Algoritmo de extração de textura baseado em Wavelet e CLBP para classificação de lesões em mamogramas
Resumo
Este artigo apresenta um sistema de auxílio ao diagnóstico (CAD) para a classificação de lesões de mamografias. As ROI’s foram obtidas do banco de dados Digital Database for Screening Mammography (DDSM). A Transformada Wavelet foi utilizada na geração de imagens no domínio do espaço e da frequência. Após isto, o operador de Padrão Binário Local Completo (Completed Local Binary Pattern - CLBP) foi aplicado para a extração de características. Atributos redundantes foram retirados com a técnica de análise de variância (ANOVA). Por fim, os casos foram classificados com uma rede neural artificial de Função de Base Radial (RBF). Análises quantitativas por meio da curva sobre a área ROC chegaram a 100% de eficácia com o método proposto.
Referências
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Balleyguier, C., Ayadi, S., Vannguyen, K., Vanel, D., Dromain, C., and Sigal, R. (2007). Birads classification in mammography. European Journal of Radiology, 61(2):192–194.
Bianconi, F. and Fernández, A. (2011). On the occurrence probability of local binary patterns: A theoretical study. Journal of Mathematical Imaging and Vision, 40:259–268.
Dantas, R., Nascimento, M., Ramos, R., and Pereira, D. (2009). Análise das variações da matriz de co-corrência em imagens derivadas da transformada wavelet haar em mamográfia. XIV Congresso Brasileiro de Física Médica.
Dantas, R. D., Nascimento, M. Z., Jacomini, R. S., Pereira, D. C., and RAMOS, R. P. (2012). Fusion of two-view information: S v d based modeling for computerized classification of breast lesions on mammograms. In Mammography Recent Advances, pages 261–278. Intech.
de Souza Jacomini, R., do Nascimento, M., Dantas, R., and Ramos, R. (2012). Comparison of pca and anova for information selection of cc and mlo views in classification of mammograms. In Intelligent Data Engineering and Automated Learning - IDEAL 2012, volume 7435, pages 117–126. Springer Berlin / Heidelberg.
Destounis, S. V., DiNitto, P., Logan-Young, W., Bonaccio, E., Zuley, M. L., and Willison, K. M. (2004). Can computer-aided detection with double reading of screening mammograms help decrease the false-negative rate? initial experience. Radiology, 232(2):578.
Do Espirito Santo, R., De Deus Lopes, R., and Rangayyan, R. (2009). Classification of breast masses in mammograms using radial basis functions and simulated annealing. International Journal of Cognitive Informatics and Natural Intelligence, 3(3):27–38. cited By (since 1996)1.
Eltoukhy, M. M., Faye, I., and Samir, B. B. (2010). A comparison of wavelet and curvelet for breast cancer diagnosis in digital mammogram. Computer in Biology and Medicine, 40(4):384–391.
Ferreira, C. B. R. and Borges, D. L. (2005). Uma estratégia de seleção de um subconjunto mínimo de características wavelet em uma abordagem multirresolução para classificação de tumores em mamogramas. V Workshop de Informática Médica, 1.
Fonseca, J. D. (1981). Curso de estatística. Atlas.
Guo, Z., Zhang, D., and Zhang, D. (2010). A completed modeling of local binary pattern operator for texture classification. Image Processing, IEEE Transactions on, 19(6):1657–1663.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H. (2009). The WEKA Data Mining Software: An Update. SIGKDD Explorations, Volume 11, Issue 1.
Haykin, S. (1998). Neural Networks: A Comprehensive Foundation (2nd Edition). Prentice Hall, 2 edition.
Huang, D., Shan, C., Ardabilian, M., Wang, Y., and Chen, L. (2011). Local binary patterns and its application to facial image analysis: A survey. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 41(6):765 –781.
Junior, G. B., C., S. A., Paiva, A. C., and Oliveira, A. C. M. (2006). Identificação de massas em mamografias usando textura, geometria e algoritmos de agrupamento e classificação. VI Workshop de Informática Médica, 1:94–104.
