application of entropy and energy measures of fuzziness to processing of ECG signal Fuzzy Sets Syst. 97 1 1998 9 18
11. J. Demšar Statistical comparisons of classifiers over multiple data sets J. Mach. Learn. Res. 7 2006 1 30
12. T.G. Dietterich, and G. Bakiri Solving multiclass learning problems via error-correcting output codes J. Artif. Intell. Res. 2 1995 263 286
13. Duan, K.; Keerthi, S.S.; Chu, W.; Shevade, S.K.; Poo, A.N.; 2003. Multi-category classification by soft-max combination of binary classifiers. In: Proceedings of the 4th International Conference on Multiple Classifier Systems, MCS'03. Springer-Verlag, Berlin, Heidelberg, pp. 125-134.
14. 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, 〈 http://globocan.iarc.fr 〉, accessed on 30/08/2012.
15. P. Filipczuk, M. Kowal, and A. Obuchowicz Automatic breast cancer diagnostics based on k-means clustering and adaptive thresholding hybrid segmentation Ryszard S. Choraś, Image Processing and Communications Challenges, vol. 3, Advances in Intelligent and Soft Computing, vol. 102 2011 Springer-Verlag Berlin, Heidelberg 295 303
16. P. Filipczuk, M. Kowal, and A. Obuchowicz Fuzzy clustering and adaptive thresholding based segmentation method for breast cancer diagnosis M.W. Red, R. Burduk, M. Kurzyński, A. Żołnierek, Computer Recognition Systems, vol. 4. Advances in Intelligent and Soft Computing, vol. 95 2011 Springer-Verlag Berlin, Heidelberg 613 622
17. P. Filipczuk, W. Wojtak, and A. Obuchowicz Automatic nuclei detection on cytological images using the firefly optimization algorithm Lecture Notes in Bioinformatics Subseries of Lecture Notes in Computer Science 7339 2012 85 92
18. Filipczuk, P.; Kowal, M.; Obuchowicz, A.; 2012b. Breast fibroadenoma automatic detection using k-means based hybrid segmentation method. In: IEEE International Symposium on Biomedical Imaging: from nano to macro - ISBI 2012. Bryan, Conference Management Service, Inc.; Barcelona, Spain, pp. 1623-1626 [CD-ROM].
19. P. Filipczuk, B. Krawczyk, and M. Woźniak Classifier ensemble for an effective cytological image analysis Pattern Recognition Lett. 34 14 2013 1748 1757
20. M. Galar, A. Fernandez, E. Barrenechea, H. Bustince, and F. Herrera An overview of ensemble methods for binary classifiers in multi-class problems experimental study on one-vs-one and one-vs-all schemes Pattern Recognition 44 8 2011 1761 1776
21. G. Giacinto, R. Perdisci, M. Del Rio, and F. Roli Intrusion detection in computer networks by a modular ensemble of one-class classifiers Inf. Fusion 9 2008 69 82
22. Y. Gong, G. Hu, Z. Pan, and Y. Zhou Radio transmitter verification based on elastic sparsity preserving projections and SVDD Int. J. Digital Content Technol. Appl. 5 10 2011 247 254
23. M. Harman, and P. McMinn A theoretical and empirical study of search-based testing local, global, and hybrid search IEEE Trans. Software Eng. 36 2 2010 226 247
24. Hempstalk, K.; Frank, E.; 2008. Discriminating against new classes: one-class versus multi-class classification. In: Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence, pp. 325-336.
25. T.K. Ho The random subspace method for constructing decision forests IEEE Trans. Pattern Anal. Mach. Intell. 20 1998 832 844
26. L. Jeleń, T. Fevens, and A. Krzyżak Classification of breast cancer malignancy using cytological images of fine needle aspiration biopsies Int. J. Appl. Math Comput. Sci. 18 1 2010 75 83
27. H. Jiang, G. Liu, X. Xiao, C. Mei, Y. Ding, and S. Yu Monitoring of solid-state fermentation of wheat straw in a pilot scale using FT-NIR spectroscopy and support vector data description Microchem. J. 102 2012
28. Knowles, J.; Corne, D.; 2004. Memetic algorithms for multiobjective optimization: issues, methods and prospects. In: Recent Advances in Memetic Algorithms. IEEE Press, pp. 325-332.
29. M.W. Koch, M.M. Moya, L.D. Hostetler, and R.J. Fogler Cueing, feature discovery, and one-class learning for synthetic aperture radar automatic target recognition Neural Networks 8 7-8 1995 1081 1102
30. M. Kowal, P. Filipczuk, A. Obuchowicz, and J. Korbicz Computer-aided diagnosis of breast cancer using Gaussian mixture cytological image segmentation J. Med. Inf. Technol. 17 2011 257 262
31. Krawczyk, B.; 2012. Diversity in ensembles for one-class classification. In: Current Developments in Databases and Information Systems. Advances in Intelligent and Soft Computing. Springer, Berlin/Heidelberg, pp. 119-129.
