Can the Coronary Artery Centerline Extraction in Computed Tomography Images Be Improved by Use of a Partial Volume Model?

  • Maria A. Zuluaga
  • Edgar J. F. Delgado Leyton
  • Marcela Hernández Hoyos
  • Maciej Orkisz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6375)


We propose the use of a statistical partial volume (PV) model to improve coronary artery tracking in 3D cardiac computed tomography images, combined with a modified centerline extraction algorithm. PV effect is a challenge when trying to separate arteries from blood-filled cardiac cavities, causing leakage and erroneous segmentations. We include a Markov Random Field with a modified weighting scheme. First, synthetic phantoms were used to evaluate the robustness and accuracy of PV detection, as well as to determine the best settings. Average Dice similarity index obtained for PV voxels was 86%. Then cardiac images from eight patients were used to evaluate the usefulness of PV detection to separate real arteries from cavities, compared to Fuzzy C-means classification. Our PV detection scheme reduced approximately by half the number of leakages between artery and cavity. The new version of artery centerline extraction algorithm takes advantage of the PV detection capacity to separate arteries from cavities and to retrieve low-signal small vessels. We show some preliminary qualitative results of the complete method.


Image Moment Cardiac Cavity Pure Classis Centerline Extraction Arterial Lumen Narrowing 
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  1. 1.
    Hernández Hoyos, M., Serfaty, J.M., Maghiar, A., Mansard, C., Orkisz, M., Magnin, I.E., Douek, P.C.: Assessment of carotid artery stenoses in 3D contrast-enhanced magnetic resonance angiography, based on improved generation of the centerline. Machine Graphics and Vision 14(4), 349–378 (2005)Google Scholar
  2. 2.
    Lesage, D., Angelini, E.D., Bloch, I., Funka-Lea, G.: A review of 3D vessel lumen segmentation techniques: Models, features and extraction schemes. Medical Image Analysis 13(6), 819–845 (2009)CrossRefGoogle Scholar
  3. 3.
    Schaap, M., Metz, C., van Walsum, T., van der Giessen, A.G., Weustink, A.C., Mollet, N.R., et al.: Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms. Medical Image Analysis 13(5), 701–714 (2009)CrossRefGoogle Scholar
  4. 4.
    Hernández Hoyos, M., Zuluaga, M.A., Lozano, M., Prieto, J.C., Douek, P.C., Magnin, I.E., Orkisz, M.: Coronary centerline tracking in CT images with use of an elastic model and image moments. Midas Journal (2008),
  5. 5.
    Metz, C., Schaap, M., van der Giessen, A., van Walsum, T., Niessen, W.J.: Semi-automatic coronary artery centerline extraction in computed tomography angiography data. In: Proc. of ISBI, pp. 856–859 (2007)Google Scholar
  6. 6.
    Renard, F., Yang, Y.: Image analysis for detection of coronary artery soft plaques in MDCT images. In: Proc. of ISBI, pp. 25–28 (2008)Google Scholar
  7. 7.
    Luengo-Oroz, M.A., Ledesma-Carbayo, M.J., Gómez-Diego, J.J., García-Fernández, M.A., Desco, M., Santos, A.: Extraction of the coronary artery tree in cardiac computer tomographic images using morphological operators. In: Sachse, F.B., Seemann, G. (eds.) FIHM 2007. LNCS, vol. 4466, pp. 424–432. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  8. 8.
    Carrillo, J.F., Hernández Hoyos, M., Dávila, E.E., Orkisz, M.: Recursive tracking of vascular tree axes in 3D medical images. Int. J. Comp. Assisted Radiol. Surg. 1(6), 331–339 (2007)CrossRefGoogle Scholar
  9. 9.
    Santago, P., Gage, H.D.: Quatification of MR brain images by mixture density and partial volume modeling. IEEE Trans. Med. Imag. 12(3), 566–574 (1993)CrossRefGoogle Scholar
  10. 10.
    Shattuck, D., Sandor-Leahy, S., Schaper, K., Rottenberg, D., Leahy, R.: Magnetic resonance image tissue classification using a partial volume model. Neuroimage 14(5), 856–876 (2001)CrossRefGoogle Scholar
  11. 11.
    Zuluaga, M.A., Hernández Hoyos, M., Orkisz, M.: Evaluation of partial volume effects in computed tomography for the improvement of coronary artery segmentation. 23rd CARS - Computer Assisted Radiology and Surgery, Int. J. Comp. Assisted Radiol. Surg. 4, 40–41 (June 2009)Google Scholar
  12. 12.
    Besag, J.: On the statistical analysis of dirty picture. Journal of the Royal Statistical Society, Series B 48(3), 259–302 (1986)zbMATHMathSciNetGoogle Scholar
  13. 13.
    Dunn, J.C.: A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics 3, 32–57 (1973)zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Metz, C., Schaap, M., van Walsum, T., van der Giessen, A., Weustink, A., Mollet, N.R.A., Krestin, G., Niessen, W.J.: 3D Segmentation in the Clinic: A Grand Challenge II - Coronary Artery Tracking. Midas Journal (2008),

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© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Maria A. Zuluaga
    • 1
    • 2
  • Edgar J. F. Delgado Leyton
    • 1
  • Marcela Hernández Hoyos
    • 1
  • Maciej Orkisz
    • 2
  1. 1.Grupo IMAGINE, Grupo de Ingeniería BiomédicaUniversidad de los AndesBogotáColombia
  2. 2.Université de Lyon; Université Lyon 1; INSA-Lyon; CNRS UMR5220; INSERM U630; CREATISVilleurbanneFrance

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