Advertisement

Entropy-Optimized Texture Models

  • Sebastian Zambal
  • Katja Bühler
  • Jiří Hladůvka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5242)

Abstract

In order to robustly match a statistical model of shape and appearance (e.g. AAM) to an unseen image, it is crucial to employ a robust model fittness measure. Dense sampling of texture over the region covered by the shape of interest makes comparison of model and image in principle robust. However, when merely texture differences are taken into account, problems with low contrast regions, fuzzy features, global intensity variations, and irregularly varying structures occur.

In this paper we introduce a novel entropy-optimized texture model (ETM). We map gray values of training images such that pixels represent anatomical structures optimally in terms of information entropy. We match the ETM to unseen images employing Bayes’ law.

We validate our approach using four training sets (hearts in basal region, hearts in mid region, brain ventricles, and lumbar vertebrae) and conclude that ETMs perform better than AAMs. Not only they reduce the average point-to-contour error, they are better suited to cope with large texture variances due to different scanners and even modalities.

Keywords

Mutual Information Training Image Active Appearance Model Statistical Shape Model Brain Ventricle 
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.

References

  1. 1.
    Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active Shape Models – Their training and application. Computer Vision and Image Understanding 61(1), 38–59 (1995)CrossRefGoogle Scholar
  2. 2.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active Appearance Models. In: European Conference on Computer Vision, vol. 2, pp. 484–498 (1998)Google Scholar
  3. 3.
    Kittipanya-ngam, P., Cootes, T.F.: The effect of texture representations on AAM performance. In: International Conference of Pattern Recognition, pp. 328–331 (2006)Google Scholar
  4. 4.
    Viola, P., Wells, W.M.: Alignment by maximization of Mutual Information. International Journal of Computer Vision 24(2), 137–154 (1997)CrossRefGoogle Scholar
  5. 5.
    Cates, J., Fletcher, P.T., Styner, M., Shenton, M., Whitaker, R.: Shape modeling and analysis with entropy-based particle systems. In: Information Processing in Medical Imaging, pp. 333–345 (2007)Google Scholar
  6. 6.
    Balci, S.K., Golland, P., Shenton, M., Wells, W.M.: Free-form B-spline deformation model for groupwise registration. In: Medical Image Computing and Computer–Assisted Intervention (Statistical Registration Workshop), pp. 23–30 (2007)Google Scholar
  7. 7.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  8. 8.
    de Bruijne, M., Nielsen, M.: Shape particle filtering for image segmentation. Medical Image Computing and Computer–Assisted Intervention 1, 168–175 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Sebastian Zambal
    • 1
  • Katja Bühler
    • 1
  • Jiří Hladůvka
    • 1
  1. 1.VRVis Research Center for Virtual Reality and VisualizationAustria

Personalised recommendations