Skip to main content
Log in

Active contour modal based on density-oriented BIRCH clustering method for medical image segmentation

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Currently, medical image segmentation has attracted more attention from researchers, which can assist in medical diagnosis. However, in the process of traditional medical image segmentation, it is sensitive to the initial contour and noise, which is difficult to deal with the weak edge image, complex iterative process. In this paper, we propose a new medical image segmentation method, which adopts density-oriented BIRCH (balanced iterative reducing and clustering using hierarchies) clustering method to modify active contour model and improve the robustness of noise. The BIRCH is a multi-stage clustering method using clustering feature tree. The improved model can effectively deal with the gray non-uniformity of real medical images. And we also introduce a new energy function in active contour model to make the contour curve approach to the edge, and finally stay at the edge of the image to complete the object segmentation. Experimental results show that this new model can overcome the influence of complex background on medical image segmentation and improve the speed and accuracy of medical segmentation results.

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

Access this article

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

References

  1. Bai X, Li C, Sun Q, et al. (2009) Contrast constrained local binary fitting for image segmentation, International Symposium on Advances in Visual Computing. Springer-Verlag

  2. Bo LI, Liu W, Dou L (2010) Unsupervised learning of Gaussian mixture model with application to image segmentation. Chin J Electron 19(3):451–456

    Google Scholar 

  3. Bonini S, Caivano G, 2014 Development of a LGD model Basel2 compliant: a case study, Mathematical and Statistical Methods for Actuarial Sciences and Finance

  4. Boskovitz V, Guterman H (2002) An adaptive neurofuzzy system for automatic image segmentation and edge detection. Fuzzy Syst IEEE Trans 10(2):247–262

    Article  Google Scholar 

  5. Chen Y, Yue X, Xu RYD, Fujita H (2017) Region scalable active contour model with global constraint. Knowl-Based Syst 120(C):57–73

    Article  Google Scholar 

  6. Gan Y, Xia Z, Xiong J, Zhao Q, Hu Y, Zhang J (2015) Toward accurate tooth segmentation from computed tomography images using a hybrid level set model. Med Phys 42(1):14–27

    Article  Google Scholar 

  7. Gong X, Liu X, Li Y, Li H (2020) A novel co-attention computation block for deep learning based image co-segmentation. Image Vis Comput 101:103973

    Article  Google Scholar 

  8. Huo G, Yang SX, Li Q, Zhou Y (2016) A robust and fast method for Sidescan sonar image segmentation using nonlocal Despeckling and active contour model. IEEE Trans Cybernetics 47(4):855–872

    Article  Google Scholar 

  9. Koh J, Kim T, Chaudhary V, et al. 2010 Automatic segmentation of the spinal cord and the dural sac in lumbar MR images using gradient vector flow field. Eng Med Biol Soc 3117–3120

  10. Lee J, Nishikawa RM (2018) Automated mammographic breast density estimation using a fully convolutional network. Med Phys 45(3):1178–1190

    Article  Google Scholar 

  11. Lin T, Li H, Yin S, Yang S (2019) Improved krill group-based region growing algorithm for image segmentation. Int J Image Data Fusion 10:327–341. https://doi.org/10.1080/19479832.2019.1604574

    Article  Google Scholar 

  12. Liu J, Yin S-L, Li H, Lin T (2017) A density-based clustering method for K-anonymity privacy protection. J Inf Hiding Multimed Signal Process 8(1):12–18

    Google Scholar 

  13. Lu X, Wang W, Ma C, et al. (2019) See more, know more: unsupervised video object segmentation with co-attention Siamese networks [C]// CVPR19

  14. Madan S, Dana KJ (2016) Modified balanced iterative reducing and clustering using hierarchies (m-BIRCH) for visual clustering. Pattern Anal Appl 19(4):1023–1040

    Article  MathSciNet  Google Scholar 

  15. Miao J, Gu X, Ying H et al (2018) Medical Imaging. Int Topical Meeting Image Detect Qual 80(5):1–4

    Google Scholar 

  16. Niu S, Chen Q, Sisternes LD et al (2017) Robust noise region-based active contour model via local similarity factor for image segmentation. Pattern Recogn 61:104–119

    Article  Google Scholar 

  17. Perazzi F, Khoreva A, Benenson R, et al. (2017) Learning video object segmentation from static images [C]// 2017 IEEE conference on computer vision and pattern recognition (CVPR). IEEE Computer Society

  18. Petke J, Jeavons P (2010) Local consistency and SATSolvers. J Artif Intell Res 43(3):398–413

    Google Scholar 

  19. Qi G, LongW SS (2015) Multiple-Channel local binary fitting model for medical image segmentation. Chin J Electron 24(4):802–806

    Article  Google Scholar 

  20. Singh E, Lin D, Barrett C, et al. 2018 Logic bug detection and localization using symbolic quick error detection, IEEE transactions on computer-aided Design of Integrated Circuits and Systems, PP(99):1–1.

