Detecção de Patologias Oculares em Imagens de Reflexo Vermelho Utilizando Descritores de Cor
Resumo
O Teste de Brückner é um exame oftalmológico que visa detectar patologias oculares precocemente. Ele opera através da identificação de um reflexo vermelho na região ocular após a incidência de um feixe luminoso. Este trabalho apresenta um novo método para identificação automática de um possível problema ocular em imagens de reflexo vermelho oriundas do Teste de Brückner através de descritores de cor. Utilizando uma otimização na escolha dos métodos de pré processamento em conjunto com os descritores de cor dominante e os momentos de cor, o método proposto alcançou 92% de acurácia, 98% de especificidade e 76% de sensibilidade utilizando o classificador XGBoost.
Referências
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Sklansky, J. (1978). Image segmentation and feature extraction. IEEE Transactions on Systems, Man, and Cybernetics, 8(4):237–247.
Stricker, M. and Orengo, M. (1995). Similarity of color images. Proceedings of SPIE The International Society for Optical Engineering, 2420.
Sun, M., Ma, A., Li, F., Cheng, K., Zhang, M., Yang, H., Nie, W., and Zhao, B. (2016). Sensitivity and specicity of red reex test in newborn eye screening. The Journal of pediatrics, 179:192–196.
Vass, J., Jia Yao, Joshi, A., Palaniappan, K., and Xinhua Zhuang (1998). Interactive image retrieval over the internet. In Proceedings Seventeenth IEEE Symposium on Reliable Distributed Systems (Cat. No.98CB36281), pages 461–466.
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Wong, C. Y., Jiang, G., Rahman, M. A., Liu, S., Lin, S. C.-F., Kwok, N., Shi, H., Yu, Y.-H., and Wu, T. (2016). Histogram equalization and optimal prole compression based approach for colour image enhancement. Journal of Visual Communication and Image Representation, 38:802–813.
Archer, S. M. (1988). Developmental aspects of the brückner test. Ophthalmology, 95(8):1098–1101.
Bergstra, J., Yamins, D., and Cox, D. D. (2013). Hyperopt: A python library for optimizing the hyperparameters of machine learning algorithms. In van der Walt, S., Millman, J., and Huff, K., editors, Proceedings of the 12th Python in Science Conference, pages 13 – 20.
Boaretto, A. E. (2009). A evolução da população mundial, da oferta de alimentos e das ciências agrárias. Revista Ceres, 56(4):513–526.
Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., et al. (2015). Xgboost: extreme gradient boosting. R package version 0.4-2, 1(4).
Desai, P., Pujari, J., and S.N, P. (2010). Content-based image retrieval using color and shape descriptors. In Arabian Journal for Science and Engineering.
Drucker, H., Burges, C. J., Kaufman, L., Smola, A., Vapnik, V., et al. (1997). Support vector regression machines. Advances in neural information processing systems, 9:155–161.
Fernandes, A. G., Martins, C., Lima, A. C., Junior, G., de Almeida, J. D., and de Paiva, A. (2019). Meta aprendizagem de extração de caracteristicas aplicada ao diagnostico de glaucoma. In Anais do XIX Simpósio Brasileiro de Computação Aplicada à Saúde, pages 342–347, Porto Alegre, RS, Brasil. SBC.
Gonzalez, R. C. and Woods, R. E. (1992). Digital image processing. Reading:AddisonWesley, pages 85–103. 3ª edition.
Gonzalez, R. C. and Woods, R. E. (2010). Processamento Digital de Imagens. Pearson, Group, G. R. S. (2020). Global Retinoblastoma Presentation and Analysis by National Income Level. JAMA Oncology, 6(5):685–695.
Hui Yu, Mingjing Li, Hong-Jiang Zhang, and Jufu Feng (2002). Color texture moments for content-based image retrieval. In Proceedings. International Conference on Image Processing, volume 3, pages 929–932 vol.3.
Indriani, O. R., Kusuma, E. J., Sari, C. A., Rachmawanto, E. H., and Setiadi, D. R. I. M. (2017). Tomatoes classication using k-nn based on glcm and hsv color space. In 2017 International Conference on Innovative and Creative Information Technology (ICITech), pages 1–6.
Junhua, C. and Jing, L. (2012). Research on color image classication based on hsv color space. In 2012 Second International Conference on Instrumentation, Measurement, Computer, Communication and Control, pages 944–947.
