Classificação do Filme Lacrimal usando a Função K de Ripley como Descritor de Textura
Resumo
A síndrome do olho seco é uma doença que afeta grande parte da população dificultando suas atividades diárias. O diagnóstico desta doença é uma tarefa desafiadora para oftalmologistas, devido à sua etiologia multifatorial. Um dos testes mais utilizados consiste na classificação manual das imagens do filme lacrimal capturadas com o interferômetro Doane. Assim, torna-se útil o uso de um sistema automático para suporte ao diagnóstico pelos especialistas. Neste trabalho é proposto um método para a classificação automática da camada lipídica do filme lacrimal, usando a função K de Ripley para extrair características e os classificadores Naive Bayes e Bayes Net. O método proposto apresenta taxas de classificação superiores a 95%.
Referências
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Doane, M. G. (1989). An instrument for in vivo tear film interferometry. Optometry and Vision Science, 66(6):383–388.
Doi, K. (2007). Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Computerized medical imaging and graphics, 31(4-5):198–211.
Duda, R. (1973). Pattern classification and scene analysis. New-York, London, Sydny, Tronto A Wiley-Interscience Publication.
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Hall, M. A. (1999). Correlation-based feature selection for machine learning. University of Waikato Hamilton.
Jensen, F. V. (1996). An introduction to Bayesian networks, volume 210. UCL press London.
Lancaster, J. and J Downes, B. (2004). Spatial point pattern analysis of available and exploited resources. Ecography, 27(1):94–102.
Martins, L. O. (2007). Detecção de massas em imagens mamográficas através do algoritmo growing neural gas e da função k de ripley. Master’s thesis, Universidade Federal do Maranhão.
Nielsen, T. D. and Jensen, F. V. (2009). Bayesian networks and decision graphs. Springer Science & Business Media.
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.
Plataniotis, K. N. and Venetsanopoulos, A. N. (2013). Color image processing and applications. Springer Science & Business Media.
Remeseiro, B., Bolon-Canedo, V., Peteiro-Barral, D., Alonso-Betanzos, A., Guijarro-Berdinas, B., Mosquera, A., Penedo, M. G., and Sánchez-Marono, N. (2014). A methodology for improving tear film lipid layer classification. IEEE journal of biomedical and health informatics, 18(4):1485–1493.
Remeseiro, B., Oliver, K. M., Tomlinson, A., Martin, E., Barreira, N., and Mosquera, A. (2015). Automatic grading system for human tear films. Pattern Analysis and Applications, 18(3):677–694.
Ripley, B. D. (1977). Modelling spatial patterns. Journal of the Royal Statistical Society. Series B (Methodological), pages 172–212.
Rodriguez, J. D., Perez, A., and Lozano, J. A. (2010). Sensitivity analysis of k-fold cross validation in prediction error estimation. IEEE transactions on pattern analysis and machine intelligence, 32(3):569–575.
Rolando, M. and Zierhut, M. (2001). The ocular surface and tear film and their dysfunction in dry eye disease. Survey of ophthalmology, 45:S203–S210.
Valim, V., Trevisani, V. F. M., de Sousa, J. M., Vilela, V. S., and Belfort, R. (2015). Current approach to dry eye disease. Clinical reviews in allergy & immunology, 49(3):288–297.
Vapnik, V. N. (1999). An overview of statistical learning theory. IEEE transactions on neural networks, 10(5):988–999.
Viera, A. J., Garrett, J. M., et al. (2005). Understanding interobserver agreement: the kappa statistic. Fam Med, 37(5):360–363.
Villaverde, D. G., Remeseiro, B., Barreira, N., Penedo, M. G., and González, A. M. (2014). Feature selection applied to human tear film classification. In ICAART (1), pages 395–402.
VOPTICAL GCU, V. (2017). Optical dataset acquired and annotated by optometrists from the department of life sciences, glasgow caledonian university (uk), 2013. [link].
Zhao, Z. and Liu, H. (2007). Searching for interacting features. In IJCAI, volume 7, pages 1156–1161.
Çesmeli, E. and Wang, D. (2001). Texture segmentation using gaussian-markov random fields and neural oscillator networks. IEEE Transactions on neural networks, 12(2):394–404.
Cooper, G. F. and Herskovits, E. (1992). A bayesian method for the induction of probabilistic networks from data. Machine learning, 9(4):309–347.
