Abstract
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.
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References
WHO: Integrated management of cardiovascular risk. The World Health Report 2002 Geneva, World Health Organization (2002)
WHO: Reducing risk and promoting healthy life. The World Health Report 2002 Geneva, World Health Organization (2002)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Duda, R., Hart, P., Stork, D.: Pattern classification. Wiley, New York (2000)
Serra, J.: Image analysis and mathematical morphology. A Press, London (1982)
Haralick, R.A., Shapiro, L.: Computer and robot vision, vol. I. Addison–Wesley, USA (1992)
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 Issue
Ballard, D.: Generalizing the hough transform to detect arbitrary shapes. Pattern Recog. 13(2), 111–122 (1981)
Gonzalez, R., Woods, R.: Digital image processing. Prentice Hall, USA (2002)
Salomon, D.: Computer graphics and geometric modeling. Springer, USA (1999)
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)
Lorensen, W., Cline, H.: Marching cubes: A high resolution 3D surface construction algorithm. Comput. Graph. 21(4), 163–169 (1987)
Schroeder, W., Martin, K., Lorensen, B.: The visualization toolkit, an object-oriented approach to 3D graphics. Prentice Hall, New York (2001)
Dice, L.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)
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Bravo, A., Vera, M., Garreau, M., Medina, R. (2011). Three–Dimensional Segmentation of Ventricular Heart Chambers from Multi–Slice Computerized Tomography: An Hybrid Approach. In: Cherifi, H., Zain, J.M., El-Qawasmeh, E. (eds) Digital Information and Communication Technology and Its Applications. DICTAP 2011. Communications in Computer and Information Science, vol 166. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21984-9_25
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DOI: https://doi.org/10.1007/978-3-642-21984-9_25
Publisher Name: Springer, Berlin, Heidelberg
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