Robust Active Shape Model Search

  • Mike Rogers
  • Jim Graham
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2353)


Active shape models (ASMs) have been shown to be a powerful tool to aid the interpretation of images. ASM model parameter estimation is based on the assumption that residuals between model fit and data have a Gaussian distribution. However, in many real applications, specifically those found in the area of medical image analysis, this assumption may be inaccurate. Robust parameter estimation methods have been used elsewhere in machine vision and provide a promising method of improving ASM search performance. This paper formulates M-estimator and random sampling approaches to robust parameter estimation in the context of ASM search. These methods have been applied to several sets of medical images where ASM search robustness problems have previously been encountered. Robust parameter estimation is shown to increase tolerance to outliers, which can lead to improved search robustness and accuracy.


Medical Image Understanding Shape Active Shape Models Robust Parameter Estimation M-estimators RANSAC Weighted Least Squares 


  1. [1]
    M. J. Black and A. D. Jepson. EigenTracking: Robust matching and tracking og articulated objects using a view-based representation. In Proceedings of the European Conference on Computer Vision, pages 329–342. Springler-Verlag, 1996.Google Scholar
  2. [2]
    A. Chauhan. The use of active shapes models for the segmentation of the prostate gland from magnetic resonance images. Master’s thesis, University of Manchester, 2001.Google Scholar
  3. [3]
    T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham. Active Shape Models-their training and application. Computer Vision and Image Understanding, 61(1):38–59, Jan. 1995.Google Scholar
  4. [4]
    M. A. Fischler and R. C. Bolles. Random Sample Consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Commumications of the ACM, 24(6):381–395, 1981.MathSciNetCrossRefGoogle Scholar
  5. [5]
    J. P. Hampel, E. M. Ronchetti, P. J. Rousseeuw, and W. A. Stahel. Robust Statistics: An Approach Based on Influence Functions. Wiley, New York, 1986.zbMATHGoogle Scholar
  6. [6]
    A. Hill, C. T. Taylor, and T. F. Cootes. Object recognition by flexible template matching using genetic algorithms. In Proceedings of the 2 nd European Conference on Computer Vision, pages 852–856, Santa Margherita Ligure, Italy, May 1992.Google Scholar
  7. [7]
    P. J. Huber. Robust Statistics. Wiley, New York, 1981.zbMATHCrossRefGoogle Scholar
  8. [8]
    P. Meer, A. Mintz, and A. Rosenfeld. Robust regression methods for computer vision: A review. International Journal of Computer Vision, 6:59–70, 1991.CrossRefGoogle Scholar
  9. [9]
    M. Rogers. Exploiting Weak Constraints on Object Shape and Structure for Segmentation of 2-D Images. PhD thesis, University of Manchester, 2001.Google Scholar
  10. [10]
    M. Rogers and J. Graham. Exploiting weak shape constraints to segment capillary images in microangiopathy. In Proceedings of Medical Image Computing and Computer-Assisted Intervention, pages 717–716, Pittsburg, USA, 2000.Google Scholar
  11. [11]
    P. J. Rousseeuw. Least median of squares regression. Journal of the American Statistical Association, 79:871–880, 1984.MathSciNetzbMATHCrossRefGoogle Scholar
  12. [12]
    P. J. Rousseeuw. Robust Regression and Outlier Detection. Wiley, New York, 1987.zbMATHCrossRefGoogle Scholar
  13. [13]
    S. Solloway, C. E. Hutchinson, J. C. Waterton, and C. J. Taylor. Quantification of articular cartilage from MR images using Active Shape Models. In B. Buxton and R. Cipolla, editors, Proceedings of the 4 th European Conference on Computer Vision, volume 2, pages 400–411, Cambridge, England, April 1996. Springer-Verlag.Google Scholar
  14. [14]
    C. V. Stewart. MINPRAM, a new robust estimator for computer vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-17(10):925–938, 1995.CrossRefGoogle Scholar
  15. [15]
    C. V. Stewart. Bias in robust estimation caused by discontinuities and multiple structures. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(8):818–833, Aug 1997.Google Scholar
  16. [16]
    H. H. Thodberg and A. Rosholm. Application of the Active Shape Model in a commercial medical device for bone densitometry. In T. F. Cootes and C. J. Taylor, editors, Proceedings of the 12 th British Machine Vision Conference, volume 1, pages 43–52, September 2001.Google Scholar
  17. [17]
    P. H. S. Torr and D. W. Murray. The development and comparison of robust methods for estimating the fundamental matrix. International Journal of Computer Vision, 24(3):271–300, 1997.CrossRefGoogle Scholar
  18. [18]
    P. H. S. Torr and A. Zisserman. MLESAC: A new robust estimator with application to estimating image geometry. Computer Vision and Image Understanding, 78(1):138–156, April 2000.Google Scholar
  19. [19]
    Z. Zhang. Parameter estimation techniques: A tutorial with application to conic fitting. Image and Vision Computing, 15:59–76, 1997.CrossRefGoogle Scholar
  20. [20]
    Z. Zhang, R. Deriche, O. Faugeras, and Q.-T. Luong. A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry. Artificial Intelligence Journal, 78:87–119, October 1995.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Mike Rogers
    • 1
  • Jim Graham
    • 1
  1. 1.Division of Imaging Science and Biomedical EngineeringUniversity of ManchesterUK

Personalised recommendations