Dash, M. and Liu, H. (2003). Consistency-based search in feature selection. Artificial intelligence, 151(1-2):155–176.
Dean, J. (2014). Big data, data mining, and machine learning: value creation for business leaders and practitioners. John Wiley & Sons.
Doane, M. G. (1989). An instrument for in vivo tear film interferometry. Optometry and Vision Science, 66(6):383–388.
Doi, K. (2007). Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Computerized medical imaging and graphics, 31(4-5):198–211.
Duda, R. (1973). Pattern classification and scene analysis. New-York, London, Sydny, Tronto A Wiley-Interscience Publication.
Fawcett, T. (2006). An introduction to roc analysis. Pattern recognition letters, 27(8):861–874.
Gonzalez, R. and Woods, R. (2008). Digital image processing. Pearson, Prentice Hall.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H. (2009). The weka data mining software: an update. ACM SIGKDD explorations newsletter, 11(1):10–18.
Hall, M. A. (1999). Correlation-based feature selection for machine learning. University of Waikato Hamilton.
Jensen, F. V. (1996). An introduction to Bayesian networks, volume 210. UCL press London.
Lancaster, J. and J Downes, B. (2004). Spatial point pattern analysis of available and exploited resources. Ecography, 27(1):94–102.
Martins, L. O. (2007). Detecção de massas em imagens mamográficas através do algoritmo growing neural gas e da função k de ripley. Master’s thesis, Universidade Federal do Maranhão.
Nielsen, T. D. and Jensen, F. V. (2009). Bayesian networks and decision graphs. Springer Science & Business Media.
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.
Plataniotis, K. N. and Venetsanopoulos, A. N. (2013). Color image processing and applications. Springer Science & Business Media.
Remeseiro, B., Bolon-Canedo, V., Peteiro-Barral, D., Alonso-Betanzos, A., Guijarro-Berdinas, B., Mosquera, A., Penedo, M. G., and Sánchez-Marono, N. (2014). A methodology for improving tear film lipid layer classification. IEEE journal of biomedical and health informatics, 18(4):1485–1493.
Remeseiro, B., Oliver, K. M., Tomlinson, A., Martin, E., Barreira, N., and Mosquera, A. (2015). Automatic grading system for human tear films. Pattern Analysis and Applications, 18(3):677–694.
Ripley, B. D. (1977). Modelling spatial patterns. Journal of the Royal Statistical Society. Series B (Methodological), pages 172–212.
Rodriguez, J. D., Perez, A., and Lozano, J. A. (2010). Sensitivity analysis of k-fold cross validation in prediction error estimation. IEEE transactions on pattern analysis and machine intelligence, 32(3):569–575.
Rolando, M. and Zierhut, M. (2001). The ocular surface and tear film and their dysfunction in dry eye disease. Survey of ophthalmology, 45:S203–S210.
Valim, V., Trevisani, V. F. M., de Sousa, J. M., Vilela, V. S., and Belfort, R. (2015). Current approach to dry eye disease. Clinical reviews in allergy & immunology, 49(3):288–297.
Vapnik, V. N. (1999). An overview of statistical learning theory. IEEE transactions on neural networks, 10(5):988–999.
Viera, A. J., Garrett, J. M., et al. (2005). Understanding interobserver agreement: the kappa statistic. Fam Med, 37(5):360–363.
Villaverde, D. G., Remeseiro, B., Barreira, N., Penedo, M. G., and González, A. M. (2014). Feature selection applied to human tear film classification. In ICAART (1), pages 395–402.
VOPTICAL GCU, V. (2017). Optical dataset acquired and annotated by optometrists from the department of life sciences, glasgow caledonian university (uk), 2013. [link].
Zhao, Z. and Liu, H. (2007). Searching for interacting features. In IJCAI, volume 7, pages 1156–1161.
Publicado
22/07/2018
Como Citar
CRUZ, Luana B.; ARAÚJO, José Denes L.; SOUSA, Jefferson A.; ALMEIDA, João D. S.; B. JÚNIOR, Geraldo; SILVA, Aristófanes C.; PAIVA, Anselmo C..
Classificação do Filme Lacrimal usando a Função K de Ripley como Descritor de Textura. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 18. , 2018, Natal.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 24-35.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2018.3669.