Kurt, B., Nabiyev, V. V., and Turhan, K. (2012). Medical images enhancement by using anisotropic lter and clahe. In 2012 International Symposium on Innovations in Intelligent Systems and Applications, pages 1–4.
Mandloi, G. (2014). A survey on feature extraction techniques for color images. In International Journal of Scientic and Engineering Research.
Manjunath, B., Ohm, J., Vasudevan, V., and Yamada, A. (2001). Color and texture descriptors. Circuits and Systems for Video Technology, IEEE Transactions on, 11:703 – 715.
Organization, W. H. et al. (2000). Global initiative for the elimination of avoidable blindness. Technical report, World Health Organization.
Pan, C.-W., Ramamurthy, D., and Saw, S.-M. (2012). Worldwide prevalence and risk factors for myopia. Ophthalmic and Physiological Optics, 32(1):3–16.
Pizer, S. M., Amburn, E. P., Austin, J. D., Cromartie, R., Geselowitz, A., Greer, T., ter Haar Romeny, B., Zimmerman, J. B., and Zuiderveld, K. (1987). Adaptive histogram equalization and its variations. Computer Vision, Graphics, and Image Processing, 39(3):355–368.
Resnikoff, S., Felch, W., Gauthier, T.-M., and Spivey, B. (2012). The number of ophthalmologists in practice and training worldwide: a growing gap despite more than 200 000 practitioners. British Journal of Ophthalmology, 96(6):783–787.
Resnikoff, S., Pascolini, D., Mariotti, S. P., and Pokharel, G. P. (2008). Global magnitude of visual impairment caused by uncorrected refractive errors in 2004. Bulletin of the World Health Organization, 86:63–70.
Rivas, P., Baker, E., Hamerly, G., and Shaw, B. (2014). Detection of leukocoria using a soft fusion of expert classiers under non-clinical settings. BMC ophthalmology, 14:110.
Saluka, K. and Selvarajah, S. (2010). Comparison of color features for image retrieval. Indian Journal of Computer Science and Engineering, 1.
Silva, I. F. S., Almeida, J. D. S., Teixeira, J. A. M., Júnior, G. B., and de Paiva, A. C. (2018a). Teste automático de brückner basedo em imagens. In Anais do XVIII Simpósio Brasileiro de Computação Aplicada à Saúde, Porto Alegre, RS, Brasil. SBC.
Silva, M. G., Pessoa, A. C., de Almeida, J. D., Junior, G. B., and de Paiva, A. C. (2018b). Diagnóstico do glaucoma em imagens de retinograa usando variantes de padroes locais binários. In Anais do XVIII Simpósio Brasileiro de Computação Aplicada à Saúde. SBC.
Sklansky, J. (1978). Image segmentation and feature extraction. IEEE Transactions on Systems, Man, and Cybernetics, 8(4):237–247.
Stricker, M. and Orengo, M. (1995). Similarity of color images. Proceedings of SPIE The International Society for Optical Engineering, 2420.
Sun, M., Ma, A., Li, F., Cheng, K., Zhang, M., Yang, H., Nie, W., and Zhao, B. (2016). Sensitivity and specicity of red reex test in newborn eye screening. The Journal of pediatrics, 179:192–196.
Vass, J., Jia Yao, Joshi, A., Palaniappan, K., and Xinhua Zhuang (1998). Interactive image retrieval over the internet. In Proceedings Seventeenth IEEE Symposium on Reliable Distributed Systems (Cat. No.98CB36281), pages 461–466.
Veluchamy, M. and Subramani, B. (2019). Image contrast and color enhancement using adaptive gamma correction and histogram equalization. Optik, 183:329–337.
Wong, C. Y., Jiang, G., Rahman, M. A., Liu, S., Lin, S. C.-F., Kwok, N., Shi, H., Yu, Y.-H., and Wu, T. (2016). Histogram equalization and optimal prole compression based approach for colour image enhancement. Journal of Visual Communication and Image Representation, 38:802–813.
Publicado
15/06/2021
Como Citar
MARTINS, Caio Manfredini da Silva; SANTOS, Robert Douglas de Araujo; ALMEIDA, João Dallyson Sousa de; BRAZ JÚNIOR, Geraldo; TEIXEIRA, Jorge Antônio Meireles.
Detecção de Patologias Oculares em Imagens de Reflexo Vermelho Utilizando Descritores de Cor. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 21. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 346-357.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2021.16078.
