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Machine Vision and Applications

, Volume 25, Issue 8, pp 1967–1987 | Cite as

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

  • Van-Truong Pham
  • Thi-Thao Tran
  • Kuo-Kai Shyu
  • Lian-Yu Lin
  • Yung-Hung Wang
  • Men-Tzung LoEmail author
Original Paper

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.

Keywords

Image segmentation Left ventricle tracking Level set method Incremental PCA Shape prior 

Notes

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).

References

  1. 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)CrossRefGoogle Scholar
  2. 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)CrossRefGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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)CrossRefGoogle Scholar
  5. 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)CrossRefGoogle Scholar
  6. 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)CrossRefGoogle Scholar
  7. 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)CrossRefGoogle Scholar
  8. 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)CrossRefGoogle Scholar
  9. 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)Google Scholar
  10. 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)Google Scholar
  11. 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)CrossRefGoogle Scholar
  12. 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)CrossRefGoogle Scholar
  13. 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)CrossRefGoogle Scholar
  14. 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)CrossRefGoogle Scholar
  15. 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)CrossRefGoogle Scholar
  16. 16.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1988)CrossRefGoogle Scholar
  17. 17.
    Sethian, J.A.: Level set methods and fast marching methods. Cambridge University Press, Cambridge (1999)zbMATHGoogle Scholar
  18. 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)MathSciNetzbMATHCrossRefGoogle Scholar
  19. 19.
    Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. Int. J. Comput. Vis. 22(1), 61–79 (1997)zbMATHCrossRefGoogle Scholar
  20. 20.
    Chan, T., Vese, L.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)zbMATHCrossRefGoogle Scholar
  21. 21.
    Ronfard, R.: Region-based strategies for active contour models. Int. J. Comput. Vis. 13(2), 229–251 (1994)CrossRefGoogle Scholar
  22. 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)CrossRefGoogle Scholar
  23. 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)zbMATHCrossRefGoogle Scholar
  24. 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)MathSciNetCrossRefGoogle Scholar
  25. 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)zbMATHCrossRefGoogle Scholar
  26. 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)zbMATHCrossRefGoogle Scholar
  27. 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)Google Scholar
  28. 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)CrossRefGoogle Scholar
  29. 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. 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)CrossRefGoogle Scholar
  31. 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)Google Scholar
  32. 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)CrossRefGoogle Scholar
  33. 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)Google Scholar
  34. 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)CrossRefGoogle Scholar
  35. 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)Google Scholar
  36. 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)CrossRefGoogle Scholar
  37. 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)CrossRefGoogle Scholar
  38. 38.
    Leu, J.G.: Shape normalization through compacting. Pattern Recognit. Lett. 10(4), 243–250 (1989)MathSciNetzbMATHCrossRefGoogle Scholar
  39. 39.
    Pei, S., Lin, C.: Image normalization for pattern recognition. Image Vis. Comput. 13(10), 711–723 (1995)CrossRefGoogle Scholar
  40. 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)CrossRefGoogle Scholar
  41. 41.
    Mumford, D., Shah, J.: Optimal approximations by piecewise smooth functions and associated variational problems. Commun. Pure Appl. Math. 42(5), 577–685 (1989)MathSciNetzbMATHCrossRefGoogle Scholar
  42. 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)MathSciNetzbMATHCrossRefGoogle Scholar
  43. 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)MathSciNetCrossRefGoogle Scholar
  44. 44.
    Unser, M.: Splines: a perfect fit for signal and image processing. IEEE Signal Process. Mag. 16(6), 22–38 (1999)CrossRefGoogle Scholar
  45. 45.
    Kybic, J., Unser, M.: Fast parametric elastic image registration. IEEE Trans. Image Process. 12(11), 1427–1442 (2003)CrossRefGoogle Scholar
  46. 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)CrossRefGoogle Scholar
  47. 47.
    Levy, A., Lindenbaum, M.: Sequential Karhunen–Loeve basis extraction and its application to images. IEEE Trans. Image Process. 9(8), 1371–1374 (2000)zbMATHCrossRefGoogle Scholar
  48. 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)Google Scholar
  49. 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)CrossRefGoogle Scholar
  50. 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)Google Scholar
  51. 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)Google Scholar
  52. 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)Google Scholar
  53. 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)CrossRefGoogle Scholar
  54. 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)Google Scholar
  55. 55.
    Bland, J., Altman, D.: Statistical methods for assessing agreement between two methods of clinical measurements. Lancet 1, 307–310 (1986) Google Scholar
  56. 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)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Van-Truong Pham
    • 1
  • Thi-Thao Tran
    • 2
  • Kuo-Kai Shyu
    • 2
  • Lian-Yu Lin
    • 3
  • Yung-Hung Wang
    • 1
  • Men-Tzung Lo
    • 1
    Email author
  1. 1.Research Center for Adaptive Data Analysis and Center for Dynamical Biomarkers and Translational MedicineNational Central UniversityTaoyuan CountyTaiwan
  2. 2.Department of Electrical EngineeringNational Central UniversityTaoyuan CountyTaiwan
  3. 3.Department of Internal MedicineNational Taiwan University HospitalTaipeiTaiwan

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