Automatic Brückner Test Based on Images
Abstract
Brückner Test is an important eye exam by witch it is possible to diagnose eye diseases early. This work presents a method to detect the presence of eye pathology based on images, using Haralick descriptors for texture analysis of the reflex and machine learning to classify normal and pathological cases. The proposed method reaches 91% accuracy, 90.9% sensibility and 91.14% specificity by using the REPTree classifier.
References
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.
Published
2018-07-22
How to Cite
SILVA, Italo F. S.; ALMEIDA, João D. S.; TEIXEIRA, Jorge A. M.; BRAZ JÚNIOR, Geraldo; DE PAIVA, Anselmo C..
Automatic Brückner Test Based on Images. In: BRAZILIAN SYMPOSIUM ON COMPUTING APPLIED TO HEALTH (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.