Ko, B. C., Kim, S. H., and Nam, J. Y. (2011). X-ray image classification using random forests with local wavelet-based cs-local binary patterns. J Digit Imaging, 24(6):1141–1151.
Liu, X., You, X., and ming Cheung, Y. (2009). Texture image retrieval using nonseparable wavelets and local binary patterns. In Computational Intelligence and Security, 2009. CIS ’09. International Conference on, volume 1, pages 287–291.
Lladó, X., Oliver, A., Freixenet, J., Martí, R., and Martí, J. (2009). A textural approach for mass false positive reduction in mammography. Computerized Medical Imaging and Graphics, 33(6):415–422.
Mallat, S. (1999). A Wavelet Tour of Signal Processing. Academic Press, San Diego, CA.
MATLAB (2010). version 7.10.0 (R2010a). The MathWorks Inc., Natick, Massachusetts.
Misiti, M., Misiti, Y., Oppenheim, G., and Poggi, J. (2008). Matlab Wavelet Toolbox User’s Guide.
Nanni, L., Brahnam, S., and Lumini, A. (2012). A very high performing system to discriminate tissues in mammograms as benign and malignant. Expert Syst. Appl., 39(2):1968–1971.
Pedrini, H. and Schwartz, W. (2008). Análise de imagens digitais: princípios algoritmos e aplicações. São Paulo: Thomson Learning.
Ramos, R. P., do Nascimento, M. Z., and Pereira, D. C. (2012). Texture extraction: An evaluation of ridgelet, wavelet and co-occurrence based methods applied to mammograms. Expert Systems with Applications, 39(12):11036 – 11047.
Rangayyan, R., Prajna, S., Ayres, F., and Desautels, J. (2008). Detection of architectural distortion in prior screening mammograms using gabor filters, phase portraits, fractal dimension, and texture analysis. International Journal of Computer Assisted Radiology and Surgery, 2:347–361.
Rashed, E. A., Ismail, I. A., and Zaki, S. I. (2007). Multiresolution mammogram analysis in multilevel decomposition. Pattern Recognition Letters, 28(2):286 – 292.
Tang, J., Rangayyan, R., Xu, J., El Naqa, I., and Yang, Y. (2009). Computer-aided detection and diagnosis of breast cancer with mammography: Recent advances. Information Technology in Biomedicine, IEEE Transactions on, 13(2):236 –251.
Balleyguier, C., Ayadi, S., Vannguyen, K., Vanel, D., Dromain, C., and Sigal, R. (2007). Birads classification in mammography. European Journal of Radiology, 61(2):192–194.
Bianconi, F. and Fernández, A. (2011). On the occurrence probability of local binary patterns: A theoretical study. Journal of Mathematical Imaging and Vision, 40:259–268.
Dantas, R., Nascimento, M., Ramos, R., and Pereira, D. (2009). Análise das variações da matriz de co-corrência em imagens derivadas da transformada wavelet haar em mamográfia. XIV Congresso Brasileiro de Física Médica.
Dantas, R. D., Nascimento, M. Z., Jacomini, R. S., Pereira, D. C., and RAMOS, R. P. (2012). Fusion of two-view information: S v d based modeling for computerized classification of breast lesions on mammograms. In Mammography Recent Advances, pages 261–278. Intech.
de Souza Jacomini, R., do Nascimento, M., Dantas, R., and Ramos, R. (2012). Comparison of pca and anova for information selection of cc and mlo views in classification of mammograms. In Intelligent Data Engineering and Automated Learning - IDEAL 2012, volume 7435, pages 117–126. Springer Berlin / Heidelberg.
Destounis, S. V., DiNitto, P., Logan-Young, W., Bonaccio, E., Zuley, M. L., and Willison, K. M. (2004). Can computer-aided detection with double reading of screening mammograms help decrease the false-negative rate? initial experience. Radiology, 232(2):578.
Do Espirito Santo, R., De Deus Lopes, R., and Rangayyan, R. (2009). Classification of breast masses in mammograms using radial basis functions and simulated annealing. International Journal of Cognitive Informatics and Natural Intelligence, 3(3):27–38. cited By (since 1996)1.
