Classification of Breast Cancer Malignancy Using Cytological Images of Fine Needle Aspiration Biopsies
By: Łukasz Jeleń, Thomas Fevens and Adam Krzyżak
Open Access
|Mar 2008References
- Ballard H. (1981). Generalizing the Hough transform to detect arbitrary Shapes,(2): 111-122.
- Bloom H. and Richardson W. (1957). Histological grading and prognosis in breast cancer,,(3): 359-377.
- Bradley A. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms,(7): 1145-1159.
- Deng J. and Tsui H. (2002). A fast level set method for segmentation of low contrast noisy biomedical images,(1-3): 161-169.
- Droske M., Meyer B., Rumpf M. and K. S. (2001). An adaptive level set method for medical image segmentation,: 416-422.
- Duda R., Hart P. and Stork D. (2000)., 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,, San Francisco, USA, pp. 188-196.
- Jeleń Ł., Krzyżak A. and Fevens T. (2006). Automated feature extraction for breast cancer grading with Bloom-Richardson scheme,(1): 468-469.
- Kohonen T. (1990). The self-organizing map,,(9): 1464-1480.
- Lee K. and Street W. (2000). Generalized Hough transforms with flexible templates,, pp. 1133-1139.
- Li C., Xu C., Gui C. and Fox M. (2005). Level set evolution without re-initialization: A new variational formulation,, 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,, Copenhagen, Denmark,: pp. 766-773.
- Nezafat R., Tabesh A., Akhavan S., Lucas C. and Zia M. (1998). Feature selection and classification for diagnosing breast cancer,, Cancun, Mexico, pp. 310-313.
- Oja E. (1982). A siplified neuron modeled as a principal component analyzer,(3): 267-273.
- Osher S. and Sethian J. (1988). Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations,: 12-49.
- Ridler T. and Calvard S. (1978). Picture thresholding using an iterative selection,: 630-632.
- Schnorrenberg F., Pattichis C., Kyriacou K. and Schizas C. (1994). Detection of cell nuclei in breast cancer biopsies using receptive fields,, pp. 649-650.
- Sethian J. and Adalsteinsson D. (1997). An overview of level set methods for etching, deposition and lithography development,(1): 167-184.
- Street N. (2000). Xcyt: A system for remote cytological diagnosis and prognosis of breast cancer,L. Jain (Ed.),, 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,, 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,(2): 137-154.
- Walker H. J. and Albertelli L. (1998). Breast cancer screening using evolved neural networks,, 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,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,: 163-171.
- Wolberg W. and Mangasarian O. (1990). Multisurface method of pattern separation for medical diagnosis applied to breast cytology,(23): 9193-9196.
- Zunic J., and Rosin P. (2002). A convexity measurement for polygons.,Cardiff, UK,: 173-182.
Language: English
Page range: 75 - 83
Published on: Mar 21, 2008
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Related subjects:
© 2008 Łukasz Jeleń, Thomas Fevens, Adam Krzyżak, published by University of Zielona Góra
This work is licensed under the Creative Commons License.