Skip to main content
SpringerLink
Log in

Local image segmentation model via Hellinger distance

  • Original article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

Highly accurate active contour models are widely used in various image segmentation methods. In this paper, we propose an image segmentation model based on Hellinger distance for local region intensity fitting (HD-LRIF). The method defines two different metrics based on the Hellinger distance and constructs a new data fitting term to segment the image efficiently by minimizing the energy function. In addition, our method is independent of the initial contour and the segmentation results consistently obtain high accuracy. The experimental results show that the HD-LRIF model is far superior to state-of-the-art segmentation methods in terms of accuracy and efficiency. Specifically, it can effectively filter the noise interference, thus enhancing robustness and improving the accuracy of image segmentation in general.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data availability

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

References

  1. Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models[J]. Int. J. Comput. Vis. 1(4), 321–331 (1988)

    Article  Google Scholar 

  2. Li, C.M., Xu, C.Y., Gui, C.F.: Distance regularized level set evolution and its application to image segmentation[J]. IEEE Trans. Image Process. 19(12), 3243–3254 (2010)

    Article  MathSciNet  Google Scholar 

  3. Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours[J]. Int. J. Comput. Vision 22, 61–79 (1997)

    Article  Google Scholar 

  4. Li, C., Liu, J., Fox, M.D.: Segmentation of external force field for automatic initialization and splitting of snakes[J]. Pattern Recogn. 38(11), 1947–1960 (2005)

    Article  Google Scholar 

  5. Li, C., Kao, C. Y., Gore, J. C., et al.: Implicit active contours driven by local binary fitting energy[C]. Computer Vision and Pattern Recognition, (2007)

  6. Pan, H.Z., Liu, W.Q., Zhou, G.L.: A novel level set approach for image segmentation with landmark constraints[J]. Int. J. Light Electron Opt. 182, 257–268 (2019)

    Article  Google Scholar 

  7. Zhang, K.H., Song, H., Zhang, L.: Active contours driven by local image fitting energy[J]. Pattern Recogn. 43(4), 1199–1206 (2010)

    Article  Google Scholar 

  8. Min, H., Lu, J.T., Jia, W., et al.: An effective local regional model based on salient fitting for image segmentation[J]. Neurocomputing 311, 245–259 (2018)

    Article  Google Scholar 

  9. Wang, L., Chang, Y., Wang, H.: An active contour model based on local fitted images for image segmentation[J]. Inf. Sci. 418, 61–73 (2017)

    Article  Google Scholar 

  10. Canny, J.: A computational approach to edge detection[J]. IEEE Trans. Pattern Anal. Mach. Intell. 6(8), 679–698 (1986)

    Article  Google Scholar 

  11. Sobel, I.: Camera models and machine perception[J]. Dissertation Stanford University, (1970)

  12. Liu, C., Liu, W., Xing, W.: A weighted edge-based level set method based on multi-local statistical information for noisy image segmentation[J]. J. Vis. Commun. Image Rep. 59, 89–107 (2019)

    Article  Google Scholar 

  13. Zhi, X., Shen, H.B.: Saliency driven region-edge-based top down level set evolution reveals the asynchronous focus in image segmentation[J]. Pattern Recogn. J. Pattern Recogn. Soc. 80, 241–255 (2018)

    Article  Google Scholar 

  14. Chan, T., Vese, L.: Active contour without edges[J]. IEEE Trans. Image Process. 10(2), 266–277 (2001)

    Article  Google Scholar 

  15. Mumford, D., Shah, J.: Optimal approximations by piecewise smooth functions and associated variational problems[J]. Commun. Pure Appl. Math. 42(5), 577–685 (1989)

    Article  MathSciNet  Google Scholar 

  16. Ma, C., He, T., Gao, J.: Skin scar segmentation based on saliency detection[J]. Vis. Comput. 1–13 (2022)

  17. Chen, L. C., Papandreou, G., Kokkinos, I., et al.: Semantic image segmentation with deep convolutional nets and fully connected CRFs[C]. International Conference on Learning Representations, (2014)

  18. Chen, L.C., Papandreou, G., Kokkinos, I., et al.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Trans. Pattern Anal. Mach. Intell. 4(40), 834–848 (2018)

    Article  Google Scholar 

  19. Chen, L. C., Papandreou, G., Schroff, F., et al.: Rethinking atrous convolution for semantic image segmentation[J]. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, 851–859

  20. Ouyang, S., Li, Y.: Combining deep semantic segmentation network and graph convolutional neural network for semantic segmentation of remote sensing Imagery[J]. Remote Sens. 1(13), 119–140 (2020)

    Article  Google Scholar 

  21. Wang, D., Hu, G., Lyu, C.: FRNet: an end-to-end feature refinement neural network for medical image segmentation[J]. Vis. Comput. 5(37), 1101–1112 (2021)

    Article  Google Scholar 

  22. Basu, A., Mandal, A., Pardo, L.: Hypothesis testing for two discrete populations based on the Hellinger distance[J]. Statist. Probab. Lett. 80, 206–214 (2010)

    Article  MathSciNet  Google Scholar 

  23. Merabet, Y. E., Ruichek, Y., Ghaffarian, S., et al.: Hellinger Kernel-based distance and local image region descriptors for sky region detection from fisheye images[C]. International Joint Conference on Computer Vision, 2017.

