Abstract
Automatic tongue image segmentation is a key technology for the research on tongue characterization in Traditional Chinese Medicine. Due to the complexity of automatic tongue image segmentation, the automation degree and segmentation precision of the existing methods for tongue image segmentation are not satisfied. To address the above problem, a method of automatic tongue image segmentation using deep neural network is proposed in this paper. In our method, an image quality evaluation method based on brightness statistics is proposed to judge whether the input image is to be segmented, and the SegNet is employed to train on the TongueDataset1 and TongueDataset2 to obtain the deep model for automatic tongue image segmentation. TongueDataset1 and TongueDataset2 are specially constructed for tongue image segmentation. The experimental results on TongueDataset1 and TongueDataset2 show that the mean intersection over union score can reach to 95.89% and 90.72%, respectively. Compared with the traditional methods of tongue image segmentation, our method can avoid the complicated process of extracting features manually, and has obvious superiority in the segmentation performance.
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References
Shen, L., Cai, Y., Zhang, X.: Collection and analysis of TCM Tongue. Beijing University of Technology Press, Beijing (2007)
Zhao, Z., Wang, A., Shen, L.: Color tongue image segmentation method based on mathematical morphology and HIS model. J. Beijing Univ. Technol. 25(2), 67–71 (1999)
Xu, W., Kanawong, R., Xu, D., Li, S., Ma, T., Zhang, G., Duan, Y.: An automatic tongue detection and segmentation framework for computer-aided tongue image analysis. In: 13th International Conference on e-Health Networking Applications and Services, pp. 189–192. IEEE, Piscataway (2011)
Wang, A., Shen, L., Zhao, Z.: Color tongue image segmentation using fuzzy kohonen networks and genetic algorithm. In: Nasser, K. (ed.) Applications of Artificial Neural Networks in Image Processing, Electronic Imaging, vol. 3962, pp. 182–190. SPIE, San Jose (2000)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 1, 321–331 (1988)
Shen, S., Wang, M.: Research on the classification of image segmentation. J. Beijing Univ. Technol. 26(3), 33–38 (2000)
Shi, M., Li, G., Li, F.: C2G2FSnake: an automatic tongue image segmentation algorithm based on prior knowledge. Sci. China Inf. Sci. 56(9), 1–14 (2013)
Guo, J., Yang, Y., Wu, Q., Su, J., Ma, F.: Adaptive active contour model based automatic tongue image segmentation. In: 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, pp: 1386–1390 (2016)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks, In: International Conference on Neural Information Processing Systems, pp. 1097–1105. Curran Associates Inc. (2012)
Long, S., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2015)
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Yuille: Semantic image segmentation with deep convolutional nets and fully connected CRFs. Comput. Sci. 4, 357–361 (2014)
Zheng, Y., Romera-Paredes, V., Su, D., Huang, T.: Conditional random fields as Recurrent Neural Networks. In: IEEE International Conference on Computer Vision, pp: 1529–1537 (2015)
Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. arXiv preprint arXiv:1511.00561 (2015)
Simonyan, Z.: Very deep convolutional networks for large-scale image recognition. Comput. Sci. (2014)
Ioffe, S.: Batch Normalization: accelerating deep network training by reducing internal covariate shift. Comput. Sci. (2015)
Acknowledgment
The work in this paper is supported by BJUT United Grand Scientific Research Program on Intelligent Manufacturing, the National Natural Science Foundation of China (No.61531006, No.61372149, No.61370189, No.61471013 and No.61602018), the Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions (No.CIT&TCD20150311, CIT&TCD201404043), the Beijing Natural Science Foundation (No.4142009, No.4163071), the Science and Technology Development Program of Beijing Education Committee (No. KM201510005004), Funding Project for Academic Human Resources Development in Institutions of Higher Learning Under the Jurisdiction of Beijing Municipality.
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Qu, P., Zhang, H., Zhuo, L., Zhang, J., Chen, G. (2017). Automatic Tongue Image Segmentation for Traditional Chinese Medicine Using Deep Neural Network. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10361. Springer, Cham. https://doi.org/10.1007/978-3-319-63309-1_23
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DOI: https://doi.org/10.1007/978-3-319-63309-1_23
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