Local difference-based active contour model for medical image segmentation and bias correction

Local difference-based active contour model for medical image segmentation and bias correction

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Image Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

This study proposes a local bias field and difference estimation (LBDE) model for medical image segmentation and bias field correction. Firstly, the LBDE model uses a linear combination of a given set of smooth orthogonal basis functions, which is called Chebyshev polynomial, to estimate the bias field. Then, a clustering criterion function is defined by considering the difference between the measured image and approximated image in a small region. By applying this difference in the local region, the LBDE model can obtain accurate segmentation results and estimation of the bias field. Finally, the energy functional is incorporated into a level set formulation with a regularisation term, and it is minimised via the level set evolution process. The LBDE model first appears as a two-phase model and then extends to the multi-phase one. Extensive experiments on medical images demonstrate that the LBDE model achieves more precise segmentation results in terms of Jaccard similarity and dice similarity coefficient than the comparative models. Therefore the proposed model can increase the segmentation accuracy and robustness to noise.


    1. 1)
      • 1. Kong, L., Zhang, H., Zheng, Y., et al: ‘Image segmentation using a hierarchical student's-t mixture model’, IET Image Process., 2017, 11, (11), pp. 10941102.
    2. 2)
      • 2. Caselles, V., Kimmel, R., Sapiro, G.: ‘Geodesic active contours’, Int. J. Comput. Vis., 1997, 22, (1), pp. 6179.
    3. 3)
      • 3. Paragios, N., Deriche, R.: ‘Geodesic active contours and level sets for the detection and tracking of moving objects’, IEEE Trans. Pattern Anal. Mach. Intell., 2000, 22, (3), pp. 266280.
    4. 4)
      • 4. Goldenberg, R., Kimmel, R., Rivlin, E., et al: ‘Fast geodesic active contours’, IEEE Trans. Image Process., 2001, 10, (10), pp. 14671475.
    5. 5)
      • 5. Paragios, N., Deriche, R.: ‘Geodesic active regions and level set methods for supervised texture segmentation’, Int. J. Comput. Vis., 2002, 46, (3), pp. 223247.
    6. 6)
      • 6. Li, C., Liu, J., Fox, M.D.: ‘Segmentation of external force field for automatic initialization and splitting of snakes’, Pattern Recognit., 2005, 38, (11), pp. 19471960.
    7. 7)
      • 7. Li, C., Xu, C., Gui, C., et al: ‘Level set evolution without re-initialization: a new variational formulation’. Proc. 2005 IEEE Conf. Comput. Vision. Pattern Recognition, San Diego, USA, June 2005, pp. 430436.
    8. 8)
      • 8. Mumford, D., Shah, J.: ‘Optimal approximations by piecewise smooth functions and associated variational problems’, Commun. Pure Appl. Math., 1989, 42, (5), pp. 577685.
    9. 9)
      • 9. Chan, T.F., Vese, L.A.: ‘Active contours without edges’, IEEE Trans. Image Process., 2001, 10, (2), pp. 266277.
    10. 10)
      • 10. Vese, L.A., Chan, T.F.: ‘A multiphase level set framework for image segmentation using the Mumford and Shah model’, Int. J. Comput. Vis., 2002, 50, (3), pp. 271293.
    11. 11)
      • 11. Li, C., Kao, C.Y., Gore, J.C., et al: ‘Implicit active contours driven by local binary fitting energy’. 2007 IEEE Conf. on Computer Vision and Pattern Recognition, Minneapolis, MN, USA, June 2007, pp. 17.
    12. 12)
      • 12. Wang, L., Li, C., Sun, Q., et al: ‘Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation’, Comput. Med. Imaging Graph., 2009, 33, (7), pp. 520531.
    13. 13)
