Edge Based Segmentation of Left and Right Ventricles Using Two Distance Regularized Level Sets

  • Yu Liu
  • Yue Zhao
  • Shuxu Guo
  • Shaoxiang Zhang
  • Chunming LiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9475)


In this paper, we present a new approach for segmentation of left and right ventricles from cardiac MR images. A two-level-set formulation is proposed which is the extension of distance regularized level set evolution (DRLSE) model in [1], with the 0-level set and k-level set representing the endocardium and epicardium, respectively. The extraction of endocardium and epicardium is obtained as a result of the interactive curve evolution of the 0 and k level sets derived from the proposed variational level set formulation. The initialization of the proposed two-level-set DRLSE model is generated by performing the original DRLSE from roughly located endocardium. Experimental results have demonstrated the effectiveness of the proposed two-level-set DRLSE model.


  1. 1.
    Li, C., Xu, C., Gui, C., Fox, M.D.: Distance regularized level set evolution and its application to image segmentation. IEEE Trans. Image Process. 19, 3243–3254 (2010)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Paragios, N.: A variational approach for the segmentation of the left ventricle in cardiac image analysis. Int. J. Comput. Vis. 50, 345–362 (2002)zbMATHCrossRefGoogle Scholar
  3. 3.
    Petitjean, C., Dacher, J.N.: A review of segmentation methods in short axis cardiac MR images. Med. Image Anal. 15, 169–184 (2011)CrossRefGoogle Scholar
  4. 4.
    Kurkure, U., Pednekar, A., Muthupillai, R., Flamm, S.D., Kakadiaris, I.A.: Localization and segmentation of left ventricle in cardiac cine-MR images. IEEE Trans. Biomed. Eng. 56, 1360–1370 (2009)CrossRefGoogle Scholar
  5. 5.
    Qian, X., Lin, Y., Zhao, Y., Wang, J., Liu, J., Zhuang, X.: Segmentation of myocardium from cardiac mr images using a novel dynamic programming based segmentation method. Med. Phys. 42, 1424–1435 (2015)CrossRefGoogle Scholar
  6. 6.
    Frangi, A.F., Niessen, W.J., Viergever, M.A.: Three-dimensional modelling for functional analysis of cardiac images: a review. IEEE Trans. Med. Image 20, 2–5 (2001)CrossRefGoogle Scholar
  7. 7.
    Li, C., Kao, C., Gore, J.C., Ding, Z.: Minimization of region-scalable fitting energy for image segmentation. IEEE Trans. Image Process. 17, 1940–1949 (2008)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Chan, T., Vese, L.: Active contours without edges. IEEE Trans. Image Process. 10, 266–277 (2001)zbMATHCrossRefGoogle Scholar
  9. 9.
    Qian, X., Wang, J., Guo, S., Li, Q.: An active contour model for medical image segmentation with application to brain ct image. Med. Phys. 40, 8 (2012)Google Scholar
  10. 10.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. Int. J. Comput. Vis. 1, 321–331 (1988)CrossRefGoogle Scholar
  11. 11.
    Chen, Y., Tagare, H.D., Thiruvenkadam, S., Huang, F., Wilson, D., Gopinath, K.S., Richard Briggs, W., Geiser, E.A.: Using prior shapes in geometric active contours in a variational framework. IJCV 50, 315–328 (2002)zbMATHCrossRefGoogle Scholar
  12. 12.
    Leventon, M., Grimson, W., Faugeras, O.: Statistical shape influence in geodesic active contours. In: 2000 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 316–323 (2000)Google Scholar
  13. 13.
    Wu, J., Brigham, K.G., Simon, M.A., Brigham, J.C.: An implementation of independent component analysis for 3d statistical shape analysis. Biomed. Sign. Process. Control 13, 345–356 (2014)CrossRefGoogle Scholar
  14. 14.
    Jolly, M.: Automatic segmentation of the left ventricle in cardiac MR and CT images. Int. J. Comput. Vis. 70, 151–163 (2006)CrossRefGoogle Scholar
  15. 15.
    Zuluaga, M.A., Cardoso, M.J., Ourselin, S.: Automatic right ventricle segmentation using multi-label fusion in cardiac mri. In: Workshop on RV Segmentation Challenge in Cardiac MRI Medical Image Computing and Computer-Assisted Intervention (2012)Google Scholar
  16. 16.
    Bai, W., Shi, W., O’Regan, D., Tong, T., Wang, H., Jamil-Copley, S., Peters, N., Rueckert, D.: A probabilistic patch-based label fusion model for multi-atlas segmentation with registration refinement: Application to cardiac mr images. IEEE Trans. Med. Imaging 32, 1302–1315 (2013)CrossRefGoogle Scholar
  17. 17.
    Ringenberg, J., Deo, M., Devabhaktuni, V., Berenfeld, O., Boyers, P., Gold, J.: Fast, accurate, and fully automatic segmentation of the right ventricle in short-axis cardiac MRI. Comput. Med. Imaging Graph. 38, 190–201 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Yu Liu
    • 1
  • Yue Zhao
    • 1
  • Shuxu Guo
    • 1
  • Shaoxiang Zhang
    • 2
  • Chunming Li
    • 3
    Email author
  1. 1.College of Electronic Science and EngineeringJilin UniversityChangchunPeople’s Republic of China
  2. 2.Institute of Digital MedicineThird Military Medical University (TMMU)ChongqingPeople’s Republic of China
  3. 3.School of Electronic EngineeringUniversity of Electronic Science and Technology of China (UESTC)ChengduPeople’s Republic of China

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