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Title

Instance Segmentation and Labeling of Teeth from Dental X-Ray using Region Based Convolutional Neural Network

  Sireesha Rodda 1 * ,   Vaibhav Kovela 2 ,   Sanjay Dokula 3

1  Department of CSE GITAM Institute of Technology GITAM (Deemed to be University), Visakhapatnam, India
    (srodda@gitam.in)

2  Department of CSE GITAM Institute of Technology GITAM (Deemed to be University), Visakhapatnam, India
    (Vaibhav.Kovela @gmail.com)

3  Department of CSE GITAM Institute of Technology GITAM (Deemed to be University), Visakhapatnam, India
    (sdokula21@hotmail.com)

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Doi   :   https://doi.org/10.54216/JNFS.020202

Received June 8, 2021 Accepted: Jan 25, 2022

Abstract :

Radiological Examination of teeth is a primary step that a dentist usually takes to diagnose the problem before further treatment. The diagnosis involves searching for diseases ranging from cavities to tumors, So, correct diagnosis is vital for timely and precise treatment. This paper attempts to solve one of the elementary steps in diagnosis i,e, Labeling of Teeth, using Region-Based Convolutional Neural Networks that help reduce monotonous work for a dentist and provide segments of each tooth for further diagnosis of diseases with the use of Mask R-CNN. We used 200 panoramic X-Ray images of 4 categories to train, test and validate the model. Mask R-CNN with pre-trained weights of COCO Dataset is employed. We further tuned the weights of the dental X-ray dataset considered in the paper for better performance. On testing the learned model, the performance measures were encouraging.

Keywords :

Panoramic X-Rays, Instance Segmentation, Mask R-CNN, Faster CNN, Dental Labeling.

  ,

References :

[1] G. Jader, J. Fontineli, M. Ruiz, K. Abdalla, M. Pithon and L. Oliveira, "Deep Instance Segmentation of Teeth in Panoramic X-Ray Images," 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), 2018, pp. 400-407, doi:10.1109/SIBGRAPI.2018.00058.

[2] N. Senthilkumaran, “Genetic Algorithm Approach to Edge Detection for Dental X-ray Image Segmentation,” International Journal of Advanced Research in Computer Science and Electronics Engineering, vol. 1, no. 7, pp. 5236–5238, 2012.

[3] P. L. Lin, Y. H. Lai, and P. W. Huang, “An effective classification and numbering system for dental bitewing radiographs using teeth region and contour information,” Pattern Recognition, vol. 43, no. 4, pp. 1380–1392, 2010.

[4] R. B. Ali, R. Ejbali, and M. Zaied, “GPU-based Segmentation of Dental X-ray Images using Active Contours Without Edges,” in International Conference on Intelligent Systems Design and Applications, vol. 1, 2015, pp. 505–510.

[5] He, Kaiming, et al. "Mask r-cnn." Proceedings of the IEEE international conference on computer vision. 2017.

[6] G. Zhu, Z. Piao and S. C. Kim, "Tooth Detection and Segmentation with Mask R-CNN," 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 2020, pp. 070-072, doi: 10.1109/ICAIIC48513.2020.9065216.

[7] Ren, Shaoqing, et al. "Faster R-CNN: towards real-time object detection with region proposal networks." IEEE transactions on pattern analysis and machine intelligence 39.6 (2016): 1137-1149.

[8] Yuniarti, Anny, et al. "Classification and numbering of dental radiographs for an automated human identification system." Telkomnika 10.1 (2012): 137.

[9] Mahoor, Mohammad H., and Mohamed Abdel-Mottaleb. "Classification and numbering of teeth in dental bitewing images." Pattern Recognition 38.4 (2005): 577-586.

[10] Tangel, Martin Leonard, et al. "Dental numbering for periapical radiograph based on multiple fuzzy attribute approach." Journal of Advanced Computational Intelligence and Intelligent Informatics 18.3 (2014): 253-261.

[11] Mahdi, Fahad Parvez, Naomi Yagi, and Syoji Kobashi. "Automatic teeth recognition in dental X-ray images using transfer learning-based faster R-CNN." 2020 IEEE 50th International Symposium on Multiple-Valued Logic (ISMVL). IEEE, 2020.

[12] O. Nomir and M. Abdel-Mottaleb, “Hierarchical contour matching for dental X-ray radiographs,” Pattern Recognition, vol. 41, no. 1, pp. 130–138, 2008.

[13] C. K. Modi and N. P. Desai, “A simple and novel algorithm for automatic selection of ROI for dental radiograph segmentation,” in Canadian Conference on Electrical and Computer Engineering, 2011,pp. 000 504–000 507.

[14] R. B. Ali, R. Ejbali, and M. Zaied, “GPU-based Segmentation of Dental X-ray Images using Active Contours Without Edges,” in International Conference on Intelligent Systems Design and Applications, vol. 1, 2015, pp. 505–510.

[15] H. Li, G. Sun, H. Sun, and W. Liu, “Watershed algorithm based on morphology for dental x-ray images segmentation,” in International Conference on Signal Processing Proceedings, vol. 2, 2012, pp. 877–880.

