Skip to main content
Log in

Multiphase B-spline level set and incremental shape priors with applications to segmentation and tracking of left ventricle in cardiac MR images

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

Abstract

This paper presents a new multiphase active contour model for object segmentation and tracking. The paper introduces an energy functional which incorporates image feature information to drive contours toward desired boundaries, and shape priors to constrain the evolution of the contours with respect to reference shapes. The shape priors, in the model, are constructed by performing the incremental principal component analysis (iPCA) on a set of training shapes and newly available shapes which are the resulted shapes derived from preceding segmented images. By performing iPCA, the shape priors are updated without repeatedly performing PCA on the entire training set including the existing shapes and the newly available shapes. In addition, by incrementally updating the resulted shape information of consecutive frames, the approach allows to encode shape priors even when the database of training shapes is not available. Moreover, in shape alignment steps, we exploit the shape normalization procedure, which takes into account the affine transformation, to directly calculate pose transformations instead of solving a set of coupled partial differential equations as in gradient descent-based approaches. Besides, we represent the level set functions as linear combinations of continuous basic functions expressed on B-spline basics for a fast convergence to the segmentation solution. The model is applied to simultaneously segment/track both the endocardium and epicardium of left ventricle from cardiac magnetic resonance (MR) images. Experimental results show the desired performances of the proposed model.

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
Fig. 15

Similar content being viewed by others

References

  1. Lynch, M., Ghita, O., Whelan, P.F.: Segmentation of the left ventricle of the heart in 3-D+t MRI data using an optimized nonrigid temporal model. IEEE Trans. Med. Imaging 27(2), 195–203 (2008)

    Article  Google Scholar 

  2. Zhu, Y., Papademetris, X., Sinusas, J.A., Duncan, S.J.: Segmentation of the left ventricle from cardiac MR images using a subject-specific dynamical model. IEEE Trans. Med. Imaging 29(3), 669–687 (2010)

    Article  Google Scholar 

  3. Santarelli, M.F., Positano, V., Michelassi, C., Lombardi, M., Landini, L., Barlaud, M.: Automated cardiac MR image segmentation: theory and measurement evaluation. Med. Eng. Phys. 25(2), 149–159 (2003)

    Article  Google Scholar 

  4. Kaus, R.M., Von Berg, J., Weese, J., Niessen, W., Pekar, V.: Automated segmentation of the left ventricle in cardiac MRI. Med. Image Anal. 8(3), 245–254 (2004)

    Article  Google Scholar 

  5. Duy, N., Karen, M., Jean-Paul, V.: Comparative evaluation of active contour model extensions for automated cardiac MR image segmentation by regional error assessment. Magn. Reson. Mater. Phys. 20(2), 69–82 (2007)

    Article  Google Scholar 

  6. Kurkure, U., Pednekar, A., Muthupillai, R., Flamm, D.S., Kakadiaris, A.L.: Localization and segmentation of left ventricle in cardiac Cine-MR images. IEEE Trans. Biomed. Eng. 56(5), 1360–1370 (2009)

    Article  Google Scholar 

  7. Tsai, I.C., Huang, Y.L., Liu, P.T., Chen, M.C.: Left ventricular myocardium segmentation on delayed phase of multi-detector row computed tomography. Int. J. Comput. Assist. Radiol. Surg. 7(5), 737–751 (2012)

    Article  Google Scholar 

  8. Rezaee, M., van der Zwet, P., Lelieveldt, B., van der Geest, R., Reiber, J.: A multiresolution image segmentation technique based on pyramidal segmentation and fuzzy clustering. IEEE Trans. Image Process. 9(7), 1238–1248 (2000)

    Article  Google Scholar 

  9. Boykov, Y., Lee, V.S., Rusinek, H., Bansal, R.: Segmentation of dynamic N-d data sets via graph cuts using markov models. In: Proceedings of International Conference on Medical Image Computing and Computer Assisted Intervention (mICCAI), pp. 1058–1066 (2001)

  10. Mahapatra, D., Sun, Y.: Orientation histograms as shape priors for left ventricle segmentation using graph cuts. In: Proceedings of International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), pp. 420–427 (2011)

  11. Hautvast, G., Lobregt, S., Breeuwer, M., Gerritsen, F.: Automatic contour propagation in cine cardiac magnetic resonance images. IEEE Trans. Med. Imaging 25(11), 1472–1482 (2006)

    Article  Google Scholar 

  12. Marsousi, M., Eftekhari, A., Kocharian, A., Alirezaie, J.: Endocardial boundary extraction in left ventricular echocardiographic images using fast and adaptive B-spline snake algorithm. Int. J. Comput. Assist. Radiol. Surg. 5(5), 501–513 (2010)

    Article  Google Scholar 

  13. Grosgeorge, D., Petitjean, C., Caudron, J., Fares, J., Nicolas Dacher, J.: Automatic cardiac ventricle segmentation in MR images: a validation study. Int. J. Comput. Assist. Radiol. Surg. 6(5), 573–581 (2011)

