Advertisement

Creaseness from level set extrinsic curvature

  • Antonio M. López
  • Felipe Lumbreras
  • Joan Serrat
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1407)

Abstract

Creases are a type of ridge/valley-like structures of a d dimensional image, characterized by local conditions. As creases tend to be at the center of anisotropic grey-level shapes, creaseness can be considered as a type of medialness. Among the several crease definitions, one of the most important is based on the extrema of the level set curvatures. In 2-d it is used the curvature of the level curves of the image landscape, however, the way it is usually computed produces a discontinuous creaseness measure. The same problem arises in 3-d with its straightforward extension and with other related creaseness measures. In this paper, we first present an alternative method of computing the level curve curvature that avoids the discontinuities. Next, we propose the Mean curvature of the level surfaces as creaseness measure of 3-d images, computed by the same method. Finally, we propose a natural extension of our first alternative method in order to enhance the creaseness measure.

Keywords

creaseness level set curvatures divergence structure tensor 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    J. Bigun, G. Granlund, and J. Wiklund. Multidimensional orientation estimation with applications to texture analysis and optical flow. IEEE Trans. on Pattern Analysis and Machine Intelligence, 13(8):775–790, 1991.CrossRefGoogle Scholar
  2. 2.
    D. Eberly, R. Gardner, B. Morse, S. Pizer, and C. Scharlach. Ridges for image analysis. Journal of Mathematical Imaging and Vision, 4:353–373, 1994.CrossRefGoogle Scholar
  3. 3.
    P. van den Elsen, J. Maintz, E-J. Pol, and M. Viergever. Automatic registration of CT and MR brain images using correlation of geometrical features. IEEE Trans. on Medical Imaging, 14:384–396, 1995.CrossRefGoogle Scholar
  4. 4.
    L. Florack, B. ter Haar Romeny, J. Koenderink, and M. Viergever. Cartesian differential invariants in scale-space. Journal of Mathematical Imaging and Vision, 3:327–348, 1993.CrossRefGoogle Scholar
  5. 5.
    J. Gauch and S. Pizer. Multiresolution analysis of ridges and valleys in grey-scale images. IEEE Trans. on Pattern Analysis and Machine Intelligence, 15:635–646, 1993.CrossRefGoogle Scholar
  6. 6.
    B. Jahne. Spatio-temporal image processing, volume 751 of Lecture Notes in Computer Science, chapter 8, pages 143–152. Springer-Verlag, 1993.Google Scholar
  7. 7.
    A. López, F. Lumbreras, and J. Serrat. Efficient computing of local creaseness. Technical Report 15, Computer Vision Center, campus of the UAB. Spain, 1997.Google Scholar
  8. 8.
    A. Lopez and J. Serrat. Ridge/valley-like structures: creases, separatrices and drainage patterns. Technical Report 21, Computer Vision Center, campus of the UAB. Spain, 1997.Google Scholar
  9. 9.
    J. Maintz, P. van den Elsen, and M. Viergever. Evaluation of ridge seeking operators for multimodality medical image matching. IEEE Trans. on Pattern Analysis and Machine Intelligence, 18:353–365, 1996.CrossRefGoogle Scholar
  10. 10.
    O. Monga and S. Benayoun. Using partial derivatives of 3d images to extract typical surface features. Computer Vision and Image Understanding, 61:171–189, 1995.CrossRefGoogle Scholar
  11. 11.
    W. Niessen, A. López, W. Van Enk, P. Van Roermund, B. ter Haar Romeny, and M. Viergever. In vivo analysis of trabecular bone architecture. In J. S. Duncan and G. Gindi, editors, Information Processing and Medical Imaging, volume 1230 of Lecture Notes in Computer Science, pages 435–440, 1997.Google Scholar
  12. 12.
    H. M. Schey. DIV, GRAD, CURL and all that. W. W. Norton & Company, 1973.Google Scholar
  13. 13.
    B. ter Haar Romeny and L. Florack. A multiscale geometric model of human vision. In W. Hendee and P. Well, editors, The Perception of Visual Information, pages 73–114. Springer-Verlag, 1993.Google Scholar
  14. 14.
    J. P. Thirion and A. Gourdon. Computing the differential characteristics of isointensity surfaces. Computer Vision and Image Understanding, 61:190–202, 1995.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Antonio M. López
    • 1
  • Felipe Lumbreras
    • 1
  • Joan Serrat
    • 1
  1. 1.Computer Vision Center and Departament d'InformàticaCampus UABBarcelonaSpain

Personalised recommendations