Abstract
During the process of tongue diagnosis automation, the segmentation of tongue body from the original image is an important part. Tongue body is a target with irregular edge, and the color of local area is similar to that of lips, so it is difficult to achieve accurate segmentation with traditional segmentation methods. In this paper, we present a tongue image segmentation method based on deep learning. We first apply semantic segmentation to tongue segmentation that use fully convolutional networks to automatically segment tongue body based on preserving tongue shape semantic information. Moreover, we try to combine it with traditional algorithm to optimize the results. The experimental results show that the semantic segmentation method based on neural network is superior to the traditional algorithm in accuracy and efficiency. In addition, comparing with traditional algorithms, the method does not require manual label, which greatly reduces the workload.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Pang, B., Zhang, D., Wang, K.: Tongue image analysis for appendicitis diagnosis. Inf. Sci. 175(3), 160–176 (2005)
Maciocia, G.: Tongue Diagnosis in Chinese Medicine, vol. 16. Eastland Press, Seattle (1995)
Zhang, H.Z., Wang, K.Q., Zhang, D., Pang, B., Huang, B.: Computer aided tongue diagnosis system. In: 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005, pp. 6754–6757. IEEE (2006)
Chiu, C.C.: A novel approach based on computerized image analysis for traditional Chinese medical diagnosis of the tongue. Comput. Methods Programs Biomed. 61(2), 77–89 (2000)
Pang, B., Zhang, D.D., Li, N., Wang, K.: Computerized tongue diagnosis based on Bayesian networks. IEEE Trans. Biomed. Eng. 51(10), 1083 (2004)
Ning, J., Zhang, D., Wu, C., Yue, F.: Automatic tongue image segmentation based on gradient vector flow and region merging. Neural Comput. Appl. 21(8), 1819–1826 (2012)
Shi, M.J., Li, G.Z., Li, F.F.: C2G2FSnake: automatic tongue image segmentation utilizing prior knowledge. Sci. China Inf. Sci. 56(9), 1–14 (2013)
Pang, B., Zhang, D., Wang, K.: The bi-elliptical deformable contour and its application to automated tongue segmentation in Chinese medicine. IEEE Trans. Med. Imaging 24(8), 946–956 (2005)
Zheng, Y., Kambhamettu, C.: Learning based digital matting. In: IEEE 12th International Conference on Computer Vision, pp. 889–896. IEEE (2009)
Li, X., Cui, Q.: Parallel accelerated matting method based on local learning. In: Lai, S.-H., Lepetit, V., Nishino, K., Sato, Y. (eds.) ACCV 2016. LNCS, vol. 10111, pp. 152–162. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-54181-5_10
Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. ar**v preprint ar**v:1511.07122 (2015)
Zheng, S., Jayasumana, S., Romera-Paredes, B., et al.: Conditional random fields as recurrent neural networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1529–1537 (2015)
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Semantic image segmentation with deep convolutional nets and fully connected CRFs. In: Computer Science, vol. 4, pp. 357–361 (2014)
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2018)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. ar**v preprint ar**v:1706.05587 (2017)
Shen, X., Tao, X., Gao, H., Zhou, C., Jia, J.: Deep automatic portrait matting. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 92–107. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_6
Russakovsky, O., Deng, J., Su, H., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 346–361. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10578-9_23
Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: 12th USENIX Symposium on Operating Systems Design and Implementation, pp. 265–283 (2016)
Acknowledgement
This work was supported by the Shanghai Innovation Action Plan Project under Grant 16511101200.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Xue, Y., Li, X., Wu, P., Li, J., Wang, L., Tong, W. (2018). Automated Tongue Segmentation in Chinese Medicine Based on Deep Learning. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11307. Springer, Cham. https://doi.org/10.1007/978-3-030-04239-4_49
Download citation
DOI: https://doi.org/10.1007/978-3-030-04239-4_49
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04238-7
Online ISBN: 978-3-030-04239-4
eBook Packages: Computer ScienceComputer Science (R0)