Skip to main content
Log in

Moment-based alignment for shape prior with variational B-spline level set

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

This paper presents a new shape prior-based implicit active contour model for image segmentation. The paper proposes an energy functional including a data term and a shape prior term. The data term, inspired from the region-based active contour approach, evolves the contour based on the region information of the image to segment. The shape prior term, defined as the distance between the evolving shape and a reference shape, constraints the evolution of the contour with respect to the reference shape. Especially, in this paper, we present shapes via geometric moments, and utilize the shape normalization procedure, which takes into account the affine transformation, to align the evolving shape with the reference one. By this way, we could directly calculate the shape transformation, instead of solving a set of coupled partial differential equations as in the gradient descent approach. In addition, we represent the level-set function in the proposed energy functional as a linear combination of continuous basic functions expressed on a B-spline basic. This allows a fast convergence to the segmentation solution. Experiment results on synthetic, real, and medical images show that the proposed model is able to extract object boundaries even in the presence of clutter and occlusion.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. He, L., Peng, Z., Everding, B., Wang, X., Han, C., Weiss, K., Wee, W.G.: A comparative study of deformable contour methods on medical image segmentation. Image Vis. Comput. 26(2), 141–163 (2008)

    Article  Google Scholar 

  2. Shyu, K.K., Pham, V.T., Tran, T.T., Lee, P.L.: Unsupervised active contours driven by density distance and local fitting energy with applications to medical image segmentation. Mach. Vis. Appl. 23(6), 1159–1175 (2012)

    Article  Google Scholar 

  3. Truc, P.T.H., Kim, T., Lee, S., Lee, Y.: A study on the feasibility of active contours on CT bone segmentation. J. Digit Imaging 23(6), 793–805 (2010)

    Article  Google Scholar 

  4. Wei, W., Xin, Y.: Feature extraction for man-made objects segmentation in aerial images. Mach. Vis. Appl. 19(1), 57–64 (2008)

    Article  Google Scholar 

  5. 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. 72(5), 195–215 (2007)

    Article  Google Scholar 

  6. Osher, S., Fedkiw, R.: Level Set Methods and Dynamic Implicit Surfaces. Springer, New York (2002)

    Google Scholar 

  7. Duan, G., Chen, Y. W., Sukegawa, T.: Automatic optical flank wear measurement of microdrills using level set for cutting plane segmentation. Mach. Vis. Appl. 21(5) (2009)

  8. Ronfard, R.: Region-based strategies for active contour models. Int. J. Comput. Vis. 13(2), 229–251 (1994)

    Article  Google Scholar 

  9. Chan, T., Vese, L.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)

    Article  MATH  Google Scholar 

  10. Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1988)

    Article  Google Scholar 

  11. Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. Int. J. Comput. Vis. 22(1), 61–79 (1997)

    Article  MATH  Google Scholar 

  12. Mumford, D., Shah, J.: Optimal approximations by piecewise smooth functions and associated variational problems. Commun. Pure Appl. Math. 42(5), 577–685 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  13. Vese, L., Chan, T.: A multiphase level set framework for image segmentation using the Mumford and Shah model. Int. J. Comput. Vis. 50(3), 271–293 (2002)

    Article  MATH  Google Scholar 

  14. Yezzi, A., Tsai, A., Willsky, A.: A fully global approach to image segmentation via coupled curve evolution equations. J. Vis. Commun. Image Represent. 13(1), 195–216 (2002)

    Article  Google Scholar 

  15. Michailovich, O., Rathi, Y., Tannenbaum, A.: Image segmentation using active contours driven by the Bhattacharyya gradient flow. IEEE Trans. Image Process. 15(11), 2787–2810 (2007)

    Article  MathSciNet  Google Scholar 

  16. Rousson, M., Deriche, R.: A variational framework for active and adaptive segmentation of vector valued images. In: Proceedings of IEEE Workshop on Motion and Video Computing (2002)

  17. Bernard, O., Friboulet, D., Thevenaz, P., Unser, M.: Variational B-spline level-set: a linear filtering approach for fast deformable model evolution. IEEE Trans. Image Process. 18(6), 1179–1191 (2009)

