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Breast Tissue Image Classification Based on Semi-supervised Locality Discriminant Projection with Kernels

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Abstract

Breast tissue classification is an important and effective way for computer aided diagnosis of breast cancer. We present Semi-supervised Locality Discriminant Projections with Kernels for breast cancer classification. The contributions of this work lie in: 1) Semi-supervised learning is used into Locality Preserving Projections (LPP) to enhance its performance using side-information together with the unlabelled training samples, while current algorithms only consider the side-information but ignoring the unlabeled training samples. 2) Kernel trick is applied into Semi-supervised LPP to improve its ability in the nonlinear classification. 3) The framework of breast cancer classification with Semi-supervised LPP with kernels is presented. Many experiments are implemented on four breast tissue databases to testify and evaluate the feasibility and affectivity of the proposed scheme.

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References

  1. http://seer.cancer.gov/cgi-bin/csr/1975_2001/search.pl#results, Estimated New Cancer Cases and Deaths for 2004.

  2. U.S. National Institutes of Health, National Cancer Institute, http://cancernet.nci.nih.gov/.

  3. Huo, Z., Giger, M., Vyborny, C., Wolverton, D., Schmidt, R., and Doi, K., Automated computerized classification of malignant and benign mass lesions on digital mammograms. Acad. Radiol. 5:155–168, 1998.

    Article  Google Scholar 

  4. Setiono, R., Generating concise and accurate classification rules for breast cancer diagnosis. Artif. Intell. Med. 18:205–219, 2000.

    Article  Google Scholar 

  5. Tourassi, G. D., Markey, M. K., Lo, J. Y., and Floyd, C. E., Jr., A neural network approach to breast cancer diagnosis as a constraint satisfaction problem. Med. Phys. 28:804–811, 2001.

    Article  Google Scholar 

  6. Chen, D., Chang, R. F., and Huang, Y. L., Breast cancer diagnosis using self-organizing map for sonography. Ultrasound Med. Biol. 26:405–411, 2000.

    Article  Google Scholar 

  7. Belhumeur, P. N., Hespanha, J. P., and Kriegman, D. J., Eigenfaces vs fisherfaces: Recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7):711–720, 1997.

    Article  Google Scholar 

  8. Batur, A. U., and Hayes, M. H., Linear subspace for illumination robust face recognition. Proc. IEEE Int’l Conf. Comput. Vis. Pattern Recogn. 296–301, 2001.

  9. Hastie, T., and Stuetzle, W., Principal curves. J. Am. Stat. Assoc. 84:502–516, 1989.

    Article  MathSciNet  MATH  Google Scholar 

  10. Chang, K.-Y., and Ghosh, J., A unified model for probabilistic principal surfaces. IEEE Trans. Pattern Anal. Mach. Intell. 23(1):22–41, 2001.

    Article  Google Scholar 

  11. Mulier, F., and Cherkassky, V., Self-organization as an iterative kernel smoothing process. Neural Comput. 7:1165–1177, 1995.

    Article  Google Scholar 

  12. Ritter, H., Martinetz, T., and Schulten, K., Neural computation and self-organizing maps. Addison-Wesley, 64–72, 1992.

  13. Zhu, Z., He, H., Starzyk, J. A., and Tseng, C., Self-organizing learning array and its application to economic and financial problems. Inform. Sci. 177(5):1180–1192, 2007.

    Article  Google Scholar 

  14. Yin, H., Data visualisation and manifold mapping using the ViSOM. Neural Netw. 15(8):1005–1016, 2002.

    Article  Google Scholar 

  15. Tenenbaum, J. B., de Silva, V., and Langford, J. C., A global geometric framework for nonlinear dimensionality reduction. Science 290:2319–2323, 2000.

    Article  Google Scholar 

  16. Roweis, S. T., Saul, L. K., and Dimensionality, N., Reduction by locally linear embedding. Science 290:2323–2326, 2000.

    Article  Google Scholar 

  17. He, X., and Niyogi, P., Locality preserving projections. Proc. Conf. Advances in Neural Information Processing Systems pp. 585–591, 2003.

  18. Shental, N., Hertz, T., Weinshall, D., and Pavel, M., Adjustment learning and relevant component analysis. Proceeding of the 7th European conference on computer vision, pp. 776–792, 2002.

  19. BarHillel, A., Hertz, T., Shental, M., and Weinshall, D., Learning a Mahalanobis metric from equivalence constrains. J. Mach. Learn. Res. 6(6):937–965, 2005.

    MathSciNet  Google Scholar 

  20. Xing, E. P., Jordan, M. I., and Russell, S., Distance metric learning with application to clustering with side-information. Adv. Neural Inform. Process. Syst. pp 505–512, MIT Press, 2003:

  21. Tang, W., and Zhong, S., Pairwise constraints-guided dimensionality reduction. Proc. Data mining workshop on feature selection for data mining. pp. 59–66, 2006.

  22. Zhang, D. Q., Zhou, Z. H., and Chen, S. C., Semi-supervised dimensionality reduction. Proc 7th SIAM International Conference on Data Mining, pp. 629–634. 2007.

  23. Van Gestel, T., Baesens, B., and Martens, D., From linear to non-linear kernel based classifiers for bankruptcy prediction. Neurocomputing 73(16–18):2955–2970, 2010.

