Técnicas de Processamento Digital de Imagens e Inteligência Artificial Aplicadas ao Diagnóstico Odontológico
Resumo
Na odontologia, técnicas de imageamento auxiliam na análise de regiões inacessíveis no exame clínico e, consequentemente, desempenham um papel fundamental no apoio ao diagnóstico. A vasta demanda por imagens é motivação para o desenvolvimento de um sistema de classificação automática que possa ser usado para suporte ao diagnóstico. Neste trabalho uma versão modificada do algoritmo K-Means é implementada para segmentação de radiografias panorâmicas, a qual levou a resultados levemente superiores aos obtidos com os algoritmos K-Means, Fuzzy K-Means e Morphological Chan Vese. Além disso, é proposto um sistema de classificação automática para detecção de lesões em dentes molares na região da maxila para tomografias computadorizadas de feixe cônico, com acurácia de até 81,25%.
Referências
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Calazans, M. A. A., Ferreira, F. A. B. S., Alcoforado, M. d. L. M. G., and Madeiro, F. (2021). Avaliação do algoritmo k-means modificado em segmentação de imagens odontológicas. Anais da XI Conferência Nacional em Comunicações, Redes e Segurança da Informação: Encom 2021.
Calazans, M. A. A., Ferreira, F. A. B. S., Alcoforado, M. d. L. M. G., Santos, A. d., Pontual, A. d. A., and Madeiro, F. (2022). Automatic classification system for periapical lesions in cone-beam computed tomography. Sensors, 22(17).
Chan, T. and Vese, L. (2001). Active contours without edges. IEEE Transactions on Image Processing, 10(2):266–277.
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, pages 248–255.
Goebel, P. M., Belbachir, A. N., and Truppe, M. (2005). Noise estimation in panoramic xray images: an application analysis approach. In IEEE/SP 13th Workshop on Statistical Signal Processing, pages 996–1001.
Huang, G., Liu, Z., Maaten, L. V., and Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Khanagar, S. B., Al-ehaideb, A., Maganur, P. C., Vishwanathaiah, S., Patil, S., Baeshen, H. A., Sarode, S. C., and Bhandi, S. (2021). Developments, application, and performance of artificial intelligence in dentistry – A systematic review. Journal of Dental Sciences, 16(1):508–522.
Lee, D., Baek, S., and Sung, K. (1997). Modified K-Means algorithm for vector quantizer design. IEEE Signal Processing Letters, 4(1):2–4.
Linde, Y., Buzo, A., and Gray, R. (1980). An algorithm for vector quantizer design. IEEE Transactions on Communications, 28(1):84–95.
Molander, B. (1996). Panoramic radiography in dental diagnostics. Swedish Dental Journal. Supplement, 119:1–26.
Mozzo, P., Procacci, C., Tacconi, A., Martini, P. T., and Andreis, I. B. (1998). A new volumetric CT machine for dental imaging based on the cone-beam technique: Preliminary results. European Radiology, 8(9):1558–1564.
Muresan, M. P., Barbura, A. R., and Nedevschi, S. (2020). Teeth detection and dental problem classification in panoramic X-ray images using deep learning and image processing techniques. In 2020 IEEE 16th International Conference on Intelligent Computer Communication and Processing (ICCP), pages 457–463.
Nelson, S. J. (2014). Wheeler’s Dental Anatomy, Physiology and Occlusion-e-book. Elsevier Health Sciences.
Scarfe, W. C., Farman, A. G., and Sukovic, P. (2006). Clinical applications of cone-beam computerized tomography in dental practice. Journal Canadian Dental Association, 72(1):75–80.
Schwendicke, F., Golla, T., Dreher, M., and Krois, J. (2019). Convolutional neural networks for dental image diagnostics: A scoping review. Journal of Dentistry, 91:103226.
Silva, G., Oliveira, L., and Pithon, M. (2018). Automatic segmenting teeth in X-ray images: trends, a novel data set, benchmarking and future perspectives. Expert Systems with Applications, 107:15–31.
Silva, R. C. P., Bezerra, M. S., Gonzaga, G. L. P., Fonseca, A. B. M., Silva, M. K. A., Santos, I. A., and Lessa, S. V. (2022). Clinical applications of cone beam computed tomography in endodontics: Literature review. Research, Society and Development, 11(1).
Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. ArXiv Preprint ArXiv:1409.1556.
Calazans, M. A. A., Ferreira, F. A. B. S., Alcoforado, M. d. L. M. G., and Madeiro, F. (2021). Avaliação do algoritmo k-means modificado em segmentação de imagens odontológicas. Anais da XI Conferência Nacional em Comunicações, Redes e Segurança da Informação: Encom 2021.
Calazans, M. A. A., Ferreira, F. A. B. S., Alcoforado, M. d. L. M. G., Santos, A. d., Pontual, A. d. A., and Madeiro, F. (2022). Automatic classification system for periapical lesions in cone-beam computed tomography. Sensors, 22(17).
Chan, T. and Vese, L. (2001). Active contours without edges. IEEE Transactions on Image Processing, 10(2):266–277.
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, pages 248–255.
Goebel, P. M., Belbachir, A. N., and Truppe, M. (2005). Noise estimation in panoramic xray images: an application analysis approach. In IEEE/SP 13th Workshop on Statistical Signal Processing, pages 996–1001.
Huang, G., Liu, Z., Maaten, L. V., and Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Khanagar, S. B., Al-ehaideb, A., Maganur, P. C., Vishwanathaiah, S., Patil, S., Baeshen, H. A., Sarode, S. C., and Bhandi, S. (2021). Developments, application, and performance of artificial intelligence in dentistry – A systematic review. Journal of Dental Sciences, 16(1):508–522.
Lee, D., Baek, S., and Sung, K. (1997). Modified K-Means algorithm for vector quantizer design. IEEE Signal Processing Letters, 4(1):2–4.
Linde, Y., Buzo, A., and Gray, R. (1980). An algorithm for vector quantizer design. IEEE Transactions on Communications, 28(1):84–95.
Molander, B. (1996). Panoramic radiography in dental diagnostics. Swedish Dental Journal. Supplement, 119:1–26.
Mozzo, P., Procacci, C., Tacconi, A., Martini, P. T., and Andreis, I. B. (1998). A new volumetric CT machine for dental imaging based on the cone-beam technique: Preliminary results. European Radiology, 8(9):1558–1564.
Muresan, M. P., Barbura, A. R., and Nedevschi, S. (2020). Teeth detection and dental problem classification in panoramic X-ray images using deep learning and image processing techniques. In 2020 IEEE 16th International Conference on Intelligent Computer Communication and Processing (ICCP), pages 457–463.
Nelson, S. J. (2014). Wheeler’s Dental Anatomy, Physiology and Occlusion-e-book. Elsevier Health Sciences.
Scarfe, W. C., Farman, A. G., and Sukovic, P. (2006). Clinical applications of cone-beam computerized tomography in dental practice. Journal Canadian Dental Association, 72(1):75–80.
Schwendicke, F., Golla, T., Dreher, M., and Krois, J. (2019). Convolutional neural networks for dental image diagnostics: A scoping review. Journal of Dentistry, 91:103226.
Silva, G., Oliveira, L., and Pithon, M. (2018). Automatic segmenting teeth in X-ray images: trends, a novel data set, benchmarking and future perspectives. Expert Systems with Applications, 107:15–31.
Silva, R. C. P., Bezerra, M. S., Gonzaga, G. L. P., Fonseca, A. B. M., Silva, M. K. A., Santos, I. A., and Lessa, S. V. (2022). Clinical applications of cone beam computed tomography in endodontics: Literature review. Research, Society and Development, 11(1).
Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. ArXiv Preprint ArXiv:1409.1556.
Publicado
27/06/2023
Como Citar
CALAZANS, Maria Alice Andrade; ALCOFORADO, Maria de Lourdes Melo Guedes; MADEIRO, Francisco.
Técnicas de Processamento Digital de Imagens e Inteligência Artificial Aplicadas ao Diagnóstico Odontológico. In: PRÊMIO ARTUR ZIVIANI - CONCURSO DE TESES E DISSERTAÇÕES (MESTRADO) - SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 23. , 2023, São Paulo/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 132-137.
ISSN 2763-8987.
DOI: https://doi.org/10.5753/sbcas_estendido.2023.230084.