    Article  MathSciNet  Google Scholar 

  18. Li, C., Kao, C., Gui, C., Fox, M. D.: Level set evolution without re-inittialization. In: Processding of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 430–436 (2005)

  19. Song, B., Chan, T.: A Fast Algorithm for Level Set Based Optimization, pp. 02–68. UCLA CAM, Report (2002)

  20. Shyu, K.K., Pham, V.T., Tran, T.T., Lee, P.L.: Global and local fuzzy energy based-active contours for image segmentation. Nonlinear Dyn. 67(2), 1559–1578 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  21. Morse, M., Liu, W., Yoo, T., Subramanian, K.: Active contours using a constraint-based implicit representation. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), New York (2005)

  22. Gelas, A., Bernard, O., Friboulet, D., Prost, R.: Compactly supported radial basic functions based collocation method for level set evolution in image segmentation. IEEE Trans. Image Process. 16(7), 1873–1887 (2007)

    Article  MathSciNet  Google Scholar 

  23. Chan, T., Zhu, W.: Level set based shape prior segmentation. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), San Diego, CA, USA, pp. 1164–1170 (2005)

  24. Riklin-Raviv, T., Kiryati, N., Sochen, N.: Prior-based segmentation and shape registration in the presence of projective distortion. Int. J. Comput. Vis. 72(3), 309–328 (2007)

    Article  Google Scholar 

  25. Cremers, D., Kohlberger, T., Schnorr, C.: Shape statistics in kernel space for variational image segmentation. Pattern Recogn. 36(9), 1929–1943 (2003)

    Article  MATH  Google Scholar 

  26. Chen, Y., Tagare, H.D., Thiruvenkadam, S., Huang, F., Wilson, D., Gopinath, K.S., Briggs, R.W., Geiser, E.A.: Using prior shapes in geometric active contours in a variational framework. Int. J. Comput. Vis. 50(3), 315–328 (2002)

    Google Scholar 

  27. Cremers, D., Osher, S.J., Schnorr, C.: Kernel density estimation and intrinsic alignment for shape priors in level set segmentation. Int. J. Comput. Vis. 69(3), 335–351 (2006)

    Article  Google Scholar 

  28. Rousson, M., Paragios, N.: Shape priors for level set representations. In: Proceedings of European Conference in Computer Vision (ECCV), Copenhagen, Denmark, pp. 78–92 (2002)

  29. Munim, H.E., Farag, A.A.: Curve/surface representation and evolution using vector level set with application to the shape-based segmentation problem. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 945–958 (2007)

    Article  Google Scholar 

  30. Paragios, N., Rousson, M., Ramesh, V.: Matching distance functions a shape-toarea variational approach for global-to-local registration. In: Proceedings of European Conference in Computer Vision (ECCV), Copenhagen, Denmark, pp. 775–789 (2002)

  31. Leventon, M., Grimson, E., Faugeras, O.: Statistical shape influence in geodesic active contours. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), Hilton Head Island, SC, USA, pp. 316–323 (2000)

  32. Tsai, A., Yezzi, A., Wells, W., Temany, C., Tucker, D., Fan, A., Grimson, W.E., Willsky, A.: A shape-based approach to the segmentation of medical imagery using level sets. IEEE Trans. Med. Imaging 22(2), 137–154 (2003)

    Article  Google Scholar 

  33. Bresson, X., Vandergheynst, P., Thiran, J.P.: A variational model for object segmentation using boundary information and shape prior driven by the Mumford–Shah functional. Int. J. Comput. Vis. 28(2), 145–162 (2006)

    Article  MathSciNet  Google Scholar 

  34. Rousson, M., Paragios, N.: Prior knowledge, level set representation and visual grouping. Int. J. Comput. Vis. 76(3), 231–243 (2008)

    Google Scholar 

  35. Yang, J., Duncan, J.S.: 3D image segmentation of deformable objects with joint shape- intensity prior models using level sets. Med. Image Anal. 8(3), 285–294 (2004)

