KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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A Level Set Method to Image Segmentation Based on Local Direction Gradient

  • Peng, Yanjun (College of Computer Science and Engineering, Shandong University of Science and Technology) ;
  • Ma, Yingran (College of Computer Science and Engineering, Shandong University of Science and Technology)
  • Received : 2017.07.27
  • Accepted : 2017.12.05
  • Published : 2018.04.30
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Abstract

For image segmentation with intensity inhomogeneity, many region-based level set methods have been proposed. Some of them however can't get the relatively ideal segmentation results under the severe intensity inhomogeneity and weak edges, and without use of the image gradient information. To improve that, we propose a new level set method combined with local direction gradient in this paper. Firstly, based on two assumptions on intensity inhomogeneity to images, the relationships between segmentation objects and image gradients to local minimum and maximum around a pixel are presented, from which a new pixel classification method based on weight of Euclidian distance is introduced. Secondly, to implement the model, variational level set method combined with image spatial neighborhood information is used, which enhances the anti-noise capacity of the proposed gradient information based model. Thirdly, a new diffusion process with an edge indicator function is incorporated into the level set function to classify the pixels in homogeneous regions of the same segmentation object, and also to make the proposed method more insensitive to initial contours and stable numerical implementation. To verify our proposed method, different testing images including synthetic images, magnetic resonance imaging (MRI) and real-world images are introduced. The image segmentation results demonstrate that our method can deal with the relatively severe intensity inhomogeneity and obtain the comparatively ideal segmentation results efficiently.

