A Hybride Active Contour Model Driven by Global and Local Image Information

  • Xu LiEmail author
  • Hairong Liu
  • Yu Xing


In this paper, a novel active contour model based on hybrid image fitting energy which utilizes both global and local image information is proposed. Two fitting images are constructed to approximate the original image and the square of the original image. Both global and local image information are incorporated into these two fitting images. Based on these two fitting images, a hybrid image fitting energy, which is then minimized in a variational level set framework to guide the evolving contours to the desired boundaries. The proposed approach is validated by experiments on both synthetic and real images. The experiments demonstrate that the proposed model is more efficient and robust for segmenting different kinds of images compared with several typical active contour models.


Image segmentation Intensity inhomogeneity Active contour Level set Hybrid model 



The author (Hairong Liu) was supported by Natural Science Foundation of Jiangsu Province (BK20140965) and Natural Science Foundation of the Jiangsu Higher Education Institutions of China (14KJB110010). The author (Yu Xing) was supported by the Natural Science Foundation for Youths of Jiangsu Province (BK20171072), the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (17KJB110007) and the Open Project of Jiangsu Key Laboratory of Financial Engineering (NSK2015-15).


  1. 1.
    Kass M, Witkin A, Terzopoulos D (1991) Snake: active contours model. Int J Comput Vis 1(4):1167–1186Google Scholar
  2. 2.
    Caselles V, Kimmel R, Sapiro G (1998) Geodesic active contours. Int J Comput Vis 22(1):61–79zbMATHGoogle Scholar
  3. 3.
    Xu C, Prince JL (1998) Snakes, shapes, and gradient vector flow. IEEE Trans Image Process 7(3):359–369MathSciNetzbMATHGoogle Scholar
  4. 4.
    Kichenassamy S, Kumar A, Olver P, et al (1995) Gradient flows and geometric active contour models. In: Proceedings of international conference on computer vision, pp 810–815Google Scholar
  5. 5.
    Chan T, Vese L (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277zbMATHGoogle Scholar
  6. 6.
    Mumford D, Shah J (1989) Optimal approximations of piecewise smooth functions and associated variational problems. Commun Pure Appl Math 42:577–685MathSciNetzbMATHGoogle Scholar
  7. 7.
    Osher S, Sethian JA (1998) Fronts propagating with curvature-dependent speed: algorithms based on Hamilton–Jacobi formulations. J Comput Phys 79(1):12–49MathSciNetzbMATHGoogle Scholar
  8. 8.
    Li C, Kao C, Gore J, Ding Z (2008) Minimization of region-scalable fitting energy for image segmentation. IEEE Trans Image Process 17:1940–1949MathSciNetzbMATHGoogle Scholar
  9. 9.
    Lankton S, Tannenbaum A (2008) Localizing region based active contour. IEEE Trans Image Process 17(11):2029–2039MathSciNetzbMATHGoogle Scholar
  10. 10.
    Zhang K, Song H, Zhang L (2010) Active contours driven by local image fitting energy. Pattern Recognit 43(4):1199–1206zbMATHGoogle Scholar
  11. 11.
    Li C, Kao C, Gore J, Ding Z (2007) Implicit active contours driven by local binary fitting energy. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR). IEEE Computer Society, Washington, DC, pp 1–7Google Scholar
  12. 12.
    Wang L, Chang Y, Wang H et al (2017) An active contour model based on local fitted images for image segmentation. Inf Sci 418:61–73Google Scholar
  13. 13.
    Li Y, Cao Q, Yu Q et al (2018) Fast and robust active contours model for image segmentation. Neural Process Lett 1:1–22Google Scholar
  14. 14.
    Wang L, Li C, Sun Q et al (2009) Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation. Comput Med Imaging Graph 33(7):520–531Google Scholar
  15. 15.