    Article  Google Scholar 

  36. Leu, J.G.: Shape normalization through compacting. Patten Recognit. Lett. 10(4), 243–250 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  37. Pei, S., Lin, C.: Image normalization for pattern recognition. Image Vis. Comput. 13(10), 711–723 (1995)

    Article  Google Scholar 

  38. Foulonneau, A., Charbonnier, P., Heitz, F.: Affine-invariant geometric shape priors for region-based active contours. IEEE Trans. Pattern Anal. Mach. Intell. 28(8), 1352–1357 (2006)

    Google Scholar 

  39. Unser, M.: Splines: a perfect fit for signal and image processing. IEEE Signal Process. Mag. 16(6), 22–38 (1999)

    Article  Google Scholar 

  40. Kybic, J., Unser, M.: Fast parametric elastic image registration. IEEE Trans. Image Process. 12(11), 1427–1442 (2003)

    Article  Google Scholar 

  41. Hu, M.K.: Visual pattern recognition by moments invariants. IRE Trans. Inf. Theory 8(1), 179–187 (1962)

    MATH  Google Scholar 

  42. Wang, X. H., Zhao, R. C.: A new method for image normalization. In: Proceedings of international symposium on intelligent multimedia, video, and speech processing, Hong Kong, pp. 356–359 (2001)

  43. Teague, M.R.: Image analysis via the general theory of moments. J. Opt. Soc. Am. 70(8), 920–930 (1980)

    Article  MathSciNet  Google Scholar 

  44. Pei, S., Lin, C.: Normalization of rotationally symmetric shapes for pattern recognition. Pattern Recogn. 25(9), 913–920 (1992)

    Article  Google Scholar 

  45. Shen, D., Ip, H.H.S.: Generalized affine invariant image normalization. IEEE Trans. Pattern Anal. Mach. Intell. 19(5), 431–440 (1997)

    Article  Google Scholar 

  46. Heikkila, J.: Pattern matching with affine moment descriptors. Pattern Recogn. 37(9), 1825–1834 (2004)

    Article  Google Scholar 

  47. Suk, T., Flusser, J.: Affine normalization of symmetric objects. In: Proceedings of the 7th International Conference on Advanced Concepts for Intelligent Vision Systems, pp. 100–107 (2005)

  48. Hosny, K.M.: On the computational aspects of affine moment invariants for gray-scale images. Appl. Math. Comput. 195(2), 762–771 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  49. Vu, N., Manjunath, B.S.: Shape prior segmentation of multiple objects with graph cuts. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), Anchorage, AK (2008)

  50. Liu, W., Shang, Y., Yang, X., Deklerck, R., Cornelis, J.: A shape prior constraint for implicit active contours. Patten Recognit. Lett. 32(15), 1937–1947 (2011)

    Article  Google Scholar 

  51. Andreopoulos, A., Tsotsos, J.K.: Efficient and generalizable statistical models of shape and appearance for analysis of cardiac MRI. Med. Image Anal. 12(3), 335–357 (2008)

    Article  Google Scholar 

  52. Tohka, J.: Surface extraction from volumetric images using deformable meshes: a comparative study. In: Proceedings of the Seventh European Conference in Computer Vision (ECCV), Copenhagen, Denmark, pp. 350–364 (2002)

Download references

Acknowledgments

The authors would like to thank the National Science Council of Taiwan for supporting this research under the Grant No.NSC-101-2221-E-008-001. They would like to thank Dr. Weiping Liu in Shanghai Jiao Tong University, for providing the data set and test images of terracotta warrior used in Section 5. They would also like to thank the reviewers and the Associate Editor for their valuable comments and suggestions, which have greatly helped in improving the content of this paper.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kuo-Kai Shyu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Tran, TT., Pham, VT. & Shyu, KK. Moment-based alignment for shape prior with variational B-spline level set. Machine Vision and Applications 24, 1075–1091 (2013). https://doi.org/10.1007/s00138-013-0504-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-013-0504-2

Keywords

Navigation