Classification of breast cancer malignancy using cytological images of fine needle aspiration biopsies

• Autorzy: Thomas Fevens , Adam Krzyżak
• Języki publikacji: EN

Abstrakty

EN

According to the World Health Organization (WHO), breast cancer (BC) is one of the most deadly cancers diagnosed among middle-aged women. Precise diagnosis and prognosis are crucial to reduce the high death rate. In this paper we present a framework for automatic malignancy grading of fine needle aspiration biopsy tissue. The malignancy grade is one of the most important factors taken into consideration during the prediction of cancer behavior after the treatment. Our framework is based on a classification using Support Vector Machines (SVM). The SVMs presented here are able to assign a malignancy grade based on preextracted features with the accuracy up to 94.24%. We also show that SVMs performed best out of four tested classifiers.

Słowa kluczowe

EN

automated malignancy grading; FNA grading; SVM; breast cancer grading; Bloom-Richardson

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2008
• Tom: 18
• Numer: 1
• Strony: 75-83

Daty

wydano

2008

Twórcy

autor

Thomas Fevens

• Department of Computer Science and Software Engineering, Concordia University, 1455 de Maisonneuve Blvd. West, Montréal, Québec, H3G 1M8, Canada

autor

Adam Krzyżak

• Department of Computer Science and Software Engineering, Concordia University, 1455 de Maisonneuve Blvd. West, Montréal, Québec, H3G 1M8, Canada

Bibliografia

• Ballard H. (1981). Generalizing the Hough transform to detect arbitrary Shapes, Pattern Recognition 13(2): 111-122.
• Bloom H. and Richardson W. (1957). Histological grading and prognosis in breast cancer, British Journal of Cancer, 11(3): 359-377.
• Bradley A. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms, Pattern Recognition 30(7): 1145-1159.
• Deng J. and Tsui H. (2002). A fast level set method for segmentation of low contrast noisy biomedical images, Pattern Recognition Letters 23(1-3): 161-169.
• Droske M., Meyer B., Rumpf M. and K. S. (2001). An adaptive level set method for medical image segmentation, Lecture Notes in Computer Science 2082: 416-422.
• Duda R., Hart P. and Stork D. (2000). Pattern Classification, 2nd edn, Wiley Interscience Publishers, New York.
• Friess T., Cristianini N. and Campbell C. (1998). The kernel adatron algorithm: A fast and simple learning procedure for support vector machines, Proceedings the 15th International Conference on Machine Learning, Morgan Kaufman Publishers, San Francisco, USA, pp. 188-196.
• Jeleń Ł., Krzyżak A. and Fevens T. (2006). Automated feature extraction for breast cancer grading with BloomRichardson scheme, International Journal of Computer Assisted Radiology and Surgery 1(1): 468-469.
• Kohonen T. (1990). The self-organizing map, Proceedings of Institute of Electrical and Electronics Engineers, 78(9): 1464-1480.
• Lee K. and Street W. (2000). Generalized Hough transforms with flexible templates, Proceedings of International Conference on Artificial Inteligence, Las Vegas, NV, pp. 1133-1139.
• Li C., Xu C., Gui C. and Fox M. (2005). Level set evolution without re-initialization: A new variational formulation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2005, San Diego, USA, pp. 430-436.
• Li S., Fevens T., Krzyżak A., Jin C. and Li S. (2006). Fast and robust clinical triple-region image segmentation using one level set function, Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention, Copenhagen, Denmark, 2: pp. 766-773.
• Nezafat R., Tabesh A., Akhavan S., Lucas C. and Zia M. (1998). Feature selection and classification for diagnosing breast cancer, Proceedings of International Association of Science and Technology for Development International Conference, Cancun, Mexico, pp. 310-313.
• Oja E. (1982). A siplified neuron modeled as a principal component analyzer, Journal of Mathematical Biology 15(3): 267-273.
• Osher S. and Sethian J. (1988). Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations, Journal of Computational Physics 79: 12-49.
• Ridler T. and Calvard S. (1978). Picture thresholding using an iterative selection, IEEE Transactions on System, Man and Cybernetics 8: 630-632.
• Schnorrenberg F., Pattichis C., Kyriacou K. and Schizas C. (1994). Detection of cell nuclei in breast cancer biopsies using receptive fields, IEEE Proceedings of Engineering in Medicine and Biology Society, pp. 649-650.
• Sethian J. and Adalsteinsson D. (1997). An overview of level set methods for etching, deposition and lithography development, IEEE Transactions on Semiconductor Manufacturing 10(1): 167-184.
• Street N. (2000). Xcyt: A system for remote cytological diagnosis and prognosis of breast cancer, in L. Jain (Ed.), Soft Computing Techniques in Breast Cancer Prognosis and Diagnosis, World Scientific Publishing, Singapore, pp. 297-322.
• Street W. N., Wolberg W. H. and Mangasarian O. L. (1993). Nuclear feature extraction for breast tumor diagnosis, Proceedings of the International Symposium on Electronic Imaging: Science and Technology, Vol. 1905, San Jose, CA, USA, pp. 861-870.
• Tsai A., Yezzi A., Wells III W., Tempany C., Tucker D., Fan A., Grimson W. and Willsky A. (2003). A shape-based approach to the segmentation of medical imagery using level sets, Medical Imaging 22(2): 137-154.
• Walker H. J. and Albertelli L. (1998). Breast cancer screening using evolved neural networks, Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, San Diego, USA, pp. 1619-1624.
• Walker H. J., Albertelli L., Titkov Y., Kaltsatis P. and Seburyano G. (1998). Evolution of neural networks for the detection of breast cancer, Proceedings of International Joint Symposia on Inteligence and Systems pp. 34-40.
• Wolberg W. H., Street W. N. and Mangasarian O. L. (1994). Machine learning techniques to diagnose breast cancer from image-processed nuclear features of fine needle aspirates, Cancer Letters 77: 163-171.
• Wolberg W. and Mangasarian O. (1990). Multisurface method of pattern separation for medical diagnosis applied to breast cytology, Proceedings of the National Academy of Science, USA 87(23): 9193-9196.
• Zunic J., and Rosin P. (2002). A convexity measurement for polygons., Proceedings of the British Machine Vision Conference Cardiff, UK, 24: 173-182.

Identyfikatory

DOI

10.2478/v10006-008-0007-x