  24. Bouhlel, N., Meric, S.: Multilook polarimetric SAR change detection using stochastic distances between Matrix-Variate G(d)(0) distributions[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 1–21.

  25. Wang, B., Tao, D., Dong, R., et al.: A contour co-tracking method for image pairs[J]. IEEE Trans. Image Process. 99, 5402–5412 (2021)

    Article  Google Scholar 

  26. Arbelaez, P., Maire, M., Fowlkes, C., et al.: Contour detection and hierarchical image segmentation[J]. IEEE Trans. Pattern Anal. Mach. Intell. 33, 898–916 (2011)

    Article  Google Scholar 

  27. Liu, X.G., Liu, G.J., Wang, Y.Z., Li, G.S., Zhang, R., Peng, W.C.: A variational level set image segmentation method via fractional differentiation[J]. Fractal Fract. 6(9), 462 (2022)

    Article  Google Scholar 

  28. Ma, D., Liao, Q., Chen, Z., et al.: Adaptive local-fitting-based active contour model for medical image segmentation[J]. Signal Process. Image Commun. 76, 201–213 (2019)

    Article  Google Scholar 

  29. Zhang, K.H., Liu, Q.S., Song, H.: A variational approach to simultaneous image segmentation and bias correction[J]. Trans. Cybern. 45(8), 1426–1437 (2014)

    Article  Google Scholar 

  30. Wang, L., He, L., Mishra, A.: Active contours driven by local Gaussian distribution fitting energy[J]. Signal Process. 89(12), 2435–2447 (2009)

    Article  Google Scholar 

  31. Samad, W., Chunming, L., Mudassar, I., et al.: Level-set evolution for medical image segmentation with alternating direction method of multipliers[J]. Signal Process. 211, 109105 (2023)

    Article  Google Scholar 

  32. Weng, G.R., XiaoL, F., Ding, K.Y.: Active contours driven by region-scalable fitting and optimized Laplacian of Gaussian energy for image segmentation[J]. Signal Process. 134, 224–233 (2017)

    Article  Google Scholar 

  33. Ding, K., Xiao, L., Weng, G.: Active contours driven by local pre-fitting energy for fast image segmentation[J]. Pattern Recogn. Lett. 104, 29–36 (2018)

    Article  Google Scholar 

  34. Fang, J., Liu, H., Zhang, L., et al.: Fuzzy region-based active contours driven by weighting global and local fitting energy[J]. IEEE Access 99, 1–19 (2019)

    Article  Google Scholar 

  35. Weng, G., Dong, B., Lei, Y.: A level set method based on additive bias correction for image segmentation[J]. Expert Syst. Appl. 185, 1–13 (2021)

    Article  Google Scholar 

  36. Wan, M., Huang, Q., Xu, Y., Gu, G., et al.: Global and local multi-feature fusion-based active contour model for infrared image segmentation[J]. Displays 78, 102452 (2023)

    Article  Google Scholar 

  37. Liu, X., Liu, G., Wang, Y., Li, G., Zhang, R., Peng, W.: A variational level set image segmentation method via fractional differentiation[J]. Fractal Fract. 6, 462 (2022)

    Article  Google Scholar 

  38. Pritpal, S., Surya, S.B.: A quantum-clustering optimization method for COVID-19 CT scan image segmentation[J]. Expert Syst. Appl. 186, 1–21 (2021)

    Google Scholar 

  39. Shu, X., Yang, Y., Wu, B.: Adaptive segmentation model for liver CT images based on neural network and level set method[J]. Neurocomputing 4532021, 438–452 (2021)

    Article  Google Scholar 

  40. Saman, S., Narayanan, S.J.: Active contour model driven by optimized energy functionals for MR brain tumor segmentation with intensity inhomogeneity correction[J]. Multim. Tools Appl. 80, 21925–21954 (2021)

    Article  Google Scholar 

Download references

Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (Grant No. 62061040, 12162029), in part by the Scientific Research Fund of Ningxia University (Grant No. NYG2022018), and in part by the Key Research and Development Plan in Ningxia District under Grant 2019BEG03056.

Author information

Authors and Affiliations

  1. School of Mathematics and Statistics, Ningxia University, Yinchuan, 750021, China

    Guojun Liu, Jianhui Guo, Yazhen Wang, Xiangguo Liu, Wentao Ma & Rui Zhang

Authors
  1. Guojun Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianhui Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yazhen Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiangguo Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wentao Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Rui Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianhui Guo.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, G., Guo, J., Wang, Y. et al. Local image segmentation model via Hellinger distance. Vis Comput (2023). https://doi.org/10.1007/s00371-023-03213-1

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00371-023-03213-1

Keywords

Search

Navigation