Carotid Artery Segmentation Using an Outlier Immune 3D Active Shape Models Framework

  • Karim Lekadir
  • Guang-Zhong Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4190)


This paper presents an outlier immune 3D active shape models framework for robust volumetric segmentation of the carotid artery required for accurate plaque burden assessment. In the proposed technique, outlier handling is based on a shape metric that is invariant to scaling, rotation and translation by using the ratio of inter-landmark distances as a local shape dissimilarity measure. Tolerance intervals for each descriptor are calculated from the training samples and used to infer the validity of landmarks. The identified outliers are corrected prior to the model fitting using the ratios distributions and appearance information. To improve the feature point search, the method exploits the geometrical knowledge from the outlier analysis at the previous iteration to weight the gray level appearance based fitness measure. A combined intensity-phase feature point search is also introduced which significantly limits the presence of outliers and improves the overall search accuracy. Both numerical and in vivo assessments of the method involving volumetric segmentation of the carotid artery have shown that the outlier handling technique is capable of handling a significant presence of outliers independently of the amplitudes.


Tolerance Interval Active Shape Model Outlier Analysis Invariant Shape Likelihood Measure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Yang, F., Holzapfel, G., Schulze-Bauer, C., Stollberger, R., Thedens, D., Bolinger, L., Stolpen, A., Sonka, M.: Segmentation of wall and plaque in in-vitro vascular MR images. International Journal of Cardiovascular Imaging 19, 419–428 (2003)CrossRefGoogle Scholar
  2. 2.
    Han, C., Hatsukami, T.S., Hwang, J.N., Yuan, C.: A fast path minimal path active contour model. IEEE Transactions on Medical Imaging 10, 865–873 (2001)MATHGoogle Scholar
  3. 3.
    Ladak, H.M., Thomas, J.B., Mitchell, J.R., Rutt, B.K., Steinman, D.A.: A semi-automatic technique for measurement of arterial wall from black blood MRI. Medical Physics 28, 1098–1107 (2001)CrossRefGoogle Scholar
  4. 4.
    Cootes, T.F., Cooper, D., Taylor, C.J., Graham, J.: Active shape models - Their training and application. Computer Vision and Image Understanding (CVIU) 61, 38–59 (1995)CrossRefGoogle Scholar
  5. 5.
    Rogers, M., Graham, J.: Robust active shape model search. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 517–530. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  6. 6.
    Duta, N., Sonka, M.: Segmentation and interpretation of MR brain images: An improved active shape model. IEEE Transaction on Medical Imaging 17, 1049–1067 (1998)CrossRefGoogle Scholar
  7. 7.
    Behiels, G., Vandermeulen, D., Maes, F., Suetens, P., Dewaele, P.: Active shape model-based segmentation of digital X-ray images. In: Taylor, C., Colchester, A. (eds.) MICCAI 1999. LNCS, vol. 1679, pp. 128–137. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  8. 8.
    Li, H., Chutatape, O.: Boundary detection of optic disk by a modified ASM method. Pattern Recognition 36, 2093–2104 (2003)MATHCrossRefGoogle Scholar
  9. 9.
    Outlier detection and handling for robust 3D active shape models search. IEEE Transaction on Medical Imaging (submitted for review)Google Scholar
  10. 10.
    Cootes, T.F., Taylor, C.J.: Active shape model search using local grey-level models: A quantitative evaluation. In: Proc. British Machine Vision Conf. (BMVC) (1993)Google Scholar
  11. 11.
    Guttman, I.: Statistical tolerance regions: Classical and Bayesian. Griffin, London (1970)MATHGoogle Scholar
  12. 12.
    Crowe, L.A., Varghese, A., Mohiaddin, R.H., Yang, G.-Z., Firmin, D.N.: Elimination of residual blood flow-related signal in 3D volume-selective TSE arterial wall imaging using velocity-sensitive phase reconstruction. Journal of Magnetic Resonance Imaging 23 (2006)Google Scholar
  13. 13.
    Ginneken, B.v., Frangi, A.F., Staal, J.J., Romeny, B.M.H., Viergever, M.A.: Active shape model segmentation with optimal features. IEEE Transaction on Medical Imaging 21, 924–933 (2002)CrossRefGoogle Scholar
  14. 14.
    Davatzikos, C., Tao, X., Shen, D.: Hierarchical active shape models, using the wavelet transform. IEEE Transaction on Medical Imaging 22, 414–423 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Karim Lekadir
    • 1
  • Guang-Zhong Yang
    • 1
  1. 1.Visual Information Processing Group, Department of ComputingImperial College LondonUnited Kingdom

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