32. B. Krawczyk, and M. Woźniak Combining diverse one-class classifiers E. Corchado, V. Snasel, A. Abraham, M. Wozniak, M. Grana, S.-B. Cho, Hybrid Artificial Intelligent Systems, Lecture Notes in Computer Science vol. 7209 2012 Springer Berlin/Heidelberg 590 601
33. Krawczyk, B.; Woźniak, M.; 2012b. Experiments on distance measures for combining one-class classifiers. In: Proceedings of the Federated Conference on Computer Science and Information Systems FEDCSIS, pp. 88-92.
34. Krawczyk, B.; Woźniak, M.; Diversity measures for one-class classifier ensembles, Neurocomputing, 10.1016/j.neucom.2013.01.053, in press.
35. B. Krawczyk, P. Filipczuk, and M. Woźniak Adaptive splitting and selection algorithm for classification of breast cytology images N.-T. Nguyen, K. Hoang, P. Jedrzejowicz, Computational Collective Intelligence. Technologies and Applications. Lecture Notes in Computer Science vol. 7653 2012 Springer Berlin, Heidelberg 475 484
36. L. Kuncheva, J.C. Bezdek, and R.P.W. Duin Decision templates for multiple classifier fusion: an experimental comparison Pattern Recognition 34 2 2001 299 314
37. Li, C.; Zhang, Y.; 2008. Bagging one-class decision trees. In: Proceedings of the 5th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2008, vol. 2, pp. 420-423.
38. B. Liu, D. Zhao, P. Reynaert, and G.G.E. Gielen Synthesis of integrated passive components for high-frequency RF ICS based on evolutionary computation and machine learning techniques IEEE Trans. Comput.-Aided Design Integrated Circuits Syst. 30 10 2011 1458 1468
39. J. Malek, A. Sebri, S. Mabrouk, K. Torki, and R. Tourki Automated breast cancer diagnosis based on GVF-Snake segmentation, wavelet features extraction and fuzzy classification J. Signal Process. Syst. 55 2009 49 66
40. L. Manevitz, and M. Yousef One-class document classification via neural networks Neurocomputing 70 7-9 2007 1466 1481
41. F. Meyer Topographic distance and watershed lines Signal Process. 38 1 1994 113 125
42. Niwas, I.S.; Palanisamy, P.; Sujathan, K.; 2010. Wavelet based feature extraction method for breast cancer cytology images. In: 2010 IEEE Symposium on Industrial Electronics & Applications (ISIEA), pp. 686-690.
43. M. Plissiti, C. Nikou, and A. Charchanti Automated detection of cell nuclei in pap smear images using morphological reconstruction and clustering IEEE Trans. Inf. Technol. Biomed. 15 2 2011 233 241
44. J. Śmietański, R. Tadeusiewicz, and E. Łuczyń ska Texture analysis in perfusion images of prostate cancer - a case study Int. J. Appl. Math. Comput. Sci. 20 1 2010 149 156
45. D.M.J. Tax, and R.P.W. Duin Support vector data description Mach. Learn. 54 1 2004 45 66
46. Tax, D.; Duin, R.P.W.; 2005. Characterizing one-class datasets. In: Proceedings of the 16th Annual Symposium of the Pattern Recognition Association of South Africa, pp. 21-26.
47. R.D.C. Team, 2008. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
48. J.C.E. Underwood Introduction to Biopsy Interpretation and Surgical Pathology 1987 Springer-Verlag London
49. T. Wilk, and M. Woźniak Complexity and multithreaded implementation analysis of one class-classifiers fuzzy combiner E. Corchado, M. Kurzynski, M. Wozniak, Hybrid Artificial Intelligent Systems, Lecture Notes in Computer Science vol. 6679 2011 Springer Berlin/Heidelberg 237 244
50. T. Wilk, and M. Woźniak Soft computing methods applied to combination of one-class classifiers Neurocomputing 75 2012 185 193
51. T. Windeatt, and R. Ghaderi Coding and decoding strategies for multi-class learning problems Inf. Fusion 4 1 2003 11 21
52. D.H. Wolpert, and W.G. Macready No free lunch theorems for optimization IEEE Trans. Evol. Comput. 1 1 1997 67 82
53. M. Woźniak, and B. Krawczyk Combined classifier based on feature space partitioning J. Appl. Math. Comput. Sci. 22 4 2012 855 866
54. X. Yang Nature-Inspired Metaheuristic Algorithms 2nd edition 2010 Luniver Press Frome, UK
55. Yang, X.; 2010b. Firefly algorithm, lévy flights and global optimization. In: Research and Development in Intelligent Systems, vol. XXVI. Springer, pp. 209-218.
56. X. Yang, H. Li, and X. Zhou Nuclei segmentation using marker-controlled watershed, tracking using mean-shift, and Kalman filter in time-lapse microscopy IEEE Trans. Circuits Syst. I 53 11 2006 2405 2414
57. C. Yeh, Z. Lee, and S. Lee Boosting one-class support vector machines for multi-class classification Appl. Artif. Intell. 23 4 2009 297 315