SpringerLink
Log in

DSE-Net: Deep Semantic Enhanced Network for Mobile Tongue Image Segmentation

  • Conference paper
  • First Online:
Neural Information Processing (ICONIP 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1794))

Included in the following conference series:

Abstract

Tongue diagnosis plays an important role in traditional Chinese medicine (TCM) because of noninvasive for health assessment. Taking advantage of the portability of mobile devices to develop a tongue diagnosis system has aroused widespread concern in artificial intelligence community. However, mobile tongue image segmentation is challenging on account of low-quality image and limited computing power. In this paper, we propose a deep semantic enhanced (DSE) network to address these issues. DSE-Net consists of a lightweight feature extraction module, efficient deep semantic enhanced module and the decoder. The encoder adopts shufflenetv2 units as backbone for the reduction of computing pressure from mobile devices and the DSE module with multi-scale feature aggregation is designed to improve the network’s recognition of tongue position. In addition, the decoder is designed not only to recover semantics, but also to embed global features from the shallow network for further boosting the retrieval performance. Extensive experiments are conducted on two diverse tongue image benchmarks, including the public tongue dataset collected by special image acquisition device and the lab-made dataset gathered by mobile phones in various uncontrolled environments. The experimental results show the proposed method’s efficiency and accuracy which outperforms the state-of-the-art methods for mobile tongue image segmentation.

This work was supported in part by the National Natural Science Foundation of China under Grant No. 61876037, the Natural Science Foundation of Jiangsu Province of China under Grant No. BK20221345 and Postgraduate Research & Practice Innovation Program of Jiangsu Province (CWQXW21001).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    http://github.com/BioHit/TongueImageDat.

References

  1. Zhang, L., Qin, J.: Tongue-image segmentation based on gray projection and threshold-adaptive method. Chin. J. Tissue Eng. Res. 14(9), 1638 (2010)

    Google Scholar 

  2. Zhi, L., Yan, J., Zhou, T.: Tongue shape detection based on B-spline. In: International Conference on Machine Learning and Cybernetics, pp. 3829–3832 (2006)

    Google Scholar 

  3. Fu, H.G., Wang, W.M., Yang, J.H., Wu, R.Q.: Automatic tongue image segmentation. Inf. Comput. Autom., 790–794 (2008)

    Google Scholar 

  4. 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)

    Google Scholar 

  5. Chen, L.C., Papandreou, G., Kokkinos, I.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2017)

    Article  Google Scholar 

  6. Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. ar**v preprint ar**v:1706.05587 (2017)

  7. Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833–851. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_49

    Chapter  Google Scholar 

  8. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L. C.: MobileNetV2: inverted residuals and linear bottlenecks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)

    Google Scholar 

  9. Howard, A., et al.: Searching for MobileNetV3. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1314–1324 (2019)

    Google Scholar 

  10. Tang, H., Wang, B. et al.: DE-Net: dilated encoder network for automated tongue segmentation. 2020 25th International Conference on Pattern Recognition (ICPR), pp. 2575–2581. IEEE (2021)

    Google Scholar 

  11. Ma, N., Zhang, X., Zheng, H.-T., Sun, J.: ShuffleNet V2: practical guidelines for efficient CNN architecture design. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018. LNCS, vol. 11218, pp. 122–138. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01264-9_8

    Chapter  Google Scholar 

  12. Gu, Z., Cheng, J., Fu, H.: CE-Net: context encoder network for 2D medical image segmentation. IEEE Trans. Med. Imaging 38(10), 2281–2292 (2019)

    Article  Google Scholar 

  13. Rezatofighi, H., Tsoi, N., Gwak, J.: Generalized intersection over union: a metric and a loss for bounding box regression. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 658–666 (2019)

    Google Scholar 

  14. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234–241 (2015)

    Google Scholar 

  15. Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)

    Article  Google Scholar 

  16. Zhao, H., et al.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2881–2890 (2017)

    Google Scholar 

  17. Chaurasia, A., Culurciello, E.: LinkNet: exploiting encoder representations for efficient semantic segmentation. In: 2017 IEEE Visual Communications and Image Processing (VCIP), pp. 1–4 (2017)

    Google Scholar 

  18. Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: BiSeNet: bilateral segmentation network for real-time semantic segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 334–349. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01261-8_20

    Chapter  Google Scholar 

  19. Li, H., **ong, P., Fan, H., Sun, J.: DFANet: deep feature aggregation for real-time semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9522–9531 (2019)

    Google Scholar 

  20. Zhang, X., Zhou, X., Lin, M., Sun, J.: ShuffleNet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6848–6856 (2018)

    Google Scholar 

  21. Han, K., Wang, Y., Tian, Q., et al.: GhostNet: more features from cheap operations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1580–1589 (2020)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Nan**g University of Finance and Economics, Nan**g, 210023, China

    Wanqiang Cai & Bin Wang

Authors
  1. Wanqiang Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bin Wang .

Editor information

Editors and Affiliations

  1. Indian Institute of Technology Indore, Indore, India

    Mohammad Tanveer

  2. Indian Institute of Information Technology - Allahabad, Prayagraj, India

    Sonali Agarwal

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

  4. Indian Institute of Technology Patna, Patna, India

    Asif Ekbal

  5. University of Innsbruck, Innsbruck, Austria

    Adam Jatowt

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Cai, W., Wang, B. (2023). DSE-Net: Deep Semantic Enhanced Network for Mobile Tongue Image Segmentation. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1794. Springer, Singapore. https://doi.org/10.1007/978-981-99-1648-1_12

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-1648-1_12

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-1647-4

  • Online ISBN: 978-981-99-1648-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation