Glaucoma Diagnosis in Retinal Fundus Images Using Variants of Local Binary Patterns
Abstract
Glaucoma is an optical disease that degrades the optic nerve until total loss of the field of view. The symptoms only appear when the disease is in an advanced and irreversible stage, thus, making the early diagnosis of this pathology necessary. The main objective of this work is to present a computational method that uses texture descriptors to detect glaucoma automatically in retinographies (eye fundus images). To describe the image texture, it was utilized the LBP, LQP, CS-LBP and CLBP. For image classification, SVM was utilized. The proposed method is organized in four steps: (1) pre-processing, (2) spatial decomposition, (3) feature extraction, and (4) pattern recognition. This method showed promising results of 90.70% of accuracy.
References
Ahmed, F., Hossain, E., Bari, A. H., and Shihavuddin, A. (2011). Compound local binary pattern (clbp) for robust facial expression recognition. In Computational Intelligence and Informatics (CINTI), 2011 IEEE 12th International Symposium on, pages 391–395. IEEE.
Ahonen, T., Hadid, A., and Pietikainen, M. (2006). Face description with local binary patterns: Application to face recognition. IEEE transactions on pattern analysis and machine intelligence, 28(12):2037–2041.
Ali, M. A., Hurtut, T., Faucon, T., and Cheriet, F. (2014). Glaucoma detection based on local binary patterns in fundus photographs. In SPIE Medical Imaging, pages 903531–903531.
Araújo, J. D. L., de Paiva, A. C., De Almeida, J. D., Neto, O. P. S., de Sousa, J. A., Silva, A. C., and Júnior, G. B. (2017). Diagnóstico de glaucoma em imagens de fundo de olho utilizando osíndices de diversidade de shannon e mcintosh. XVII Workshop de Informática Médica.
Cavararo, R. (2015). Pesquisa nacional de saúde 2013. instituto brasileiro de geografia e estatística–ibge. diretoria de pesquisas. coordenação de trabalho e rendimento, rio de janeiro, 2015.
Chimieski, B. F. and Fagundes, R. D. R. (2013). Association and classification data mining algorithms comparison over medical datasets. Journal of health informatics, 5(2).
de Sousa, J. A., de Paiva, A. C., de Almeida, J. D. S., Silva, A. C., Junior, G. B., and Gattass, M. (2017). Texture based on geostatistic for glaucoma diagnosis from fundus eye image. Multimedia Tools and Applications, pages 1–18.
Dorion, T. (2002). Manual de Exame Do Fundo de Olho. Manolen.
Eberhart, R. and Kennedy, J. (1995). A new optimizer using particle swarm theory. In Micro Machine and Human Science, 1995. MHS’95., Proceedings of the Sixth International Symposium on, pages 39–43. IEEE.
Giger, M. L. and Suzuki, K. (2008). Computer-aided diagnosis. In Biomedical information technology, pages 359–XXII. Elsevier.
Gonzalez, R. C. E., Woods, S. L., Gonzalez, R. E. R. E. R. C., Woods, R. E., and Eddins, S. L. (2004). Digital image processing using MATLAB. Number 04; TA1637, G6.
Heikkilä, M., Pietikäinen, M., and Schmid, C. (2006). Description of interest regions with center-symmetric local binary patterns. In ICVGIP, volume 6, pages 58–69. Springer.
Hering, E. (1880). Zur Erklärung der Farbenblindheit aus der Theorie der Gegenfarben, volume 1. Tempsky.
Marques Filho, O. and Neto, H. V. (1999). Processamento digital de imagens. Brasport.
Nanni, L., Lumini, A., and Brahnam, S. (2012). Survey on lbp based texture descriptors for image classification. Expert Systems with Applications, 39(3):3634–3641.
Ojala, T., Pietikäinen, M., and Harwood, D. (1996). A comparative study of texture measures with classification based on featured distributions. Pattern recognition, 29(1):51–59.
Ramos Neto, G. M. (2016). Estudo de Variantes de Padrões Locais Binários para o Diagnóstico de Câncer de Mama. Monografia (Bacharel em Ciência da Computação), UFMA (Universidade Federal do Maranhão), São Luís, Brasil.
Rivera, A. R., Castillo, J. A. R., and Chae, O. (2012). Recognition of face expressions using local principal texture pattern. In Image Processing (ICIP), 2012 19th IEEE International Conference on, pages 2609–2612. IEEE.
Sousa, J. A. d. (2017). Diagnóstico de glaucoma em retinografias utilizando funções geoestatística. Dissertação (Mestrado) - Universidade Federal do Maranhão, Curso de Pós Graduação em Ciência da Computação, São Luís, MA.
Spaeth, G. (1981). Appearances of the optic disc in glaucoma: a pathogenetic classification. In Trans Acad Ophthalmol Symposium on Glaucoma. CV Mosby, St. Louis.
Taleb, A., Faria, M. A. R. d., Ávila, M., and Mello, P. (2012). As condições de saúde ocular no brasil–2012. Conselho Brasileiro de Oftalmologia. São Paulo.
Trucco, E., Ruggeri, A., Karnowski, T., Giancardo, L., Chaum, E., Hubschman, J. P., Al-Diri, B., Cheung, C. Y., Wong, D., Abramoff, M., et al. (2013). Validating retinal fundus image analysis algorithms: Issues and a proposalvalidating retinal fundus image analysis algorithms. Investigative ophthalmology & visual science, 54(5):3546–3559.
