Filtros de Resposta Máxima e Padrões Binários Locais Aplicados na Detecção de Anormalidades em Mamografias
Resumo
O câncer de mama é um tumor maligno que se desenvolve como consequ ência de alterações genéticas em um conjunto de células da mama. Neste trabalho utilizamos a análise de textura, uma das subáreas da visão computacional, para auxiliar na detecção de prováveis nódulos mamários. A suposição é que a textura de regiões com nódulos seja diferente da textura de regiões normais. Em análise de textura, uma técnica que se destaca são os padrões binários locais (LBP). Embora o LBP obtenha bons resultados em imagens de textura, seu desempenho foi drasticamente baixo em imagens de mamografia. Para contornar esse problema, esse artigo propõe o uso dos filtros de resposta máxima e o LBP para a classificação de nódulos em mamografias. O método proposto obteve uma acurácia de 88% comparado com apenas 68% do LBP.
Referências
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Heath, M., Bowyer, K., Kopans, D., Kegelmeyer Jr, P., Moore, R., Chang, K., and Munishkumaran, S. (1998). Current status of the digital database for screening mammography. In Digital mammography, pages 457–460. Springer.
Hussain, M. (2013). False positive reduction using gabor feature subset selection. In Information Science and Applications (ICISA), 2013 International Conference on, pages 1–5. IEEE.
INCA (2014). http://www2.inca.gov.br/wps/wcm/connect/acoes programas/site/home/nobrasil/ programa controle cancer mama/conceito magnitude. Access date: 27 nov. 2014.
Kitanovski, I., Jankulovski, B., Dimitrovski, I., and Loskovska, S. (2011). Comparison of feature extraction algorithms for mammography images. In Image and Signal Processing (CISP), 2011 4th International Congress on, volume 2, pages 888–892. IEEE.
Liu, J., Liu, X., Chen, J., and Tang, J. (2011). Improved local binary patterns for classification of masses using mammography. In Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on, pages 2692–2695. IEEE.
Mudigonda, N. R., Rangayyan, R. M., and Desautels, J. L. (2000). Gradient and texture analysis for the classification of mammographic masses. Medical Imaging, IEEE Transactions on, 19(10):1032–1043.
Nanni, L., Lumini, A., and Brahnam, S. (2012). Survey on lbp based texture descriptors for image classification. Expert Systems with Applications, 39(3):3634–3641.
Ojala, T., Pietikäinen, M., and Harwood, D. (1996). A comparative study of texture measures with classification based on featured distributions. Pattern recognition, 29(1):51– 59.
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Rangayyan, R. M., Ayres, F. J., and Leo Desautels, J. (2007). A review of computeraided diagnosis of breast cancer: Toward the detection of subtle signs. Journal of the Franklin Institute, 344(3):312–348.
Suckling, J., Parker, J., Dance, D., Astley, S., Hutt, I., Boggis, C., Ricketts, I., Stamatakis, E., Cerneaz, N., Kok, S.-L., et al. (1994). The mammographic image analysis society digital mammogram database.
Thurfjell, E. L., Lernevall, K. A., and Taube, A. (1994). Benefit of independent double reading in a population-based mammography screening program. Radiology, 191(1):241–244.
Varma, M. and Zisserman, A. (2003). Texture classification: Are filter banks necessary? In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pages 691–698.
Verma, B. and Zakos, J. (2001). A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques. Information Technology in Biomedicine, IEEE Transactions on, 5(1):46–54.
Vyborny, C. J. and Giger, M. L. (1994). Computer vision and artificial intelligence in mammography. AJR. American journal of roentgenology, 162(3):699–708.
Zhang, H. (2004). The optimality of naive bayes. AA, 1(2):3.
Duda, R. O., Hart, P. E., and Stork, D. G. (2012). Pattern classification. John Wiley & Sons.
Heath, M., Bowyer, K., Kopans, D., Kegelmeyer Jr, P., Moore, R., Chang, K., and Munishkumaran, S. (1998). Current status of the digital database for screening mammography. In Digital mammography, pages 457–460. Springer.
Hussain, M. (2013). False positive reduction using gabor feature subset selection. In Information Science and Applications (ICISA), 2013 International Conference on, pages 1–5. IEEE.
INCA (2014). http://www2.inca.gov.br/wps/wcm/connect/acoes programas/site/home/nobrasil/ programa controle cancer mama/conceito magnitude. Access date: 27 nov. 2014.
Kitanovski, I., Jankulovski, B., Dimitrovski, I., and Loskovska, S. (2011). Comparison of feature extraction algorithms for mammography images. In Image and Signal Processing (CISP), 2011 4th International Congress on, volume 2, pages 888–892. IEEE.
Liu, J., Liu, X., Chen, J., and Tang, J. (2011). Improved local binary patterns for classification of masses using mammography. In Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on, pages 2692–2695. IEEE.
Mudigonda, N. R., Rangayyan, R. M., and Desautels, J. L. (2000). Gradient and texture analysis for the classification of mammographic masses. Medical Imaging, IEEE Transactions on, 19(10):1032–1043.
Nanni, L., Lumini, A., and Brahnam, S. (2012). Survey on lbp based texture descriptors for image classification. Expert Systems with Applications, 39(3):3634–3641.
Ojala, T., Pietikäinen, M., and Harwood, D. (1996). A comparative study of texture measures with classification based on featured distributions. Pattern recognition, 29(1):51– 59.
OMS (2014). http://wwwj
Rangayyan, R. M., Ayres, F. J., and Leo Desautels, J. (2007). A review of computeraided diagnosis of breast cancer: Toward the detection of subtle signs. Journal of the Franklin Institute, 344(3):312–348.
Suckling, J., Parker, J., Dance, D., Astley, S., Hutt, I., Boggis, C., Ricketts, I., Stamatakis, E., Cerneaz, N., Kok, S.-L., et al. (1994). The mammographic image analysis society digital mammogram database.
Thurfjell, E. L., Lernevall, K. A., and Taube, A. (1994). Benefit of independent double reading in a population-based mammography screening program. Radiology, 191(1):241–244.
Varma, M. and Zisserman, A. (2003). Texture classification: Are filter banks necessary? In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pages 691–698.
Verma, B. and Zakos, J. (2001). A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques. Information Technology in Biomedicine, IEEE Transactions on, 5(1):46–54.
Vyborny, C. J. and Giger, M. L. (1994). Computer vision and artificial intelligence in mammography. AJR. American journal of roentgenology, 162(3):699–708.
Zhang, H. (2004). The optimality of naive bayes. AA, 1(2):3.
Publicado
20/07/2015
Como Citar
FISTAROL, Danilo; GONÇALVES, Wesley.
Filtros de Resposta Máxima e Padrões Binários Locais Aplicados na Detecção de Anormalidades em Mamografias. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 15. , 2015, Recife.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2015
.
p. 101-110.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2015.10370.
