Advertisement

Pattern Analysis and Applications

, Volume 21, Issue 3, pp 613–627 | Cite as

A novel active contour model driven by J-divergence entropy for SAR river image segmentation

  • Bin Han
  • Yiquan Wu
Survey
  • 106 Downloads

Abstract

It is of great difficulty to utilize the existing active contour models (ACMs) to achieve accurate segmentation of synthetic aperture radar (SAR) river images. To address this problem, a novel ACM driven by J-divergence entropy is proposed. The external energy constraint term of the proposed model is defined by the J-divergence entropy, which differs from those of many existing ACMs defined by the Euclidean distance. Moreover, the median absolute deviations of pixel grayscale values inside and outside the curve are utilized as energy weights, which can adaptively adjust proportions of region energies inside and outside the curve, leading to the improvement in segmentation efficiency. Experiments are performed on a large number of SAR river images, and the results demonstrate that, compared with the existing ACMs, the proposed model shows clear advantages in terms of both segmentation performance and segmentation efficiency.

Keywords

Image segmentation SAR river image Active contour model J-divergence entropy Median absolute deviation 

Notes

Acknowledgements

This work is partially supported by the National Natural Science Fund of China under Grant 61573183, Key Laboratory of Yellow River Sediment of Ministry of Water Resources under Grant 2014006, Engineering Technology Research Center of Wuhan Intelligent Basin under Grant CKWV2013225/KY, State Key Laboratory of Urban Water Resources and Environment under Grant LYPK201304.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Xia GS, Liu G, Yang W, Zhang L (2016) Meaningful object segmentation from SAR images via a multiscale nonlocal active contour model. IEEE Trans Geosci Remote Sens 54(3):1860–1873CrossRefGoogle Scholar
  2. 2.
    Sukcharoenpong A, Yilmaz A, Li R (2016) An integrated active contour approach to shoreline mapping using HSI and DEM. IEEE Trans Geosci Remote Sens 54(3):1586–1597CrossRefGoogle Scholar
  3. 3.
    Figorito B, Tarantino E (2014) Semi-automatic detection of linear archaeological traces from orthorectified aerial images. Int J Appl Earth Obs Geoinf 26(1):458–463CrossRefGoogle Scholar
  4. 4.
    Ahmadi S, Zoej MJV, Ebadi H, Moghaddam HA, Mohammadzadeh A (2010) Automatic urban building boundary extraction from high resolution aerial images using an innovative model of active contours. Int J Appl Earth Obs Geoinf 12(3):150–157CrossRefGoogle Scholar
  5. 5.
    Li Z, Shi W, Wang Q, Miao Z (2015) Extracting man-made objects from high spatial resolution remote sensing images via fast level set evolutions. IEEE Trans Geosci Remote Sens 54(2):883–899Google Scholar
  6. 6.
    Song Y, Wu YQ, Dai YM (2016) A new active contour remote sensing river image segmentation algorithm inspired from the cross entropy. Digit Signal Process 48:322–332MathSciNetCrossRefGoogle Scholar
  7. 7.
    Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277CrossRefzbMATHGoogle Scholar
  8. 8.
    Caselles V, Kimmel R, Sapiro G (1997) Geodesic active contours. Int J Comput Vis 22(1):61–79CrossRefzbMATHGoogle Scholar
  9. 9.
    Caselles V, Kimmel R, Sapiro G (1995) Geodesic active contours. In: Proceedings of fifth international conference on computer vision, pp 694–699Google Scholar
  10. 10.
    Li Q, Deng TQ, Xie W (2016) Active contours driven by divergence of gradient vector flow. Signal Process 120:185–199CrossRefGoogle Scholar
  11. 11.
    Yu C, Zhang W, Yu Y et al (2013) A novel active contour model for image segmentation using distance regularization term. Comput Math Appl 65(11):1746–1759MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    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):271–293CrossRefzbMATHGoogle Scholar
  13. 13.
    Ma Z, Jorge RNM, Tavares JMRS (2010) A shape guided C–V model to segment the levator ani muscle in axial magnetic resonance images. Med Eng Phys 32(7):766–774CrossRefGoogle Scholar
  14. 14.
    Li BN, Chui CK, Change S et al (2011) Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation. Comput Biol Med 41(1):1–10CrossRefGoogle Scholar
