Segmentation and Classification of Breast Cancer Using Independent Component Analysis, Texture Features and Neural Networks
Resumo
We propose a method for segmentation and classification of breast cancer in digital mammograms using Independent Component Analysis (ICA), Texture Features and Multilayer Perceptron (MLP) Neural Networks. The method was tested for a mammogram set from MIAS database, resulting in 90.15% success rate, with 92% of specificity and 88.3% of sensitivity.Referências
Braz , G. Jr., E. C. Silva, A. C. Paiva and A. C. Silva, “Breast Tissues Classification Based on the Application of Geostatistical Features and Wavelet Transform”, In: International Special Topic Conference on Information Technology Applications in Biomedicine, ITAB 2007, 6th, 227-230, 2007.
Bick U., M. Giger, R. Schmidt, R. Nishikawa, D. Wolverton and K. Doi. “Computer-aided breast cancer detection in screening mammography”, In: Digital Mammogr'9Chicago, Il (1996), pp. 97–103.
Bishop, C.M.: “Neural Networks for Pattern Recognition”. Oxford University Press, New York (1999) Bocchi L, G. Coppini, R. De Dominicis and G. Valli, Tissue characterization from X-ray images, Med Eng Phys 1997;19(4); pp. 336–342.
Campos, L. F. A. ; Barros, A. K. ; Silva, A. C. “Independent Component Analysis and Neural Networks Applied for Classification of Malignant, Benign and Normal Tissue in Digital Mammography”, In: Special Issue - Methods of Information in Medicine, v. 46, p. 212-215, 2007.
Campos, L. F. A. ;Costa, D. D.; Barros, A. K. “Segmentation on Breast Cancer Using Texture Features and Independent Component Analysis”, In: Bioinspired Cognitive Systems, BICS 2008.
Christoyianni I., Koutras A., Kokkinakis G., “Computer aided diagnosis of breast cancer in digitized mammograms”, In: Comp. Med. Imag. & Graph., 26:309-319, 2002.
Domínguez, A. Rojas. Nandi,A. K. “Detection of masses in mammograms using enhanced multilevel thresholding segmentation and region selection based on rank”, In: Proceedings of the fifth conference on Proceeding of the Fifth IASTED International Conference: biomedical engineering. 2007
Duda, R.O., Hart, P.E.: “Pattern Classification and Scene Analysis”, In: Wiley-Interscience Publication, New York (1973) Fukunaga, K.: “Introduction to Statistical Pattern Recognition”. 2nd ed. London: AcademicPress. 1990.
Homer M. J, “Imaging features and management of characteristically benign and probably benign lesions”, In: RadiolClin N Am 1987; 25 (5); pp. 939–951.
Homer M. J, “Breast imaging: Pitfalls, controversies and some practical thoughts”, In: Radiol Clin N Am 23 (1985) (3), pp. 459–472.
Hyvärinen A. and E. Oja. “A fast fixed-point algorithm for independent component analysis”, In: Neural Computation, 9(7):1483-1492, 1997.
INCa, Internet site address: [link] accessed in 04/12/2010.
Jain, A.K, F. Farrokhnia, “Unsupervised texture segmentation using Gabor filters”, In: Pattern Recognition 24 (12) (1991) 1167–1186. 2001
Marchini J. L, Heaton C, Ripley B D. “FastICA algorithms to perform ICA and Projection Pursuit”. (2004) Available at [link]
Meyer J. E. , E. Amin, K.K. Lindfors, J.C. Lipman, P.C. Stomper and D. Genest, “Medulary carcinoma of the breast: Mammographic and US appearance”, In: Radiology, 1989; 79-82
Moore A.: “K-means and Hierarchical Clustering -Tutorial Slides”, 2001. Internet site address: [link]
Newstead G. M, P.B. Baute and H.K. Toth, “Invasive lobular and ductal carcinoma: Mammographic findings and stage at diagnosis”, In: Radiology 1992; 184; pp. 623–627.
Sickles E. A.: “Breast masses: mammographic evaluation”, In: Radiology 1989; 173; pp. 297–303.
Suckling, J et al (1994): “The Mammographic Image Analysis Society Digital Mammogram Database”, In: Exerpta Medica. International Congress Series 1069 pp375-378.