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References

  1. M. Kass, A. Witkin, and D. Terzopoulos, "Snakes: Active contour models," International Journal of Computer Vision, vol. 1, no. 4, pp. 321-331, January, 1988. https://doi.org/10.1007/BF00133570
  2. V. Caselles, F. Catte, T. Coll, and F. Dibos, "A geometric model for active contours in image processing," Numerical Mathematics, vol. 66, no. 1, pp. 1-31, December, 1993. https://doi.org/10.1007/BF01385685
  3. V. Caselles, R. Kimmel, and G. Sapiro, "Geodesic active contours," International Journal of Computer Vision, vol. 22, no. 1, pp. 61-79, February, 1997. https://doi.org/10.1023/A:1007979827043
  4. X. Bresson, S. Esedoglu, P. Vandergheynst, J.-P. Thiran, and S. Osher, "Fast global minimization of the active contours/snake model," Journal of Mathematical Imaging and Vision, vol. 28, no. 2, pp. 151-167, June, 2005. https://doi.org/10.1007/s10851-007-0002-0
  5. X. Bresson, S. Esedoglu, P. Vandergheynst, J.-P. Thiran, and S. Osher, "Global minimizers of the active contour/snake model," in Proc. of UCLA CAM Report, January, 2005.
  6. D. Mumford and J. Shah, "Optimal approximations by piecewise smooth functions and associated variational problems. Commun," Pure Appl. Math, vol. 42, no. 5, pp. 577-685, July, 1989. https://doi.org/10.1002/cpa.3160420503
  7. T. F. Chan and L. A. Vese, "Active contours without edges," IEEE Transactions on Image Process, vol. 10, no. 2, pp. 266-277, February, 2001. https://doi.org/10.1109/83.902291
  8. L. A. Vese and T. F. Chan, "A multiphase level set framework for image segmentation using the Mumford and Shah model," International Journal of Computer Vision, vol. 50, no. 3, pp. 271-293, December, 2002. https://doi.org/10.1023/A:1020874308076
  9. D. Cremers, M. Rousson, and R. Deriche, "A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape," International Journal of Computer Vision, vol. 72, no. 2, pp. 195-215, 2007. https://doi.org/10.1007/s11263-006-8711-1
  10. C. Li, C.-Y. Kao, J. C. Gore, and Z. Ding, "Implicit active contours driven by local binary fitting energy," in Proc. of IEEE Conf. on Computer Vision & Pattern Recognition, pp. 1-7, June 17-22, 2007.
  11. C. Li, R. Huang, Z. Ding, J. C. Gatenby, D. N. Metaxas, and J. C. Gore, "A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI," IEEE Transactions on Image Process, vol. 20, no. 7, pp. 2007-2016, April, 2011. https://doi.org/10.1109/TIP.2011.2146190
  12. C. Li, J. C. Gore, and C. Davatzikos, "Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation," Magnetic Resonance Imaging, vol. 32, no. 7, pp. 913-923, September, 2014. https://doi.org/10.1016/j.mri.2014.03.010
  13. S. Lankton and A. Tannenbaum, "Localizing region-based active contours," IEEE Transactions on Image Process, vol. 17, no. 11, pp. 2029-2039, September, 2008. https://doi.org/10.1109/TIP.2008.2004611
  14. T. Brox and D. Cremers, "On local region models and a statistical interpretation of the piecewise smooth Mumford-Shah functional," International Journal of Computer Vision, vol. 84, no. 2, pp. 184-193, August, 2009. https://doi.org/10.1007/s11263-008-0153-5
  15. K. Zhang, L. Zhang, K.-M. Lam, D. Zhang, "A level set approach to image segmentation with intensity inhomogeneity," IEEE Transactions on Cybernetics, vol.46, no. 2, pp. 546-557, March, 2016. https://doi.org/10.1109/TCYB.2015.2409119
  16. D. Chopp, "Computing minimal surface via level set curvature flow," Journal of Computational Physics, vol. 106, no. 1, pp. 77-91, May, 1993. https://doi.org/10.1006/jcph.1993.1092
  17. M. Sussman, P. Smereka, and S. Osher, "A level set approach for computing solutions to incompressible two-phase flow," Journal of Computational Physics, vol. 114, no. 1, pp. 146-159, September, 1994. https://doi.org/10.1006/jcph.1994.1155
  18. D. Peng, B. Merriman, S. Osher. H. Zhao, and M. Kang, "A PDE-based fast local level set method," Journal of Computational Physics, vol. 155, no. 2, pp. 410-438, November, 1999. https://doi.org/10.1006/jcph.1999.6345
  19. C. Li, C. Xu, C. Gui, and M. D. Fox, "Level set evolution without re-initialization: A new variational formulation," in Proc. of IEEE Conf. on Computer Vision & Pattern Recognition, pp. 430-436, June 20-25, 2005.
  20. X. Xie, "Active contouring based on gradient vector interaction and constrained level set diffusion," IEEE Transactions on Image Process, vol. 19, no. 1, pp. 154-164, September, 2010. https://doi.org/10.1109/TIP.2009.2032891
  21. C. Li, C. Xu, C. Gui, and M. D. Fox, "Distance regularized level set evolution and its application to image segmentation," IEEE Transactions on Image Process, vol. 19, no. 12, pp. 3243-3254, August, 2010. https://doi.org/10.1109/TIP.2010.2069690
  22. K. Zhang, L. Zhang, H. Song, and D. Zhang, "Reinitialization-free level set evolution via reaction diffusion," IEEE Transactions on Image Process, vol. 22, no. 1, pp. 258-271, January, 2013. https://doi.org/10.1109/TIP.2012.2214046
  23. Y. Duan, H. Chang, W. Huang, J. Zhou, Z. Lu, and C. Wu, "The L0 regularized Mumford-Shah model for bias correction and segmentation of medical images," IEEE Transactions on Image Process, vol. 24, no. 11, pp. 3927-3938, November, 2015. https://doi.org/10.1109/TIP.2015.2451957
  24. H. Chang, W. Huang, C. Wu, S. Huang, C. Guan, S. Sekar, K. K. Bhakoo, and Y. Duan, "A new variational method for bias correction and its applications to rodent brain extraction," IEEE Transactions on Medical Imaging, vol. 36, no. 3, pp. 721-733, March, 2017. https://doi.org/10.1109/TMI.2016.2636026
  25. M. N. Ahmed, S. M. Yamany, N. Mohamed, A. A. Farag, and T. Moriarty, "A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data," IEEE Transactions on Medical Imaging, vol. 21, no. 3, pp. 193-199, March, 2002. https://doi.org/10.1109/42.996338
  26. S. C. Chen, and D. Q. Zhang, "Robust image segmentation using FCM with spatial constrains based on new kernel-induced distance measure," IEEE Transactions on Systems Man, Cybernetics Society, vol. 34, no. 4, pp. 1907-1916, August, 2004. https://doi.org/10.1109/TSMCB.2004.831165
  27. K. S. Chuang, H.-L. Tzeng, S. Chen, J. Wu, and T.-J. Chen, "Fuzzy C-means clustering with spatial information for image segmentation," Computerized Medical Imaging and Graphics, vol. 30, no. 1, pp. 9-15, January, 2006. https://doi.org/10.1016/j.compmedimag.2005.10.001
  28. D. W. Shattuck, S. R. Sandor-Leahy, K. A. Schaper, D. A. Rottenberg, and R. M. Leahy, "Magnetic resonance image tissue classification using a partial volume model," Neuroimage, vol. 13, no. 5, pp. 856-876, May, 2001. https://doi.org/10.1006/nimg.2000.0730
  29. B. Likar, M. A. Viergever, and F. Pernus, "Retrospective correction of MR intensity inhomogeneity by information minimization," IEEE Transactions on Medical Imaging, vol. 20, no. 12, pp. 1398-1410, December, 2001. https://doi.org/10.1109/42.974934