  15. 15.
    Li C, Kao C, Gore J, Ding Z (2007) Implicit active contours driven by local binary fitting energy. In: Proceeding of IEEE conference on computer vision and pattern recognition, pp 1–7Google Scholar
  16. 16.
    Li C, Kao C, Gore JC, Ding Z (2008) Minimization of region-scalable fitting energy for image segmentation. IEEE Trans Image Process 17(10):1940–1949MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Zhang KH, Song HH, Zhang L (2010) Active contours driven by local image fitting energy. Pattern Recognit 43(4):1199–1206CrossRefzbMATHGoogle Scholar
  18. 18.
    He CJ, Wang Y, Chen Q (2012) Active contours driven by weighted region-scalable fitting energy based on local entropy. Signal Process 92(2):587–600CrossRefGoogle Scholar
  19. 19.
    Liu WP, Shang YF, Yang X (2013) Active contour model driven by local histogram fitting energy. Pattern Recognit Lett 34(6):655–662CrossRefGoogle Scholar
  20. 20.
    Li CM, Huang R, Ding ZH et al (2011) A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Trans Image Process 20(7):2007–2016MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Liu SG, Peng YL (2012) A local region-based Chan–Vese model for image segmentation. Pattern Recognit 45(7):2769–2779CrossRefzbMATHGoogle Scholar
  22. 22.
    Dong F, Chen Z, Wang J (2013) A new level set method for inhomogeneous image segmentation. Image Vis Comput 31(10):809–822CrossRefGoogle Scholar
  23. 23.
    Wang L, Li C, Sun Q, Xia D, Kao CY (2009) Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation. Comput Med Imaging Gr 33(7):520–531CrossRefGoogle Scholar
  24. 24.
    Zhou SP, Wang JJ, Zhang S, Gong YH (2016) Active contour model based on local and global intensity information for medical image segmentation. Neurocomputing 186:107–118CrossRefGoogle Scholar
  25. 25.
    Wang H, Huang T, Xu Z, Wang Y (2014) An active contour model and its algorithms with local and global Gaussian distribution fitting energies. Inf Sci 263:43–59CrossRefGoogle Scholar
  26. 26.
    Wang H, Huang T, Xu Z, Wang Y (2016) A two-stage image segmentation via global and local region active contours. Neurocomputing 205:130–140CrossRefGoogle Scholar
  27. 27.
    Mondal A, Ghosh S, Ghosh A (2016) Robust global and local fuzzy energy based active contour for image segmentation. Appl Soft Comput 47:191–215CrossRefGoogle Scholar
  28. 28.
    Jiang XL, Wu XL, Xiong Y, Li B (2015) Active contours driven by local and global intensity fitting energies based on local entropy. Optik 126(24):5672–5677CrossRefGoogle Scholar
  29. 29.
    Zhang K, Zhang L, Song H, Zhou W (2010) Active contours with selective local or global segmentation: a new formulation and level set method. Image Vis Comput 28(4):668–676CrossRefGoogle Scholar
  30. 30.
    Thieu QT, Luong M, Rocchisani JM, Sirakov NM, Viennet E (2015) Efficient segmentation with the convex local-global fuzzy Gaussian distribution active contour for medical applications. Ann Math Artif Intell 75(1–2):249–266MathSciNetCrossRefzbMATHGoogle Scholar
  31. 31.
    Kullback S, Leibler RA (1951) On information and sufficiency. Ann Math Stat 22:79–86MathSciNetCrossRefzbMATHGoogle Scholar
  32. 32.
    Jeffreys H (1946) An invariant form for the prior probability in estimation problems. Proc R Soc Lond 186(1007):453–461MathSciNetCrossRefzbMATHGoogle Scholar
  33. 33.
    Li C, Xu C, Gui C, Fox MD (2010) Distance regularized level set evolution and its application to image segmentation. IEEE Trans Image Process 19(12):3243–3254MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Electronic and Information EngineeringNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.Key Laboratory of Yellow River Sediment of Ministry of Water Resources, Yellow River Institute of Hydraulic ResearchYellow Water Resources CommissionZhengzhouChina
  3. 3.Engineering Technology Research Center of Wuhan Intelligent Basin, Changjiang River Scientific Research InstituteChangjiang Water Resources CommissionWuhanChina
  4. 4.State Key Laboratory of Urban Water Resources and Environment, Harbin Institute of TechnologyHarbinChina

Personalised recommendations