Intravascular Ultrasound Images Vessel Characterization Using AdaBoost

  • Oriol Pujol
  • Misael Rosales
  • Petia Radeva
  • Eduard Nofrerias-Fernández
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2674)


This paper presents a method for accurate location of the vessel borders based on boosting of classifiers and feature selection. Intravascular Ultrasound Images (IVUS) are an excellent tool for direct visualization of vascular pathologies and evaluation of the lumen and plaque in coronary arteries. Nowadays, the most common methods to separate the tissue from the lumen are based on gray levels providing non-satisfactory segmentations. In this paper, we propose and analyze a new approach to separate tissue from lumen based on an ensemble method for classification and feature selection. We perform a supervised learning of local texture patterns of the plaque and lumen regions and build a large feature space using different texture extractors. A classifier is constructed by selecting a small number of important features using AdaBoost. Feature selection is achieved by a modification of the AdaBoost. A snake is set to deform to achieve continuity on the classified image. Different tests on medical images show the advantages.


Feature Selection Feature Space Feature Point Gray Level Linear Discriminant Analysis 
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.


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  1. 1.
    R.E. Schapire: “The Boosting Approach to Machine Learning. An Overview”. MSRI Workshop on Nonlinear Estimation and Classification. 2002.Google Scholar
  2. 2.
    P. Viola and M. Jones: “Rapid Object Detection using a Boosted Cascade of Simple Features”. Accepted Conference on Computer Vision and Pattern Recognition 2001.Google Scholar
  3. 3.
    G. Ratsch, T. Onoda, and K.-R. Muller: “Soft Margins for AdaBoost”. NeuroColt2 Technical Report Series. NC-TR-1998-021.Google Scholar
  4. 4.
    J. Malik, S. Belongie, T. Leung, and J. Shi: “Contour and Texture Analysis for Image Segmentation”. International Journal of Computer Vision. 43(1), 7–27,2001.zbMATHCrossRefGoogle Scholar
  5. 5.
    J. Puzicha, T. Hoffman, and J. Buhmann: “Unsupervised texture segmentation in a deterministic anhealing framework”. Trans. on Pattern Recognition and Machine Intelligence. 20(8): 803–818,1998.CrossRefGoogle Scholar
  6. 6.
    M. Sonka, X. Zhang, M. Siebes et al.: “Segmentation of intravascular ultrasound images: A knowledge based approach”. IEEE Trans. on Medical Imaging. 14: 719–732. 1995.CrossRefGoogle Scholar
  7. 7.
    X. Zhang, C.R. McKay, and M. Sonka: “Tissue Characterization in intravascular ultrasound images”. IEEE Trans. on Medical Imaging. 17: 889–899. 1998.CrossRefGoogle Scholar
  8. 8.
    C. von Birgelen, A. van der Lugt, A. Nicosia et al.: “Computerized assessment of coronary lumen and atherosclerotic plaque dimensions in three-dimensional intravascular ultrasound correlated with histomorphometry”. Amer. J. Cardiol. 78: 1202–1209, 1996.CrossRefGoogle Scholar
  9. 9.
    J.D. Klingensmith, R. Shekhar, and D.G. Vince: “Evaluation of Three-Dimensional Segmentation Algorithms for Identification of Luminal and Medial-Adventitial Borders in Intravascular Ultrasound Images”, IEEE Trans. on Medical Imaging, 19(10): 996–1011, 2000.CrossRefGoogle Scholar
  10. 10.
    R. Haralick, K. Shanmugam, and I. Dinstein: “Textural Features for Image Classification”. IEEE Trans. System, Man, Cybernetics. 3: 610–621. 1973.CrossRefGoogle Scholar
  11. 11.
    P.P. Ohanian and R.C. Dubes: “Performance Evaluation for Four Classes of Textural Features”. Pattern Recognition. 25(8), 819–833, 1992.CrossRefGoogle Scholar
  12. 12.
    Trygve Randen and John H. Husoy: “Filtering for Texture Classification: A Comparative Study”. Pattern Recognition. 21(4): 291–310. 1999.Google Scholar
  13. 13.
    M. Turceyan: “Moment Based texture segmentation”. Pattern Recognition Letters. 15: 659–668. 1994.CrossRefGoogle Scholar
  14. 14.
    J. Martinez and F. Thomas: “Efficient computation of local geometrical moments”. Submitted to IEEE Trans. on Image Processing.Google Scholar
  15. 15.
    Richard O. Duda, Peter E. Hart, and David G. Stork: “Pattern Classification”. Wiley-Interscience, 2001. 2nd Ed.Google Scholar
  16. 16.
    M. Kass, A. Witkin, and D. Terzopoulos: “Snakes, Active contour models”. Int. J. Computer Vision, 1(4): 321–331. 1987.CrossRefGoogle Scholar
  17. 17.
    V. Caselles, F. Catte, T. Coll, and F. Dibos: “A geometric model for active contours”. Numerische Mathematik. 66: 1–31, 1993.zbMATHCrossRefMathSciNetGoogle Scholar
  18. 18.
    T. McInerney and D. Terzopoulos: “Deformable models in medical images analysis: a survey”. Medical Image Analysis. 1(2): 91–108, 1996.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Oriol Pujol
    • 1
  • Misael Rosales
    • 1
  • Petia Radeva
    • 1
  • Eduard Nofrerias-Fernández
    • 2
  1. 1.Computer Vision CenterUniversitat Autònoma de BarcelonaBellaterraSpain
  2. 2.Hospital Universitari Germans Trias i PujolCan Ruti. BarcelonaSpain

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