Eltoukhy, M. M., Faye, I., and Samir, B. B. (2010). A comparison of wavelet and curvelet for breast cancer diagnosis in digital mammogram. Computer in Biology and Medicine, 40(4):384–391.
Ferreira, C. B. R. and Borges, D. L. (2005). Uma estratégia de seleção de um subconjunto mínimo de características wavelet em uma abordagem multirresolução para classificação de tumores em mamogramas. V Workshop de Informática Médica, 1.
Fonseca, J. D. (1981). Curso de estatística. Atlas.
Guo, Z., Zhang, D., and Zhang, D. (2010). A completed modeling of local binary pattern operator for texture classification. Image Processing, IEEE Transactions on, 19(6):1657–1663.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H. (2009). The WEKA Data Mining Software: An Update. SIGKDD Explorations, Volume 11, Issue 1.
Haykin, S. (1998). Neural Networks: A Comprehensive Foundation (2nd Edition). Prentice Hall, 2 edition.
Huang, D., Shan, C., Ardabilian, M., Wang, Y., and Chen, L. (2011). Local binary patterns and its application to facial image analysis: A survey. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 41(6):765 –781.
Junior, G. B., C., S. A., Paiva, A. C., and Oliveira, A. C. M. (2006). Identificação de massas em mamografias usando textura, geometria e algoritmos de agrupamento e classificação. VI Workshop de Informática Médica, 1:94–104.
Ko, B. C., Kim, S. H., and Nam, J. Y. (2011). X-ray image classification using random forests with local wavelet-based cs-local binary patterns. J Digit Imaging, 24(6):1141–1151.
Liu, X., You, X., and ming Cheung, Y. (2009). Texture image retrieval using nonseparable wavelets and local binary patterns. In Computational Intelligence and Security, 2009. CIS ’09. International Conference on, volume 1, pages 287–291.
Lladó, X., Oliver, A., Freixenet, J., Martí, R., and Martí, J. (2009). A textural approach for mass false positive reduction in mammography. Computerized Medical Imaging and Graphics, 33(6):415–422.
Mallat, S. (1999). A Wavelet Tour of Signal Processing. Academic Press, San Diego, CA.
MATLAB (2010). version 7.10.0 (R2010a). The MathWorks Inc., Natick, Massachusetts.
Misiti, M., Misiti, Y., Oppenheim, G., and Poggi, J. (2008). Matlab Wavelet Toolbox User’s Guide.
Nanni, L., Brahnam, S., and Lumini, A. (2012). A very high performing system to discriminate tissues in mammograms as benign and malignant. Expert Syst. Appl., 39(2):1968–1971.
Pedrini, H. and Schwartz, W. (2008). Análise de imagens digitais: princípios algoritmos e aplicações. São Paulo: Thomson Learning.
Ramos, R. P., do Nascimento, M. Z., and Pereira, D. C. (2012). Texture extraction: An evaluation of ridgelet, wavelet and co-occurrence based methods applied to mammograms. Expert Systems with Applications, 39(12):11036 – 11047.
Rangayyan, R., Prajna, S., Ayres, F., and Desautels, J. (2008). Detection of architectural distortion in prior screening mammograms using gabor filters, phase portraits, fractal dimension, and texture analysis. International Journal of Computer Assisted Radiology and Surgery, 2:347–361.
Rashed, E. A., Ismail, I. A., and Zaki, S. I. (2007). Multiresolution mammogram analysis in multilevel decomposition. Pattern Recognition Letters, 28(2):286 – 292.
Tang, J., Rangayyan, R., Xu, J., El Naqa, I., and Yang, Y. (2009). Computer-aided detection and diagnosis of breast cancer with mammography: Recent advances. Information Technology in Biomedicine, IEEE Transactions on, 13(2):236 –251.
Publicado
23/07/2013
Como Citar
DUARTE, Yan A. S.; NASCIMENTO, Marcelo Z. do; OLIVEIRAS, Domingos L. L..
Algoritmo de extração de textura baseado em Wavelet e CLBP para classificação de lesões em mamogramas. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 13. , 2013, Maceió/AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2013
.
p. 1276-1285.
ISSN 2763-8952.
