Abstract
In the procedure of the Chinese medical tongue diagnosis, it’s necessary to carry out the original tongue image segmentation to reduce interference to the tongue feature extraction caused by the non-tongue part of the face. In this paper, we propose a new method based on enhanced HSV color model convolutional neural network for tongue image segmentation. This method can get a better in tongue image segmentation results compared with others. This method also has a great advantage over other methods in the processing speed.
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
Yuan, L., Liw, E., Yao, J., et al.: Research progress of information processing technology on tongue diagnosis of traditional Chinese medicine. Acta Univ. Tradit. Med. Sin. Pharmacol. Shanghai 25(02), 80–86 (2011)
Guo, R., Wang, Y.-Q., Yan, J.-J., et al.: Study on the objectivity of traditional Chinese medicinal tongue inspection. Chin. J. Integr. Tradit. West. Med. 29(07), 642–645 (2009)
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)
Sun, X., Pang, C.: An improved snake model method on tongue segmentation. J. Chang. Univ. Sci. Technol. 36(5), 154–156 (2013)
Wang, K., Guo, Q., Zhuang, D.: An image segmentation method based on the improved snake model. In: IEEE International Conference on Mechatronics and Automation, pp. 532–536 (2006)
Xu, C.Y., Prince, J.L.: Snakes and gradient vector flow. IEEE Trans. Image Process. 7(3), 359–369 (1998)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1988)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25, no. 2 (2012)
Sun, Y., Wang, X., Tang, X.: Deep learning face representation from predicting 10,000 classes. In: Computer Vision and Pattern Recognition, pp. 1891–1898. IEEE (2014)
Zuiderveld, K.: Contrast Limited Adaptive Histogram Equalization. Graphics Gems, pp. 474–485. Academic Press, San Diego (1994)
Zhang, L.: Contrast limited adaptive histogram equalization. Comput. Knowl. Technol. (2010)
Lecun, Y., Bengio, Y.: Convolutional Networks for Images, Speech, and Time Series. The Handbook of Brain Theory and Neural. MIT Press, Cambridge (1997)
Unsupervised Feature Learning and Deep Learning. http://ufldl.stanford.edu/
Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: International Conference on Artificial Intelligence and Statistics (2011)
Hinton, G.E., Srivastava, N., Krizhevsky, A., et al.: Improving neural networks by preventing co-adaptation of feature detectors. Comput. Sci. 3(4), 212–223 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Li, J., Xu, B., Ban, X., Tai, P., Ma, B. (2017). A Tongue Image Segmentation Method Based on Enhanced HSV Convolutional Neural Network. In: Luo, Y. (eds) Cooperative Design, Visualization, and Engineering. CDVE 2017. Lecture Notes in Computer Science(), vol 10451. Springer, Cham. https://doi.org/10.1007/978-3-319-66805-5_32
Download citation
DOI: https://doi.org/10.1007/978-3-319-66805-5_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-66804-8
Online ISBN: 978-3-319-66805-5
eBook Packages: Computer ScienceComputer Science (R0)