    Xie X, Wang C et al (2013) Active contours model exploiting hybrid image information: an improved formulation and level set method. J Comput Inf Syst 9(20):8371–8379Google Scholar
  16. 16.
    Ge Q, Li C, Shao W et al (2015) A hybrid active contour model with structured feature for image segmentation. Signal Process 108:147–158Google Scholar
  17. 17.
    Dai L, Ding J, Yang J (2015) Inhomogeneity-embedded active contour for natural image segmentation. Pattern Recognit 48(8):2513–2529Google Scholar
  18. 18.
    Kim W, Kim C (2013) Active contours driven by the salient edge energy model. IEEE Trans Image Process 22(4):1667–1673MathSciNetzbMATHGoogle Scholar
  19. 19.
    Piovano J, Rousson M, Papadopoulo T (2007) Efficient segmentation of piecewise smooth images. In: SSVM07, Ischia, pp 709–720Google Scholar
  20. 20.
    Zhang Z, Xu Y, Shao L et al (2017) Discriminative block-diagonal representation learning for image recognition. IEEE Trans Neural Netw Learn Syst 29(7):3111–3125MathSciNetGoogle Scholar
  21. 21.
    Li X, Li C, Fedorov A et al (2016) Segmentation of prostate from ultrasound images using level sets on active band and intensity variation across edges. Med Phys 43:3090–3103Google Scholar
  22. 22.
    Zhang Z, Liu L, Shen F, Shen HT, Shao L (2018) Binary multi-view clustering. IEEE Trans Pattern Anal Mach Intell 1:83Google Scholar
  23. 23.
    Zhang Z, Shao L, Xu Y, Liu L, Yang J (2017) Marginal representation learning with graph structure self-adaptation. IEEE Trans Neural Netw Learn Syst 23:1Google Scholar
  24. 24.
    Ma Q, Peng J, Kong D (2017) Image segmentation via mean curvature regularized Mumford–Shah model and thresholding. Neural Process Lett 3:1–15Google Scholar
  25. 25.
    Gao J, Dai X, Zhu C et al (2017) Supervoxel segmentation and bias correction of MR image with intensity inhomogeneity. Neural Process Lett 8:1–14Google Scholar
  26. 26.
    Zhang Z, Li F, Zhao M et al (2017) Robust neighborhood preserving projection by nuclear/L2,1-norm regularization for image feature extraction. IEEE Trans Image Process 26(4):1607–1622MathSciNetGoogle Scholar
  27. 27.
    Ye Q, Fu L, Zhang Z et al (2018) Lp-and Ls-norm distance based robust linear discriminant analysis. Neural Netw 105:393–404Google Scholar
  28. 28.
    Zhang Y, Zhang Z, Qin J et al (2018) Semi-supervised local multi-manifold Isomap by linear embedding for feature extraction. Pattern Recognit 76:662–678Google Scholar
  29. 29.
    Kullback S, Leibler RA (1951) On information and sufficiency. Ann Math Stat 22(1):79–86MathSciNetzbMATHGoogle Scholar
  30. 30.
    Liu T, Sun J, Zheng N et al (2007) Learning to detect a salient object. In: Proceedings of IEEE international conference on computer vision pattern recognition, pp 1–8Google Scholar
  31. 31.
    Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26(3):297–302Google Scholar
  32. 32.
    Cheng MM, et al (2011) Global contrast based salient region detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 409–416Google Scholar
  33. 33.
    Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: NIPS, pp 1097–1105Google Scholar
  34. 34.
    Szegedy C, Liu W, Jia Y, Sermanet P, Reed C, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: IEEE conference on computer vision and pattern recognition, Boston, pp 1–9Google Scholar
  35. 35.
    Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440Google Scholar
  36. 36.
    Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, Cham, pp 234–241Google Scholar
  37. 37.
    He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778Google Scholar

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

Authors and Affiliations

  1. 1.School of Mathematical SciencesZhejiang UniversityHangzhouChina
  2. 2.School of ScienceNanjing Forestry UniversityNanjingChina
  3. 3.School of FinanceNanjing Audit UniversityNanjingChina

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