Detection and Enhancement of Line Structures in an Image by Anisotropic Diffusion

  • Koichiro Deguchi
  • Tadahiro Izumitani
  • Hidekata Hontani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2059)


This paper describes a method to enhance line structures in a gray level image. For this purpose, we blur the image using anisotropic gaussian filters along the directions of each line structures. In a line structure region the gradients of image gray levels have a uniform direction. To find such line structures, we evaluate the uniformity of the directions of the local gradients. Before this evaluation, we need to smooth out small structures to obtain line directions. We, first, blur the given image by a set of gaussian filters. The variance of the gaussian filter which maximizes the uniformity of the local gradient directions is detected position by position. Then, the line directions in the image are obtained from this blurred image. Finally, we blur the image using anisotropic filter again along the directions, and enhance every line structure.


Line structure enhancement multi-resolution analysis anisotropic filter structure tensor 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    J.B. Antoine Maintz, Petra A. vanden Elsen, and Max A. Viergever, Evaluation of Ridge Seeking Operators for Multi-modality Medical Image Matching, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 18, no. 3, 353–365, 1996.CrossRefGoogle Scholar
  2. 2.
    T. Lindeberg, Scale-space theory in computer vision, Kluwer, Boston, 1994.Google Scholar
  3. 3.
    J. Bigun, G.H. Granlund and J. Wiklund, Multidimensional Orientation Estimation With Applications to Texture Analysis and Optical Flow, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 13, no. 8, 775–790, 1991.CrossRefGoogle Scholar
  4. 4.
    J. Weickert, Multiscale texture enhancement, Lecture Notes in Comp. Science, vol.970, Springer, Berlin, 230–237, 1995.Google Scholar
  5. 5.
    J. Weickert, A Review of Nonlinear Diffusion Filtering, Lecture Notes in Comp. Science, vol.972, Springer, Berlin, 3–28, 1997.Google Scholar
  6. 6.
    Bart M. ter Haar Romeny(Ed.), Geometry-Driven Diffusion in Computer Vision, Kluwer Academic Publishers, 1–38, 1994.Google Scholar
  7. 7.
    Bart M. ter Haar Romeny, Introduction to Scale-Space Theory: Multiscale Geometric Image Analysis, Technical Report No. ICU-96-21, Utrecht University, 1996.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Koichiro Deguchi
    • 1
  • Tadahiro Izumitani
    • 2
  • Hidekata Hontani
    • 3
  1. 1.Graduate School of Information SciencesTohoku UniversitySendaiJapan
  2. 2.Faculty of EngineeringUniversity of TokyoTokyoJapan
  3. 3.Faculty of EngineeringYamagata UniversityYonezawaJapan

Personalised recommendations