    Article  Google Scholar 

  24. Zhua, Q., Reformative nonlinear feature extraction using kernel MSE. Neurocomputing 73(16–18):3334–3337, 2010.

    Article  Google Scholar 

  25. Veerabhadrappa, and Rangarajan, L., Diagonal and secondary diagonal locality preserving projection for object recognition. Neurocomputing 73(16–18):3328–3333, 2010.

    Article  Google Scholar 

  26. Zhang, L., Qiao, L., and Chen, S., Graph-optimized locality preserving projections. Pattern Recogn. 43(6):1993–2002, 2010.

    Article  MATH  Google Scholar 

  27. Wang, J., Zhang, B., Wang, S., Qi, M., and Kong, J., An adaptively weighted sub-pattern locality preserving projection for face recognition. J. Netw. Comput. Appl. 33(3):323–332, 2010.

    Article  Google Scholar 

  28. Gui-Fu, Lu, Lin, Z., and Jin, Z., Face recognition using discriminant locality preserving projections based on maximum margin criterion. Pattern Recogn. 43(10):3572–3579, 2010.

    Article  MATH  Google Scholar 

  29. Wang, X., Chung, Fu-lai, and Wang, S., On minimum class locality preserving variance support vector machine. Pattern Recogn. 43(8):2753–2762, 2010.

    Article  MATH  Google Scholar 

  30. Cheng, J., Liu, Q., Lua, H., and Chen, Y. W., Supervised kernel locality preserving projections for face recognition. Neurocomputing 67:443–449, 2005.

    Article  Google Scholar 

  31. Li, J. B., Pan, J. S., and Chu, S. C., Kernel class-wise locality preserving projection. Inform. Sci. 178(7):1825–1835, 2008.

    Article  MATH  Google Scholar 

  32. Zhao, H., Sun, S., Jing, Z., and Yang, J., Local structure based supervised feature extraction. Pattern Recogn. 39(8):1546–1550, 2006.

    Article  MATH  Google Scholar 

  33. Jossinet, J., Variability of impedivity in normal and pathological breast tissue. Med. Biol. Eng. Comput. 34:346–350, 1996.

    Article  Google Scholar 

  34. Silva, J. E., Marques de Sá, J. P., and Jossinet, J., Classification of breast tissue by electrical impedance spectroscopy. Med. Biol. Eng. Comput. 38:26–30, 2000.

    Article  Google Scholar 

  35. Mangasarian, O. L., and Wolberg, W. H., Cancer diagnosis via linear programming. SIAM News 23(5):1–18, 1990.

    Google Scholar 

  36. Wolberg, W. H., Street, W. N., Heisey, D. M., and Mangasarian, O. L., Computer-derived nuclear features distinguish malignant from benign breast cytology. Hum. Pathol. 26:792–796, 1995.

    Article  Google Scholar 

  37. Street, W. N., Mangasarian, O. L., and Wolberg, W. H., An inductive learning approach to prognostic prediction. In: Prieditis, A., and Russell, S. (Eds.), Proceedings of the Twelfth International Conference on Machine Learning. Morgan Kaufmann, San Francisco, pp. 522–530, 1995.

    Google Scholar 

  38. Mangasarian, O. L., Street, W. N., and Wolberg, W. H., Breast cancer diagnosis and prognosis via linear programming. Oper. Res. 43(4):570–577, 1995.

    Article  MathSciNet  MATH  Google Scholar 

  39. Wolberg, W. H., Street, W. N., Heisey, D. M., and Mangasarian, O. L., Computerized breast cancer diagnosis and prognosis from fine needle aspirates. Arch. Surg. 130:511–516, 1995.

    Article  Google Scholar 

  40. Wolberg, W. H., Street, W. N., and Mangasarian, O. L., Image analysis and machine learning applied to breast cancer diagnosis and prognosis. Anal. Quant. Cytol. Histol. 17(2):77–87, 1995.

    Google Scholar 

  41. Krinidis, S., and Pitas, I., Statistical analysis of human facial expressions. Journal of Information Hiding and Multimedia Signal Processing 1(3):241–260, 2010.

    Google Scholar 

  42. Jun-Bao Li, Mammographic image based breast tissue classification with kernel self-optimized fisher discriminant for beast cancer diagnosis. J. Med. Syst. doi:10.1007/s10916-011-9691-4)

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Acknowledgments

This work is supported by National Science Foundation of China under Grant No. 61001165, Natural Science Foundation of Heilongjiang Province under Grant No. QC2010066. HIT Young Scholar Foundation of 985 Project.

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Authors and Affiliations

  1. Department of Automatic Test and Control, Harbin Institute of Technology, P. O. Box 3033, Room 426, Building 2A, HIT Sciences Park, No. 2 Yi-Kuang Street, Nan-Gang District, Harbin, 150080, China

    Jun-Bao Li, Yang Yu & Zhi-Ming Yang

  2. School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China

    Lin-Lin Tang

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  1. Jun-Bao Li
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  2. Yang Yu
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  3. Zhi-Ming Yang
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  4. Lin-Lin Tang
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Correspondence to Jun-Bao Li.

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Li, JB., Yu, Y., Yang, ZM. et al. Breast Tissue Image Classification Based on Semi-supervised Locality Discriminant Projection with Kernels. J Med Syst 36, 2779–2786 (2012). https://doi.org/10.1007/s10916-011-9754-6

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  • DOI: https://doi.org/10.1007/s10916-011-9754-6

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