Teste Automático de Brückner basedo em Imagens
Resumo
O teste de Brückner é um importante exame oftalmológico pelo qual é possível diagnósticar doenças oculares precocemente. Este trabalho apresenta um método para detectar a presença de patologia na visão baseado em imagens, fazendo uso dos descritores de Haralick para analisar a textura do reflexo e aprendizado de máquina para classificar os casos em normais ou patológicos. O método proposto alcança 91% de acurácia, 90,9% de sensibilidade e 91,14% de especificidade utilizando o classificador REPTree.
Referências
Almeida, J. D. S., Silva, A. C., de Paiva, A. C., and Teixeira, J. A. M. (2012). Computational methodology for automatic detection of strabismus in digital images through hirschberg test. Computers in Biology and Medicine, 42(1):135 – 146.
Burger, W. and Burge, M. J. (2013). Principles of Digital Image Processing: Advanced Methods. Springer Publishing Company, Incorporated.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H. (2009). The weka data mining software: An update. SIGKDD Explor. Newsl., 11(1):10–18.
Haralick, R. M., Shanmugam, K., et al. (1973). Textural features for image classification. IEEE Transactions on systems, man, and cybernetics, (6):610–621.
Henning, R., Rivas-Perea, P., Shaw, B., and Hamerly, G. (2014). A convolutional neural network approach for classifying leukocoria. In Image Analysis and Interpretation (SSIAI), 2014 IEEE Southwest Symposium on, pages 9–12. IEEE.
Itseez (2015). Open source computer vision library. [link].
Jalis, M. (2015). Use of bruckner test for the detection of significant refractive errors in children. Journal of Rawalpindi Medical College, 19(3):200–203.
Rivas-Perea, P., Baker, E., Hamerly, G., and Shaw, B. F. (2014). Detection of leukocoria using a soft fusion of expert classifiers under non-clinical settings. BMC ophthalmology, 14(1):110.
Sun, M., Ma, A., Li, F., Cheng, K., Zhang, M., Yang, H., Nie, W., and Zhao, B. (2016). Sensitivity and specificity of red reflex test in newborn eye screening. The Journal of pediatrics, 179:192–196.
Thornton, C., Hutter, F., Hoos, H. H., and Leyton-Brown, K. (2013). Auto-weka: Combined selection and hyperparameter optimization of classification algorithms. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 847–855. ACM.
Viola, P. and Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on, volume 1, pages I–I. IEEE.
Zayed, N. and Elnemr, H. A. (2015). Statistical analysis of haralick texture features to discriminate lung abnormalities. Journal of Biomedical Imaging, 2015:12:12–12:12.
Zhang, L., Lu, K., Pan, C., and Xia, S. (2014). Eye detection for electronic map control application. In Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2014 Sixth International Conference on, volume 1, pages 241–244. IEEE.
Burger, W. and Burge, M. J. (2013). Principles of Digital Image Processing: Advanced Methods. Springer Publishing Company, Incorporated.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H. (2009). The weka data mining software: An update. SIGKDD Explor. Newsl., 11(1):10–18.
Haralick, R. M., Shanmugam, K., et al. (1973). Textural features for image classification. IEEE Transactions on systems, man, and cybernetics, (6):610–621.
Henning, R., Rivas-Perea, P., Shaw, B., and Hamerly, G. (2014). A convolutional neural network approach for classifying leukocoria. In Image Analysis and Interpretation (SSIAI), 2014 IEEE Southwest Symposium on, pages 9–12. IEEE.
Itseez (2015). Open source computer vision library. [link].
Jalis, M. (2015). Use of bruckner test for the detection of significant refractive errors in children. Journal of Rawalpindi Medical College, 19(3):200–203.
Rivas-Perea, P., Baker, E., Hamerly, G., and Shaw, B. F. (2014). Detection of leukocoria using a soft fusion of expert classifiers under non-clinical settings. BMC ophthalmology, 14(1):110.
Sun, M., Ma, A., Li, F., Cheng, K., Zhang, M., Yang, H., Nie, W., and Zhao, B. (2016). Sensitivity and specificity of red reflex test in newborn eye screening. The Journal of pediatrics, 179:192–196.
Thornton, C., Hutter, F., Hoos, H. H., and Leyton-Brown, K. (2013). Auto-weka: Combined selection and hyperparameter optimization of classification algorithms. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 847–855. ACM.
Viola, P. and Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on, volume 1, pages I–I. IEEE.
Zayed, N. and Elnemr, H. A. (2015). Statistical analysis of haralick texture features to discriminate lung abnormalities. Journal of Biomedical Imaging, 2015:12:12–12:12.
Zhang, L., Lu, K., Pan, C., and Xia, S. (2014). Eye detection for electronic map control application. In Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2014 Sixth International Conference on, volume 1, pages 241–244. IEEE.
Publicado
22/07/2018
Como Citar
SILVA, Italo F. S.; ALMEIDA, João D. S.; TEIXEIRA, Jorge A. M.; BRAZ JÚNIOR, Geraldo; DE PAIVA, Anselmo C..
Teste Automático de Brückner basedo em Imagens. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 18. , 2018, Natal.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 223-228.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2018.3673.
