Glaucoma Diagnosis in Fundus Images Using Shannon and McIntosh Diversity Indices
Abstract
Glaucoma is an asymptomatic ocular disease in the early stages that if left untreated can lead to blindness. In most cases it causes an increase in pressure inside the eye (intraocular pressure) causing damage to the optic nerve. The use of image processing techniques for analysis of the fundus of the eye helps experts in the diagnosis of glaucoma, thus preventing vision loss. In this work we propose a method for diagnosis of glaucoma in fundus images using the Shannon and McIntosh diversity indexes as descriptors of texture patterns and support vector machine (SVM) for classification. The application of the Shannon and McIntosh indexes as the texture descriptors proved to be effective, reaching a mean accuracy of 88,35%, a mean sensitivity of 84,50% and a mean specificity of 91,37%.
References
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Vapnik, V. (1998). Statistical Learning Theory. Wiley New York.
WGA (2017). What is glaucoma? [link]. (Acesso em 17 mar. 2017).
Zhuang, L. and Dai, H. (2006). Parameter optimization of kernel-based one-class classifier on imbalance learning. Journal of Computers, 1(7):32–40.
Banić, N. and Lončarić, S. (2013). Light random sprays retinex: Exploiting the noisy illumination estimation. IEEE Signal Processing Letters, 20(12):1240–1243.
CBO (2017). Glaucoma. [link]. (Acesso em 26 fev. 2017).
Chang, C.-C. and Lin, C.-J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1–27:27.
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).
Duda and Hart (1973). Pattern classification and scene analysis. John Wiley.
Faust, O., Acharya, R., Ng, E. Y.-K., Ng, K.-H., and Suri, J. S. (2012). Algorithms for the automated detection of diabetic retinopathy using digital fundus images: a review. Journal of medical systems, 36(1):145–157.
Gajbhiye, G. O. and Kamthane, A. N. (2015). Automatic classification of glaucomatous images using wavelet and moment feature. In 2015 Annual IEEE India Conference (INDICON), pages 1–5.
Haleem, M. S., Han, L., van Hemert, J., and Fleming, A. (2015). Glaucoma classification using regional wavelet features of the onh and its surroundings. In Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE, pages 4318–4321. IEEE.
Lin, S. C., Singh, K., Jampel, H. D., Hodapp, E. A., Smith, S. D., Francis, B. A., Dueker, D. K., Fechtner, R. D., Samples, J. S., Schuman, J. S., et al. (2007). Optic nerve head and retinal nerve fiber layer analysis: a report by the american academy of ophthalmology. Ophthalmology, 114(10):1937–1949.
Marrugan, A. (2004). Measuring biological diversity. Victoria, Australia: Blackwell Scienc Ltd a Blackwell Publishing company.
McIntosh, R. P. (1967). An index of diversity and the relation of certain concepts to diversity. Ecology, 48(3):392–404.
Melo, A. S. (2008). O que ganhamos confundindo riqueza de espécies e equabilidade em um índice de diversidade. Biota Neotropica, 8(3):21–27.
Salam, A. A., Khalil, T., Akram, M. U., Jameel, A., and Basit, I. (2016). Automated detection of glaucoma using structural and non structural features. SpringerPlus, 5(1):1519.
SBG (2009). 3a consenso brasilleiro de glaucoma de ângulo aberto. [link]. (Acesso em 01 mar. 2017).
Shannon, C. E. (1949). W. weaver the mathematical theory of communication. Urbana: University of Illinois Press, 29.
Sousa, J. A. d., Almeida, J. D. S., Paiva, A. C. d., Silva, A. C., and Gattass, M. (2016). Diagnóstico de glaucoma em retinografias baseado em geoestatística. Journal of Health Informatics, 8:737–746.
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. (1998). Statistical Learning Theory. Wiley New York.
WGA (2017). What is glaucoma? [link]. (Acesso em 17 mar. 2017).
Zhuang, L. and Dai, H. (2006). Parameter optimization of kernel-based one-class classifier on imbalance learning. Journal of Computers, 1(7):32–40.
Published
2017-07-02
How to Cite
ARAÚJO, José Denes L.; DE PAIVA, Anselmo C.; DE ALMEIDA, João D. S.; S. NETO, Otilio Paulo; DE SOUSA, Jefferson A.; SILVA, Aristófanes C.; BRAZ JÚNIOR, Geraldo.
Glaucoma Diagnosis in Fundus Images Using Shannon and McIntosh Diversity Indices. In: BRAZILIAN SYMPOSIUM ON COMPUTING APPLIED TO HEALTH (SBCAS), 17. , 2017, São Paulo.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2017
.
p. 1873-1882.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2017.3698.
