Multimedia Tools and Applications

, Volume 78, Issue 23, pp 33921–33937 | Cite as

An adaptable active contour model for medical image segmentation based on region and edge information

  • Xianghai WangEmail author
  • Wei Li
  • Chong Zhang
  • Wanqi Lou
  • Ruoxi SongEmail author


Due to the complexity of the internal structure of human body and the physiological movement of the illuminated tissue, the digital medical image exists low contrast, high noise intensity, complex internal structure and edge blur phenomenon, which will limit the segmentation accuracy of traditional active contour models. To solve this problem, this paper proposes a novel active contour model based on the combination of regional information and the edge information of the image. The new approach has four key characteristics. First the local information fitting of the image is incorporated into the pressure force function (SPF) of the SBGFRLS model, which improves the ability of dealing with medical images with low contrast and complex structure. Second, the adaptive balance of local information and global information is realized by adding a novel weighting function, which accelerates the evolution speed and enhances the adaptability of the model; Third, in the numerical implementation process of the proposed model, the divergence operator is replaced by the Gaussian filter, in this way, the level set function is smoothed and the computation is simplified. Last, a penalty term of symbolic function is introduced to reduce the computational complexity of the level set function due to re-initialization and regularization process. In order to verify the effectiveness of the model, we use different kinds of medical images for simulation experiments. Experimental results show that compared with the traditional active contour models, the proposed method can achieve an satisfactory both in segmentation speed and accuracy.


Medical image segmentation Adaptable active contour model Global information Local information Edge information Adaptive balance function 



Computed tomography


Geometric active contour


Local binary fitting


Magnetic resonance


Funding information

This research has been funded by the National Natural Science Foundation of China (Grant Nos. 41671439 and 61402214), and Innovation Team Support Program of Liaoning Higher Education Department (LT2017013).

Compliance with ethical standards

Competing interests

The authors declare that they have no competing interests.


  1. 1.
    Bibi I, Liu F, Razi A et al (2017) Image segmentation by active contour model using hyperbolic trigonometric formulation. Signal Processing, Communications and Computing (ICSPCC), 2017 IEEE International Conference on. IEEE. p 1–6Google Scholar
  2. 2.
    Caselles V, Kimmel R, Sapiro G (1997) Geodesic active contours. Int J Comput Vis 22(1):61–79zbMATHCrossRefGoogle Scholar
  3. 3.
    Chan TF, Vese L (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277zbMATHCrossRefGoogle Scholar
  4. 4.
    Cheng J, Foo SW (2006) Dynamic directional gradient vector flow of snake. IEEE Trans Image Process 15(6):1563–1571CrossRefGoogle Scholar
  5. 5.
    Cohen LD (1991) On active contour models and balloons. CVGIP: Image Understanding 53(2):211–218MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Hemalatha RJ, Vijaybaskar V, Thamizhvani TR (2018) Performance evaluation of contour based segmentation methods for ultrasound images. Advances in Multimedia 2018:Article ID 4976372, 8 pages. CrossRefGoogle Scholar
  7. 7.
    Li C, Kao CY, Gore JC et al (2008) Minimization of region-scalable fitting energy for image segmentation. IEEE Trans Image Process 17(10):1940–1949MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Liu TT, Xu H, Jin W et al (2014) Medical image segmentation based on a hybrid region-based active contour model. Comput Math Methods Med. Article ID 890725, 10 pageszbMATHGoogle Scholar
  9. 9.
    Liu Y, Nie L, Liu L (2016) From action to activity: sensor-based activity recognition. Neurocomputing 181(12):108–115CrossRefGoogle Scholar
  10. 10.
    Liu Y, Nie L , Han L (2015). Action2Activity: recognizing complex activities from sensor data. In Proceedings of the International Joint Conference on Artificial Intelligence, pp. 1617–1623Google Scholar
  11. 11.
    Liu W, Yang X, Tao D, Cheng J, Tang Y (2018) Multiview dimension reduction via hessian multiset canonical correlations. Information Fusion 41:119–128CrossRefGoogle Scholar
  12. 12.
    Min H, Jia W, Wang XF et al (2015) An intensity-texture model based level set method for image segmentation. Pattern Recogn 48(4):1547–1562CrossRefGoogle Scholar
  13. 13.
    Mumford D, Shah J (1989) Optimal approximations by piecewise smooth functions and associated variational problems. Commun Pure Appl Math 42(5):577–685MathSciNetzbMATHCrossRefGoogle Scholar
  14. 14.
    Shi N, Pan JX (2016) An improved active contours model for image segmentation by level set method. Optik 127(3):1037–1042CrossRefGoogle Scholar
  15. 15.
    Shivapatham G, Loganathan T (2017) Overview of medical image segmentation process of selected magnetic resonance images; manual segmentation and active contour/snake model. International Journal for Scientific Research and Development 5(5):618–621Google Scholar
  16. 16.
    Tang J, Millington S (2006) Surface extraction and thickness measurement of the auricular cartilage from MR images using directional gradient vector flow snake. IEEE Trans Biomed Eng 53(5):896–907CrossRefGoogle Scholar
  17. 17.
    Tian Y, Duan FQ (2013) Active contour model combining region and edge information. Machine Vision and Applications 24(1):47–61CrossRefGoogle Scholar
  18. 18.
    Tu S, Li Y, Su Y (2015) Overview of SAR image segmentation based on active contour model. J Syst Eng Electron 37(8):1754–1766Google Scholar
  19. 19.
    Wang XH, Fang LL (2013) Survey of image segmentation based on active contour model. Int J Pattern Recognit Artif Intell 26(8):751–760Google Scholar
  20. 20.
    Wang XH, Song RX, Zhang C et al (2016) Image segmentation model based on adaptive adjustment of global and local information. Int J Imaging Syst Technol 26(3):179–187CrossRefGoogle Scholar
  21. 21.
    Xu C, Prince JL (1998) Snakes, shapes and gradient vector flow. IEEE Trans Image Process 7(3):359–369MathSciNetzbMATHCrossRefGoogle Scholar
  22. 22.
    Yu J, Rui Y, Tao D (2014) Click prediction for web image reranking using multimodal sparse coding. IEEE Trans Image Process 23(5):2019–2032MathSciNetzbMATHCrossRefGoogle Scholar
  23. 23.
    Yu J, Yang X, Gao F, Tao D (2017) Deep multimodal distance metric learning using click constraints for image ranking. IEEE Transactions on Cybernetics 47(12):4014–4024CrossRefGoogle Scholar
  24. 24.
    Zahir M, Mourad O, Abdelaziz O (2013) Performance comparison of active contour level set methods in image segmentation, Systems, Signal Processing and their Applications (WoSSPA), 2013 8th International Workshop on IEEEGoogle Scholar
  25. 25.
    Zanaty EA, Ghoniemy S (2016) Medical image segmentation techniques: an overview. International Journal of Informatics and Medical Data Processing 1(1):16–37Google Scholar
  26. 26.
    Zhang K, Zhang L, Song H (2010) Active contours with selective local or global segmentation: a new formulation and level set method. Proc IEEE Conf Comput Vis Pattern Recognit 28(4):668–676Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Computer and Information TechnologyLiaoning Normal UniversityDalian CityChina
  2. 2.School of MathematicsLiaoning Normal UniversityDalian CityChina
  3. 3.School of Urban and Environmental SciencesLiaoning Normal UniversityDalian CityChina

Personalised recommendations