Three–Dimensional Segmentation of Ventricular Heart Chambers from Multi–Slice Computerized Tomography: An Hybrid Approach

  • Antonio Bravo
  • Miguel Vera
  • Mireille Garreau
  • Rubén Medina
Part of the Communications in Computer and Information Science book series (CCIS, volume 166)


This research is focused on segmentation of the heart ventricles from volumes of Multi Slice Computerized Tomography (MSCT) image sequences. The segmentation is performed in three–dimensional (3–D) space aiming at recovering the topological features of cavities. The enhancement scheme based on mathematical morphology operators and the hybrid–linkage region growing technique are integrated into the segmentation approach. Several clinical MSCT four dimensional (3–D + t) volumes of the human heart are used to test the proposed segmentation approach. For validating the results, a comparison between the shapes obtained using the segmentation method and the ground truth shapes manually traced by a cardiologist is performed. Results obtained on 3–D real data show the capabilities of the approach for extracting the ventricular cavities with the necessary segmentation accuracy.


Segmentation mathematical morphology region growing multi slice computerized tomography cardiac images heart ventricles 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    WHO: Integrated management of cardiovascular risk. The World Health Report 2002 Geneva, World Health Organization (2002)Google Scholar
  2. 2.
    WHO: Reducing risk and promoting healthy life. The World Health Report 2002 Geneva, World Health Organization (2002)Google Scholar
  3. 3.
    Chen, T., Metaxas, D., Axel, L.: 3D cardiac anatomy reconstruction using high resolution CT data. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 411–418. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  4. 4.
    Fleureau, J., Garreau, M., Hernández, A., Simon, A., Boulmier, D.: Multi-object and N-D segmentation of cardiac MSCT data using SVM classifiers and a connectivity algorithm. Computers in Cardiology, 817–820 (2006)Google Scholar
  5. 5.
    Fleureau, J., Garreau, M., Boulmier, D., Hernández, A.: 3D multi-object segmentation of cardiac MSCT imaging by using a multi-agent approach. In: 29th Conf. IEEE Eng. Med. Biol. Soc., pp. 6003–6600 (2007)Google Scholar
  6. 6.
    Sermesant, M., Delingette, H., Ayache, N.: An electromechanical model of the heart for image analysis and simulation. IEEE Trans. Med. Imag. 25(5), 612–625 (2006)CrossRefGoogle Scholar
  7. 7.
    El Berbari, R., Bloch, I., Redheuil, A., Angelini, E., Mousseaux, E., Frouin, F., Herment, A.: An automated myocardial segmentation in cardiac MRI. In: 29th Conf. IEEE Eng. Med. Biol. Soc., pp. 4508–4511 (2007)Google Scholar
  8. 8.
    Lynch, M., Ghita, O., Whelan, P.: Segmentation of the left ventricle of the heart in 3-D+t MRI data using an optimized nonrigid temporal model. IEEE Trans. Med. Imag. 27(2), 195–203 (2008)CrossRefGoogle Scholar
  9. 9.
    Assen, H.V., Danilouchkine, M., Dirksen, M., Reiber, J., Lelieveldt, B.: A 3D active shape model driven by fuzzy inference: Application to cardiac CT and MR. IEEE Trans. Inform. Technol. Biomed. 12(5), 595–605 (2008)CrossRefGoogle Scholar
  10. 10.
    Ecabert, O., Peters, J., Schramm, H., Lorenz, C., Von Berg, J., Walker, M., Vembar, M., Olszewski, M., Subramanyan, K., Lavi, G., Weese, J.: Automatic model-based segmentation of the heart in CT images. IEEE Trans. Med. Imaging 27(9), 1189–1201 (2008)CrossRefGoogle Scholar
  11. 11.
    Zhuang, X., Rhode, K.S., Razavi, R., Hawkes, D.J., Ourselin, S.: A registration–based propagation framework for automatic whole heart segmentation of cardiac MRI. IEEE Trans. Med. Imag. 29(9), 1612–1625 (2010)CrossRefGoogle Scholar
  12. 12.
    Bravo, A., Clemente, J., Vera, M., Avila, J., Medina, R.: A hybrid boundary-region left ventricle segmentation in computed tomography. In: International Conference on Computer Vision Theory and Applications, Angers, France, pp. 107–114 (2010)Google Scholar
  13. 13.
    Suzuki, K., Horiba, I., Sugie, N., Nanki, M.: Extraction of left ventricular contours from left ventriculograms by means of a neural edge detector. IEEE Trans. Med. Imag. 23(3), 330–339 (2004)CrossRefGoogle Scholar
  14. 14.
    Duda, R., Hart, P., Stork, D.: Pattern classification. Wiley, New York (2000)zbMATHGoogle Scholar
  15. 15.
    Serra, J.: Image analysis and mathematical morphology. A Press, London (1982)zbMATHGoogle Scholar
  16. 16.
    Haralick, R.A., Shapiro, L.: Computer and robot vision, vol. I. Addison–Wesley, USA (1992)Google Scholar
  17. 17.
    Pauwels, E., Frederix, G.: Finding salient regions in images: Non-parametric clustering for images segmentation and grouping. Computer Vision and Image Understanding 75(1,2), 73–85 (1999); Special IssueCrossRefGoogle Scholar
  18. 18.
    Ballard, D.: Generalizing the hough transform to detect arbitrary shapes. Pattern Recog. 13(2), 111–122 (1981)CrossRefzbMATHGoogle Scholar
  19. 19.
    Gonzalez, R., Woods, R.: Digital image processing. Prentice Hall, USA (2002)Google Scholar
  20. 20.
    Salomon, D.: Computer graphics and geometric modeling. Springer, USA (1999)CrossRefzbMATHGoogle Scholar
  21. 21.
    Livnat, Y., Parker, S., Johnson, C.: Fast isosurface extraction methods for large image data sets. In: Bankman, I.N. (ed.) Handbook of Medical Imaging: Processing and Analysis, pp. 731–774. Academic Press, San Diego (2000)CrossRefGoogle Scholar
  22. 22.
    Lorensen, W., Cline, H.: Marching cubes: A high resolution 3D surface construction algorithm. Comput. Graph. 21(4), 163–169 (1987)CrossRefGoogle Scholar
  23. 23.
    Schroeder, W., Martin, K., Lorensen, B.: The visualization toolkit, an object-oriented approach to 3D graphics. Prentice Hall, New York (2001)Google Scholar
  24. 24.
    Dice, L.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Antonio Bravo
    • 1
  • Miguel Vera
    • 2
  • Mireille Garreau
    • 3
    • 4
  • Rubén Medina
    • 5
  1. 1.Grupo de BioingenieríaUniversidad Nacional Experimental del Táchira, Decanato de InvestigaciónSan CristóbalVenezuela
  2. 2.Laboratorio de Física, Departamento de CienciasUniversidad de Los Andes–TáchiraSan CristóbalVenezuela
  3. 3.INSERM, U 642RennesFrance
  4. 4.Laboratoire Traitement du Signal et de l’ImageUniversité de Rennes 1RennesFrance
  5. 5.Grupo de Ingeniería BiomédicaUniversidad de Los Andes, Facultad de IngenieríaMéridaVenezuela

Personalised recommendations