Vapnik, V. N. (1999). An overview of statistical learning theory. IEEE transactions on neural networks, 10(5):988–999.
Ahonen, T., Hadid, A., and Pietikainen, M. (2006). Face description with local binary patterns: Application to face recognition. IEEE transactions on pattern analysis and machine intelligence, 28(12):2037–2041.
Ali, M. A., Hurtut, T., Faucon, T., and Cheriet, F. (2014). Glaucoma detection based on local binary patterns in fundus photographs. In SPIE Medical Imaging, pages 903531–903531.
Araújo, J. D. L., de Paiva, A. C., De Almeida, J. D., Neto, O. P. S., de Sousa, J. A., Silva, A. C., and Júnior, G. B. (2017). Diagnóstico de glaucoma em imagens de fundo de olho utilizando osíndices de diversidade de shannon e mcintosh. XVII Workshop de Informática Médica.
Cavararo, R. (2015). Pesquisa nacional de saúde 2013. instituto brasileiro de geografia e estatística–ibge. diretoria de pesquisas. coordenação de trabalho e rendimento, rio de janeiro, 2015.
Chimieski, B. F. and Fagundes, R. D. R. (2013). Association and classification data mining algorithms comparison over medical datasets. Journal of health informatics, 5(2).
de Sousa, J. A., de Paiva, A. C., de Almeida, J. D. S., Silva, A. C., Junior, G. B., and Gattass, M. (2017). Texture based on geostatistic for glaucoma diagnosis from fundus eye image. Multimedia Tools and Applications, pages 1–18.
Dorion, T. (2002). Manual de Exame Do Fundo de Olho. Manolen.
Eberhart, R. and Kennedy, J. (1995). A new optimizer using particle swarm theory. In Micro Machine and Human Science, 1995. MHS’95., Proceedings of the Sixth International Symposium on, pages 39–43. IEEE.
Giger, M. L. and Suzuki, K. (2008). Computer-aided diagnosis. In Biomedical information technology, pages 359–XXII. Elsevier.
Gonzalez, R. C. E., Woods, S. L., Gonzalez, R. E. R. E. R. C., Woods, R. E., and Eddins, S. L. (2004). Digital image processing using MATLAB. Number 04; TA1637, G6.
Heikkilä, M., Pietikäinen, M., and Schmid, C. (2006). Description of interest regions with center-symmetric local binary patterns. In ICVGIP, volume 6, pages 58–69. Springer.
Hering, E. (1880). Zur Erklärung der Farbenblindheit aus der Theorie der Gegenfarben, volume 1. Tempsky.
Marques Filho, O. and Neto, H. V. (1999). Processamento digital de imagens. Brasport.
Nanni, L., Lumini, A., and Brahnam, S. (2012). Survey on lbp based texture descriptors for image classification. Expert Systems with Applications, 39(3):3634–3641.
Ojala, T., Pietikäinen, M., and Harwood, D. (1996). A comparative study of texture measures with classification based on featured distributions. Pattern recognition, 29(1):51–59.
Ramos Neto, G. M. (2016). Estudo de Variantes de Padrões Locais Binários para o Diagnóstico de Câncer de Mama. Monografia (Bacharel em Ciência da Computação), UFMA (Universidade Federal do Maranhão), São Luís, Brasil.
Rivera, A. R., Castillo, J. A. R., and Chae, O. (2012). Recognition of face expressions using local principal texture pattern. In Image Processing (ICIP), 2012 19th IEEE International Conference on, pages 2609–2612. IEEE.
Sousa, J. A. d. (2017). Diagnóstico de glaucoma em retinografias utilizando funções geoestatística. Dissertação (Mestrado) - Universidade Federal do Maranhão, Curso de Pós Graduação em Ciência da Computação, São Luís, MA.
Spaeth, G. (1981). Appearances of the optic disc in glaucoma: a pathogenetic classification. In Trans Acad Ophthalmol Symposium on Glaucoma. CV Mosby, St. Louis.
Taleb, A., Faria, M. A. R. d., Ávila, M., and Mello, P. (2012). As condições de saúde ocular no brasil–2012. Conselho Brasileiro de Oftalmologia. São Paulo.
Trucco, E., Ruggeri, A., Karnowski, T., Giancardo, L., Chaum, E., Hubschman, J. P., Al-Diri, B., Cheung, C. Y., Wong, D., Abramoff, M., et al. (2013). Validating retinal fundus image analysis algorithms: Issues and a proposalvalidating retinal fundus image analysis algorithms. Investigative ophthalmology & visual science, 54(5):3546–3559.
Vapnik, V. N. (1999). An overview of statistical learning theory. IEEE transactions on neural networks, 10(5):988–999.
Published
2018-07-22
How to Cite
SILVA, Mayara G.; PESSOA, Alexandre C. P.; DE ALMEIDA, João D. S.; BRAZ JUNIOR, Geraldo; DE PAIVA, Anselmo Cardoso.
Glaucoma Diagnosis in Retinal Fundus Images Using Variants of Local Binary Patterns. In: BRAZILIAN SYMPOSIUM ON COMPUTING APPLIED TO HEALTH (SBCAS), 18. , 2018, Natal.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 94-104.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2018.3679.