  21. Sun G, Lin K, Wang J et al (2019) An enhanced affinity graph for image segmentation. ICE Trans Inf Syst E102–D(5):1073–1080

    Article  Google Scholar 

  22. Tong T, Wolz R, Wang Z, Gao Q, Misawa K, Fujiwara M, Mori K, Hajnal JV, Rueckert D (2015) Discriminative dictionary learning for abdominal multiorgan segmentation. Med Image Anal 23(1):92–104

    Article  Google Scholar 

  23. Vese LA, Chan TF (2002) A multiphase level set framework for image segmentation using the Mumford and Shah model. Int J Comput Vis 50(3):271–293

    Article  MATH  Google Scholar 

  24. Wang HJ, Liu M, Ma WL 2010 Color image segmentation based on a new geometric active contour model. Int Conf Mach Vision Human-Mach Interface

  25. Wang J, Ju L, Wang X (2016) An edge-weighted Centroidal Voronoi tessellation model for image segmentation. Comput Mathematics Appl 71(11):2272–2284

    Article  MathSciNet  MATH  Google Scholar 

  26. Wang G, Li W, Zuluaga MA, Pratt R, Patel PA, Aertsen M, Doel T, David AL, Deprest J, Ourselin S, Vercauteren T (2018) Interactive medical image segmentation using deep learning with image-specific fine tuning. IEEE Trans Med Imaging 37(7):1562–1573

    Article  Google Scholar 

  27. Wang W, Lu X, Shen J, Crandall D, Shao L, Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks 2019 IEEE/CVF international conference on computer vision (ICCV), Seoul, Korea (South): pp 9235–9244, doi: https://doi.org/10.1109/ICCV.2019.00933.

  28. Wang X, Li W, Zhang C, Lou W, Song R (2019) An adaptable active contour model for medical image segmentation based on region and edge information. Multimed Tools Appl 78:33921–33937. https://doi.org/10.1007/s11042-019-08073-3

    Article  Google Scholar 

  29. Yan Z, Yang X, Cheng K-T (2018) Joint segment-level and pixel-wise losses for deep learning based retinal vessel segmentation. IEEE Trans Biomed Eng 65(9):1912–1923

    Article  Google Scholar 

  30. Yin S, Zhang Y (2018) Singular value decomposition based anisotropic diffusion for fusion of infrared and visible images. Int J Image Data Fusion 9(4):1–18

    Google Scholar 

  31. Yin SL, Zhang Y, Karim S (2018) Large scale remote sensing image segmentation based on fuzzy region competition and Gaussian mixture model. IEEE Access 6:26069–26080

    Article  Google Scholar 

  32. Zhang L, Jiang M, Yang M 2010 Hippocampus segmentation based on prior knowledge and GAC model. IEEE Int Conf Signal Process

  33. Zhang K, Zhang L, Lam KM, Zhang D (2016) A level set approach to image segmentation with intensity inhomogeneity. IEEE Trans Cybernetics 46(2):546–557

    Article  Google Scholar 

  34. Zhang M, Jiang W, Zhou X et al (2017) A hybrid biogeography-based optimization and fuzzy C-means algorithm for image segmentation. Soft Comput 1:1–14

    Google Scholar 

Download references

Acknowledgements

This research was funded by Heilongjiang Province science found for returnees (grant number: LC2017027), Jiamusi University Science and Technology Innovation Team Construction Project (grant number: CXTDPY-2016-3), Basic Research Project of Heilongjiang Province Department Of Education (grant number:2016-kyywf-0547).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hang Li.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yin, S., Li, H., Liu, D. et al. Active contour modal based on density-oriented BIRCH clustering method for medical image segmentation. Multimed Tools Appl 79, 31049–31068 (2020). https://doi.org/10.1007/s11042-020-09640-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-09640-9

Keywords

Navigation