A Novel Active Contour Model Using Oriented Smoothness and Infinite Laplacian for Medical Image Segmentation

  • Chunhong CaoEmail author
  • Chengyao Zhou
  • Jie Yu
  • Kai Hu
  • Fen Xiao
Part of the Studies in Computational Intelligence book series (SCI, volume 810)


Active contour model (ACM) has been widely used in image segmentation. The original ACM has poor weak edge preservation ability and it is difficult to converge to the concave, especially long and thin indentation convergence. In order to address these defects, a series of models such as gradient vector flow (GVF) and general gradient vector flow (GGVF) were proposed. A new edge-preserving ACM using oriented smoothness, infinite Laplacian is proposed in this paper to further address these issues. Oriented smoothness and infinite Laplacian are adopted as the smoothness term in the energy function to promote the model’s weak edge preservation and concave convergence ability. Furthermore, we employ a component-based normalization to accelerate the concave convergence rate. The experimental results show that the proposed method achieves better performance than the other comparative methods.


Active contour model Medical image segmentation Oriented smoothness Infinite Laplacian 



This work was supported by the NSFC under Grants 61401386, 61802328.


  1. 1.
    Lu, H., Li, B., Zhu, J., Li, Y., Li, Y., Xu, X., Serikawa, S.: Wound intensity correction and segmentation with convolutional neural networks. Concurr. Comput. Pract. Exp. 29(6), e3927 (2017)CrossRefGoogle Scholar
  2. 2.
    He, L., Peng, Z., Everding, B., Wang, X., Han, C.Y., Weiss, K.L., Wee, W.G.: A comparative study of deformable contour methods on medical image segmentation. Image Vis. Comput. 26(2), 141–163 (2008)CrossRefGoogle Scholar
  3. 3.
    Mustaqeem, A., Javed, A., Fatima, T.: An efficient brain tumor detection algorithm using watershed & thresholding based segmentation. Int. J. Image Graph. Signal Process. 4(10), 34 (2012)CrossRefGoogle Scholar
  4. 4.
    Asari, K.V.: A fast and accurate segmentation technique for the extraction of gastrointestinal lumen from endoscopic images. Med. Eng. Phys. 22(2), 89–96 (2000)CrossRefGoogle Scholar
  5. 5.
    Zhu, S., Gao, R.: A novel generalized gradient vector flow snake model using minimal surface and component-normalized method for medical image segmentation. Biomed. Signal Process. Control 26, 1–10 (2016)CrossRefGoogle Scholar
  6. 6.
    Lu, H., Li, Y., Chen, M., Kim, H., Serikawa, S.: Brain intelligence: go beyond artificial intelligence. Mob. Netw. Appl. 23(2), 368–375 (2018)CrossRefGoogle Scholar
  7. 7.
    Lu, H., Li, Y., Uemura, T., Kim, H., Serikawa, S.: Low illumination underwater light field images reconstruction using deep convolutional neural networks. Futur. Gener. Comput. Syst. 82, 142–148 (2018)CrossRefGoogle Scholar
  8. 8.
    Lu, H., Li, Y., Mu, S., Wang, D., Kim, H., Serikawa, S.: Motor anomaly detection for unmanned aerial vehicles using reinforcement learning. IEEE Internet Things J. 5(4), 2315–2322 (2018)CrossRefGoogle Scholar
  9. 9.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1988)CrossRefGoogle Scholar
  10. 10.
    Xu, C., Prince, J.L.: Snakes, shapes, and gradient vector flow. IEEE Trans. Image Process. 7(3), 359–369 (1998)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Jifeng, N., Chengke, W., Shigang, L., Shuqin, Y.: NGVF: an improved external force field for active contour model. Pattern Recognit. Lett. 28(1), 58–63 (2007)CrossRefGoogle Scholar
  12. 12.
    Qin, L., Zhu, C., Zhao, Y., Bai, H., Tian, H.: Generalized gradient vector flow for snakes: new observations, analysis, and improvement. IEEE Trans. Circuits Syst. Video Technol. 23(5), 883–897 (2013)CrossRefGoogle Scholar
  13. 13.
    Wu, Y., Wang, Y., Jia, Y.: Adaptive diffusion flow active contours for image segmentation. Comput. Vis. Image Underst. 117(10), 1421–1435 (2013)CrossRefGoogle Scholar
  14. 14.
    Xu, C., Prince, J.L.: Generalized gradient vector flow external forces for active contours 1. Signal Process. 71(2), 131–139 (1998)CrossRefGoogle Scholar
  15. 15.
    Wang, Y., Liu, L., Zhang, H., Cao, Z., Lu, S.: Image segmentation using active contours with normally biased GVF external force. IEEE Signal Process. Lett. 17(10), 875–878 (2010)CrossRefGoogle Scholar
  16. 16.
    Nagel, H.H., Enkelmann, W.: An investigation of smoothness constraints for the estimation of displacement vector fields from image sequences. IEEE Trans. Pattern Anal. Mach. Intell. PAMI 8(5), 565–593 (1986)Google Scholar
  17. 17.
    Li, C., Liu, J., Fox, M.D.: Segmentation of external force field for automatic initialization and splitting of snakes. Pattern Recognit. 38(11), 1947–1960 (2005)CrossRefGoogle Scholar
  18. 18.
    MedPix, Free Online Medical Image Database.
  19. 19.
    Radau, P., Lu, Y., Connelly, K., Paul, G., Dick, A., Wright, G.: Evaluation framework for algorithms segmenting short axis cardiac MRI. Midas J. 1–9 (2009).
  20. 20.
    Lui, D., Scharfenberger, C., Fergani, K., Wong, A., Clausi, D.A.: Enhanced decoupled active contour using structural and textural variation energy functionals. IEEE Trans. Image Process. 23(2), 855–869 (2014)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Chunhong Cao
    • 1
    Email author
  • Chengyao Zhou
    • 1
  • Jie Yu
    • 1
  • Kai Hu
    • 1
  • Fen Xiao
    • 1
  1. 1.Key Laboratory of Intelligent Computing and Information Processing of Ministry of EducationXiangtan UniversityXiangtanChina

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