    Article  Google Scholar 

  14. 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 

  15. 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 

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

    Article  Google Scholar 

  17. Sethian, J.A.: Level set methods and fast marching methods. Cambridge University Press, Cambridge (1999)

    MATH  Google Scholar 

  18. Caselles, V., Catte, F., Coll, T., Dibos, F.: A geometric model for active contours in image processing. Numer. Math. 66(1), 1–31 (1993)

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  22. 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 

  23. 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 

  24. 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 

  25. 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)

    Article  MATH  Google Scholar 

  26. Paragios, N.: A variational approach for the segmentation of the left ventricle in cardiac image analysis. Int. J. Comput. Vis. 50(3), 345–362 (2002)

    Article  MATH  Google Scholar 

  27. Zhang, S., Zhan, Y., Dewan, M., Huang, J., Metaxas, D.N., Zhou, X.S.: Sparse shape composition: a new framework for shape prior modeling. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), pp. 1025–1032 (2011)

  28. Petitjean, C., Dacher, J.N.: A review of segmentation methods in short axis cardiac MR images. Med. Image Anal. 15(2), 169–184 (2011)

    Article  Google Scholar 

  29. Qin., X., Li, X., Liu, Y., Lu, H., Yan, P.: Adaptive shape prior constrained level sets for bladder MR image segmentation. IEEE J. Biomed. Health Inf. (2013). doi:10.1109/JBHI.2013.2288935

  30. 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 

  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., Wells, W., Tempany, C., Grimson, E., Willsky, A.: Mutual information in coupled multi-shape model for medical image segmentation. Med. Image Anal. 8(4), 429–445 (2004)

    Article  Google Scholar 

  33. Rousson, M., Paragios, N., Deriche, R.: implicit active shape models for 3D segmentation in MRi imaging. In: Proceedings of International Conference on Medical image Computing and Computer Assisted intervention (MiCCAi) (2004)

  34. Dambreville, S., Rathi, Y., Tannenbaum, A.: A framework for image segmentation using shape models and Kernel space shape priors. IEEE Trans. Pattern Anal. Mach. Intell. 30(8), 1385–1399 (2008)

    Article  Google Scholar 

  35. 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)

  36. 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 

  37. 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 

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

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  40. Ross, D., Lim, J., Lin, R.-S., Yang, M.-H.: Incremental learning for robust visual tracking. Int. J. Comput. Vis. 77(1), 125–141 (2008)

    Article  Google Scholar 

  41. 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 

  42. Aubert, G., Barlaud, M., Faugeras, O., Jehan-Besson, S.: Image segmentation using active contours: calculus of variations or shape gradients? SIAM Appl. Math. 63(6), 2128–2154 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  43. 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 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  46. Cootes, T., Taylor, C., Cooper, D., Graham, J.: Active shape models—their training and application. Comput. Vis. Image Underst. 61(1), 38–59 (1995)

    Article  Google Scholar 

  47. Levy, A., Lindenbaum, M.: Sequential Karhunen–Loeve basis extraction and its application to images. IEEE Trans. Image Process. 9(8), 1371–1374 (2000)

    Article  MATH  Google Scholar 

  48. 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)

  49. Tran, T.T., Pham, V.T., Shyu, K.K.: Moment-based alignment for shape prior with variational B-spline level set. Mach. Vis. Appl. 24(5), 1075–1091 (2013)

    Article  Google Scholar 

  50. 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)

  51. Dambreville, S., Rathi, Y., Tannenbaum, A.: A shape-based approach to robust image segmentation. In: Campilho, A.C., Mohamed S.K. (eds.) Proceedings of the Third International Conference on Image Analysis and Recognition, pp. 173–183. Springer, Berlin (2006)

  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)

  53. Woo, J.-H., Slomka, P., Kuo, J., Hong, B.-W.: Multiphase segmentation using an implicit dual shape prior: application to detection of left ventricle in cardiac MRI. Comput. Vis. Image Underst. 117(9), 1084–1094 (2013)

    Article  Google Scholar 

  54. Song, Q., Wu, X., Liu, Y., Garvin, M., Sonka, M.: Simultaneous searching of globally optimal interacting surfaces with shape priors. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, pp. 2879–2886 (2010)

  55. Bland, J., Altman, D.: Statistical methods for assessing agreement between two methods of clinical measurements. Lancet 1, 307–310 (1986)

  56. El Berbari, R., Bloch, I., Redheuil, A., Angelini, E.D., Mousseaux, E., Frouin, F., Herment, A.: Automated segmentation of the left ventricle including papillary muscles in cardiac magnetic resonance images. In: Proceedings of Functional Imaging Modelling of the Heart (2007)

Download references

Acknowledgments

The authors would 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. M-T Lo was supported by NSC (Taiwan, ROC), Grant No NSC 102-2221-E-008-008, joint foundation of CGH and NCU, Grant No. CNJRF-101CGH-NCU-A4, VGHUST103-G1-3-3 and NSC support for the Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan (NSC 102-2911-I-008-001).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Men-Tzung Lo.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pham, VT., Tran, TT., Shyu, KK. et al. Multiphase B-spline level set and incremental shape priors with applications to segmentation and tracking of left ventricle in cardiac MR images. Machine Vision and Applications 25, 1967–1987 (2014). https://doi.org/10.1007/s00138-014-0626-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-014-0626-1

Keywords

Navigation