Unser M, M. Eden, “Nonlinear operators for improving texture segmentation based on features extracted by spatial filtering”, In: IEEE Trans. Syst. Man Cybern. 20 (1990) 804–815.
Bick U., M. Giger, R. Schmidt, R. Nishikawa, D. Wolverton and K. Doi. “Computer-aided breast cancer detection in screening mammography”, In: Digital Mammogr'9Chicago, Il (1996), pp. 97–103.
Bishop, C.M.: “Neural Networks for Pattern Recognition”. Oxford University Press, New York (1999) Bocchi L, G. Coppini, R. De Dominicis and G. Valli, Tissue characterization from X-ray images, Med Eng Phys 1997;19(4); pp. 336–342.
Campos, L. F. A. ; Barros, A. K. ; Silva, A. C. “Independent Component Analysis and Neural Networks Applied for Classification of Malignant, Benign and Normal Tissue in Digital Mammography”, In: Special Issue - Methods of Information in Medicine, v. 46, p. 212-215, 2007.
Campos, L. F. A. ;Costa, D. D.; Barros, A. K. “Segmentation on Breast Cancer Using Texture Features and Independent Component Analysis”, In: Bioinspired Cognitive Systems, BICS 2008.
Christoyianni I., Koutras A., Kokkinakis G., “Computer aided diagnosis of breast cancer in digitized mammograms”, In: Comp. Med. Imag. & Graph., 26:309-319, 2002.
Domínguez, A. Rojas. Nandi,A. K. “Detection of masses in mammograms using enhanced multilevel thresholding segmentation and region selection based on rank”, In: Proceedings of the fifth conference on Proceeding of the Fifth IASTED International Conference: biomedical engineering. 2007
Duda, R.O., Hart, P.E.: “Pattern Classification and Scene Analysis”, In: Wiley-Interscience Publication, New York (1973) Fukunaga, K.: “Introduction to Statistical Pattern Recognition”. 2nd ed. London: AcademicPress. 1990.
Homer M. J, “Imaging features and management of characteristically benign and probably benign lesions”, In: RadiolClin N Am 1987; 25 (5); pp. 939–951.
Homer M. J, “Breast imaging: Pitfalls, controversies and some practical thoughts”, In: Radiol Clin N Am 23 (1985) (3), pp. 459–472.
Hyvärinen A. and E. Oja. “A fast fixed-point algorithm for independent component analysis”, In: Neural Computation, 9(7):1483-1492, 1997.
INCa, Internet site address: [link] accessed in 04/12/2010.
Jain, A.K, F. Farrokhnia, “Unsupervised texture segmentation using Gabor filters”, In: Pattern Recognition 24 (12) (1991) 1167–1186. 2001
Marchini J. L, Heaton C, Ripley B D. “FastICA algorithms to perform ICA and Projection Pursuit”. (2004) Available at [link]
Meyer J. E. , E. Amin, K.K. Lindfors, J.C. Lipman, P.C. Stomper and D. Genest, “Medulary carcinoma of the breast: Mammographic and US appearance”, In: Radiology, 1989; 79-82
Moore A.: “K-means and Hierarchical Clustering -Tutorial Slides”, 2001. Internet site address: [link]
Newstead G. M, P.B. Baute and H.K. Toth, “Invasive lobular and ductal carcinoma: Mammographic findings and stage at diagnosis”, In: Radiology 1992; 184; pp. 623–627.
Sickles E. A.: “Breast masses: mammographic evaluation”, In: Radiology 1989; 173; pp. 297–303.
Suckling, J et al (1994): “The Mammographic Image Analysis Society Digital Mammogram Database”, In: Exerpta Medica. International Congress Series 1069 pp375-378.
Unser M, M. Eden, “Nonlinear operators for improving texture segmentation based on features extracted by spatial filtering”, In: IEEE Trans. Syst. Man Cybern. 20 (1990) 804–815.
Publicado
19/07/2011
Como Citar
CAMPOS, Lucio Flávio A.; LEMOS, Emanuel C. M.; SILVA, Luis C. O.; COSTA, Daniel D.; BARROS, Allan Kardec.
Segmentation and Classification of Breast Cancer Using Independent Component Analysis, Texture Features and Neural Networks. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 11. , 2011, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2011
.
p. 1764-1773.
ISSN 2763-8952.