      • 13. Zhang, K., Song, H., Zhang, L.: ‘Active contours driven by local image fitting energy’, Pattern Recognit., 2010, 43, (4), pp. 11991206.
    14. 14)
      • 14. Wang, X.F., Huang, D.S., Xu, H.: ‘An efficient local Chan–Vese model for image segmentation’, Pattern Recognit., 2010, 43, (3), pp. 603618.
    15. 15)
      • 15. Huang, C., Zeng, L.: ‘An active contour model for the segmentation of images with intensity inhomogeneities and bias field estimation’, PLOS ONE, 2015, 10, (4), p. e0120399.
    16. 16)
      • 16. Styner, M., Brechbühler, C., Szckely, G., et al: ‘Parametric estimate of intensity inhomogeneities applied to MRI’, IEEE Trans. Med. Imaging, 2000, 19, (3), pp. 153165.
    17. 17)
      • 17. Lewis, E.B., Fox, N.C.: ‘Correction of differential intensity inhomogeneity in longitudinal MR images’, Neuroimage, 2004, 23, (1), pp. 7583.
    18. 18)
      • 18. Xu, H., Jiang, G., Yu, M., et al: ‘A local Gaussian distribution fitting energy-based active contour model for image segmentation’, Comput. Electr. Eng., 2018, 70, pp. 317333.
    19. 19)
      • 19. Chen, Y., Zhang, J., Yang, J.: ‘An anisotropic images segmentation and bias correction method’, Magn. Reson. Imaging, 2012, 30, (1), pp. 8595.
    20. 20)
      • 20. Zhan, T., Zhang, J., Xiao, L., et al: ‘An improved variational level set method for MR image segmentation and bias field correction’, Magn. Reson. Imaging, 2013, 31, (3), pp. 439447.
    21. 21)
      • 21. Wu, P., Liu, Y., Li, Y., et al: ‘Robust prostate segmentation using intrinsic properties of TRUS images’, IEEE Trans. Med. Imaging, 2015, 34, (6), pp. 13211335.
    22. 22)
      • 22. Zhou, S., Wang, J., Zhang, M., et al: ‘Correntropy-based level set method for medical image segmentation and bias correction’, Neurocomputing, 2017, 234, (Supplement C), pp. 216229.
    23. 23)
      • 23. Feng, C., Zhao, D., Huang, M.: ‘Image segmentation and bias correction using local inhomogeneous intensity clustering (LINC): a region-based level set method’, Neurocomputing, 2017, 219, (Supplement C), pp. 107129.
    24. 24)
      • 24. Li, C., Huang, R., Ding, Z., et al: ‘A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI’, IEEE Trans. Image Process., 2011, 20, (7), pp. 20072016.
    25. 25)
      • 25. Li, C., Gore, J.C., Davatzikos, C.: ‘Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation’, Magn. Reson. Imaging, 2014, 32, (7), pp. 913923.
    26. 26)
      • 26. Zhang, K., Zhang, L., Lam, K.M., et al: ‘A level set approach to image segmentation With intensity inhomogeneity’, IEEE Trans. Cybern., 2016, 46, (2), pp. 546557.
    27. 27)
      • 27. Li, C., Kao, C.-Y., Gore, J.C., et al: ‘Minimization of region-scalable fitting energy for image segmentation’, IEEE Trans. Image Process., 2008, 17, (10), pp. 19401949.
    28. 28)
      • 28. Li, C., Xu, C., Gui, C., et al: ‘Distance regularized level set evolution and its application to image segmentation’, IEEE Trans. Image Process., 2010, 19, (12), pp. 32433254.
    29. 29)
      • 29. Cremers, D., Rousson, M., Deriche, R.: ‘A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape’, Int. J. Comput. Vis., 2007, 72, (2), pp. 195215.
    30. 30)
      • 30. Zheng, Q., Lu, Z., Yang, W., et al: ‘A robust medical image segmentation method using KL distance and local neighborhood information’, Comput. Biol. Med., 2013, 43, (5), pp. 459470.
    31. 31)
      • 31. Zhang, Y., Guo, H., Chen, F., et al: ‘Weighted kernel mapping model with spring simulation based watershed transformation for level set image segmentation’, Neurocomputing, 2017, 249, pp. 118.
    32. 32)
      • 32. ‘BrainWeb: Simulated Brain Database’. Available at

Related content

This is a required field
Please enter a valid email address