Classificação de Blocos de Imagens de Padrões Radiológicos Pulmonares com Resampling SMOTE
Resumo
Doenças Pulmonares Difusas podem afetar o parênquima pulmonar, causando deficiências respiratórias até a quase completa perda de função, sendo necessária uma avaliação mais precisa para um diagnóstico concreto. Utilizando-se de técnicas computacionais, a proposta deste trabalho é utilizar descritores de características (LBP, CLBP, histograma de níveis de cinza e GLCM) para a classificação de padrões pulmonares, auxiliando radiologistas no diagnóstico dessas doenças. Utilizando uma abordagem baseada em blocos, resampling SMOTE e o classificador SVM, uma taxa de acerto de 87,41% e sensibilidade de 88,31% foram alcançadas.
Referências
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Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P. (2002). Smote: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16:321–357.
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Gonzalez, R. C. and Woods, R. E. (2002). Digital Image Processing. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 2nd edition.
Guo, Z., Zhang, L., and Zhang, D. (2010). A completed modeling of local binary pattern operator for texture classification. IEEE Transactions on Image Processing, 19(6):1657–1663.
Haralick, R. M., Shanmugam, K., et al. (1973). Textural features for image classification. IEEE Transactions on systems, man, and cybernetics, SMC-3(6):610–621.
Kale, M., Mukhopadhyay, S., Dash, J. K., Garg, M., and Khandelwal, N. (2016). Differentiation of several interstitial lung disease patterns in hrct images using support vector machine: role of databases on performance. In Medical Imaging 2016: Computer-Aided Diagnosis, volume 9785, page 97852Z. International Society for Optics and Photonics.
Li, Q., Cai, W., and Feng, D. D. (2013). Lung image patch classification with automatic feature learning. In Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, pages 6079–6082. IEEE.
ONO, S., KOIWAI, M., SHOUNO, H., and KIDO, S. (2017). Comparison of feature selection method for diffuse lung disease. PDPTA.
Song, Y., Cai, W., Huang, H., Zhou, Y., Feng, D. D., Wang, Y., Fulham, M. J., and Chen, M. (2015). Large margin local estimate with applications to medical image classification. IEEE transactions on medical imaging, 34(6):1362–1377.
Song, Y., Cai, W., Zhou, Y., and Feng, D. D. (2013). Feature-based image patch approximation for lung tissue classification. IEEE transactions on medical imaging, 32(4):797–808.
Xu, R., Hirano, Y., Tachibana, R., and Kido, S. (2013). A bag-of-features approach to classify six types of pulmonary textures on high-resolution computed tomography. IEICE TRANSACTIONS on Information and Systems, 96(4):845–855.
Zhao, W., Xu, R., Hirano, Y., Tachibana, R., and Kido, S. (2013). Classification of diffuse lung diseases patterns by a sparse representation based method on hrct images. In Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, pages 5457–5460. IEEE.
Zhao, W., Xu, R., Hirano, Y., Tachibana, R., and Kido, S. (2015). A sparse representation based method to classify pulmonary patterns of diffuse lung diseases. Computational and mathematical methods in medicine, 2015.
Bagesteiro, L. D. (2015). Classificação de padrões radiológicos por blocos em imagens não segmentadas de tomografia computadorizada.
Bergstra, J. and Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13(Feb):281–305.
Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P. (2002). Smote: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16:321–357.
Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3):273–297.
Depeursinge, A., Vargas, A., Platon, A., Geissbuhler, A., Poletti, P.-A., and Müller, H. (2012). Building a reference multimedia database for interstitial lung diseases. Computerized medical imaging and graphics, 36(3):227–238.
Firmino, M., Morais, A. H., Mendoça, R. M., Dantas, M. R., Hekis, H. R., and Valentim, R. (2014). Computer-aided detection system for lung cancer in computed tomography scans: review and future prospects. Biomedical engineering online, 13(1):41.
Gonzalez, R. C. and Woods, R. E. (2002). Digital Image Processing. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 2nd edition.
Guo, Z., Zhang, L., and Zhang, D. (2010). A completed modeling of local binary pattern operator for texture classification. IEEE Transactions on Image Processing, 19(6):1657–1663.
Haralick, R. M., Shanmugam, K., et al. (1973). Textural features for image classification. IEEE Transactions on systems, man, and cybernetics, SMC-3(6):610–621.
Kale, M., Mukhopadhyay, S., Dash, J. K., Garg, M., and Khandelwal, N. (2016). Differentiation of several interstitial lung disease patterns in hrct images using support vector machine: role of databases on performance. In Medical Imaging 2016: Computer-Aided Diagnosis, volume 9785, page 97852Z. International Society for Optics and Photonics.
Li, Q., Cai, W., and Feng, D. D. (2013). Lung image patch classification with automatic feature learning. In Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, pages 6079–6082. IEEE.
ONO, S., KOIWAI, M., SHOUNO, H., and KIDO, S. (2017). Comparison of feature selection method for diffuse lung disease. PDPTA.
Song, Y., Cai, W., Huang, H., Zhou, Y., Feng, D. D., Wang, Y., Fulham, M. J., and Chen, M. (2015). Large margin local estimate with applications to medical image classification. IEEE transactions on medical imaging, 34(6):1362–1377.
Song, Y., Cai, W., Zhou, Y., and Feng, D. D. (2013). Feature-based image patch approximation for lung tissue classification. IEEE transactions on medical imaging, 32(4):797–808.
Xu, R., Hirano, Y., Tachibana, R., and Kido, S. (2013). A bag-of-features approach to classify six types of pulmonary textures on high-resolution computed tomography. IEICE TRANSACTIONS on Information and Systems, 96(4):845–855.
Zhao, W., Xu, R., Hirano, Y., Tachibana, R., and Kido, S. (2013). Classification of diffuse lung diseases patterns by a sparse representation based method on hrct images. In Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, pages 5457–5460. IEEE.
Zhao, W., Xu, R., Hirano, Y., Tachibana, R., and Kido, S. (2015). A sparse representation based method to classify pulmonary patterns of diffuse lung diseases. Computational and mathematical methods in medicine, 2015.
Publicado
22/07/2018
Como Citar
ROGALSKY, Johanna Elisabeth; ALVES, Jeovane Honório; DE OLIVEIRA, Lucas Ferrari.
Classificação de Blocos de Imagens de Padrões Radiológicos Pulmonares com Resampling SMOTE. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 18. , 2018, Natal.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 253-258.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2018.3688.