[16] J. Kaur and J. Kaur, “Dental image disease analysis using pso and backpropagation neural network classifier,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 6, no. 4, pp. 158–160, 2016.

[17] Wang, C-W, Huang, C-T, Lee, J-H, Li, C-H, Chang, S-W, Siao, M-J, Lai, T-M, Ibragimov, B, Vrtovec, T, Ronneberger, O, Fischer, P, Cootes, TF & Lindner, C 2016, 'A benchmark for comparison of dental radiography analysis algorithms', Medical Image Analysis, vol. 31, pp. 63-76.

[18] Gao, Hui & Chae, Oksam. (2010). Individual tooth segmentation from CT images using level set method with shape and intensity prior. Pattern Recognition. 43. 2406-2417. 10.1016/j.patcog.2010.01.010.

[19] Yusra Y. Amer, Musbah J. Aqel, An Efficient Segmentation Algorithm for Panoramic Dental Images, Procedia Computer Science, Volume 65,2015, Pages 718-725, ISSN 1877-0509,

[20] Marques Lira, Pedro Henrique et al. “Dental R-Ray Image Segmentation Using Texture Recognition.” IEEE Latin America Transactions 12 (2014): 694-698.

[21] Poonsri, A., Aim Jirakul, N., Charoenpong, T., & Sukjamsri, C. (2016). Teeth segmentation from dental x-ray image by template matching. 2016 9th Biomedical Engineering International Conference (BMEiCON), 1-4.

[22] Rad, A.E., Rahim, M.S., Kumoi, R., and Norouzi, A. (2013). Dental x-ray image segmentation and multiple feature extraction. Global Journal on Technology, 2.

[23] Indraswari, R., Arifin, A.Z., Navastara, D.A., & Jawas, N. (2015). Teeth segmentation on dental panoramic radiographs using decimation-free directional filter bank thresholding and multistage adaptive thresholding. 2015 International Conference on Information & Communication Technology and Systems (ICTS), 49-54.

[24] Razali et al. (2015) Razali, M. R. M., Ahmad, N. S., Hassan, R., Zaki, Z. M., and Ismail, W. (2015). Sobel and canny edges segmentations for the dental age assessment. In Intl. Conference on Computer Assisted System in Health, pages 62–66.

[25] Li et al. (2006) Li, S., Fevens, T., Krzyzak, A., and Li, S. (2006). An automatic variational level set segmentation framework for computer aided dental x-rays analysis in clinical environments. Computerized Medical Imag. and Graph., 30(2):65–74.

[26] Jader G. (2019, February). Deep instance segmentation of teeth in panoramic X-ray images.

https://github.com/IvisionLab/deep-dental-image .

[27] Chan, Tony F. and Luminita A. Vese. “Active contours without edges.” IEEE transactions on image processing : a publication of the IEEE Signal Processing Society 10 2 (2001): 266-77 .

[28] Piotr Skalski (2019, February). Makesense . https://github.com/SkalskiP/make-sense.

 

Cite this Article as :
Style #
MLA Sireesha Rodda, Vaibhav Kovela , Sanjay Dokula. "Instance Segmentation and Labeling of Teeth from Dental X-Ray using Region Based Convolutional Neural Network." Full Length Article, Vol. 2, No. 2, 2022 ,PP. 20-30 (Doi   :  https://doi.org/10.54216/JNFS.020202)
APA Sireesha Rodda, Vaibhav Kovela , Sanjay Dokula. (2022). Instance Segmentation and Labeling of Teeth from Dental X-Ray using Region Based Convolutional Neural Network. Journal of Full Length Article, 2 ( 2 ), 20-30 (Doi   :  https://doi.org/10.54216/JNFS.020202)
Chicago Sireesha Rodda, Vaibhav Kovela , Sanjay Dokula. "Instance Segmentation and Labeling of Teeth from Dental X-Ray using Region Based Convolutional Neural Network." Journal of Full Length Article, 2 no. 2 (2022): 20-30 (Doi   :  https://doi.org/10.54216/JNFS.020202)
Harvard Sireesha Rodda, Vaibhav Kovela , Sanjay Dokula. (2022). Instance Segmentation and Labeling of Teeth from Dental X-Ray using Region Based Convolutional Neural Network. Journal of Full Length Article, 2 ( 2 ), 20-30 (Doi   :  https://doi.org/10.54216/JNFS.020202)
Vancouver Sireesha Rodda, Vaibhav Kovela , Sanjay Dokula. Instance Segmentation and Labeling of Teeth from Dental X-Ray using Region Based Convolutional Neural Network. Journal of Full Length Article, (2022); 2 ( 2 ): 20-30 (Doi   :  https://doi.org/10.54216/JNFS.020202)
IEEE Sireesha Rodda, Vaibhav Kovela, Sanjay Dokula, Instance Segmentation and Labeling of Teeth from Dental X-Ray using Region Based Convolutional Neural Network, Journal of Full Length Article, Vol. 2 , No. 2 , (2022) : 20-30 (Doi   :  https://doi.org/10.54216/JNFS.020202)
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