Comparison of Two Restoration Techniques in the Context of 3D Medical Imaging

  • Miguel A. Rodriguez-Florido
  • Karl Krissian
  • Juan Ruiz-Alzola
  • Carl-Fredrik Westin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2208)


In this paper, we compare two restoration techniques applied to 3D angiographies and to femoral CT scans. The first technique uses a Partial Derivative Equation and the second one is based on an extension of adaptive Wiener filters. We first present each method. Then, we discuss and compare the estimation of the local orientations in 3D images obtained either by the smoothed gradient and the principal curvature directions or by the eigenvectors of the structure tensor. A good estimation of the orientations is essential because it directs the restoration process. Finally, we compare the restored images on both synthetic and real images for the two studied applications.


Real Image Noisy Image Structure Tensor Synthetic Image Restoration Technique 
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  1. [1]
    Abramatic J.F. and Silverman M. Nonlinear restoration of noisy images. IEEE Trans. of Pattern Analysis and Machine Intelligence, 4(2):141–149, 1982.zbMATHCrossRefGoogle Scholar
  2. [2]
    Bigün J. and Granlund G. H. and Wiklund J. Multidimensional orientation: texture analysis and optical flow. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-13(8), August 1991.Google Scholar
  3. [3]
    Knutsson H., Haglund L., Bårman H. and Granlund G. H. A framework for anisotropic adaptive filtering and analysis of image sequences and volumes. In Proceedings ICASSP-92, San Fransisco, CA, USA, March 1992. IEEE.Google Scholar
  4. [4]
    Knutsson H., Wilson R. and Granlund G. H. Anisotropic non-stationary image estimation and its applications-part i: Restoration of noisy images. IEEE Trans. on Communications. COM-31, 3:388–397, 1983.CrossRefGoogle Scholar
  5. [5]
    Krissian, K. Flux-based anisotropic diffusion: application to enhancement of 3d angiographies. Technical Report 0011, Instituto Universitario de Ciencias y Tecnologías Cibernéticas, Las Palmas, Spain, Dec. 2000.Google Scholar
  6. [6]
    Krissian, K. Traitement multi-échelle: applications à l’imagerie médicale et à la détection tridimensionnelle de vaisseaux. PhD thesis, Univ. de Nice-Sophia Antipolis, Av. Joseph Vallot, 06108 Nice cedex 2, 2000.Google Scholar
  7. [7]
    Krissian, K. and Malandain, G. and Ayache, N. Directional anisotropic diffusion applied to segmentation of vessels in 3d images. In Scale-Space Theory in Computer Vision (Scale-Space), volume 1252 of Lecture Notes in Computer Science, pages 345–348, Utrecht, The Netherlands, July 1997. Springer Verlag.Google Scholar
  8. [8]
    Krissian K., Rodriguez-Florido M.A., Ruiz-Alzola J., Westin C.-F. Comparison between two multidimensional anisotropic filtering techniques. Technical Report 18, Instituto Universitario de Ciencias y Tecnologías Cibernéticas, Las Palmas, Spain, 2001.Google Scholar
  9. [9]
    Perona, P. and Malik, J. Scale-Space and edge detection using anisotropic diffusion. IEEE Trans. on Pattern Analysis and Machine Intel., 12(7):629–639, July 1990.CrossRefGoogle Scholar
  10. [10]
    Schriber W.F. Wirephoto quality improvement by unsharp masking. J.Pattern Recognition, 2:117–121, 1970.CrossRefGoogle Scholar
  11. [11]
    Weickert, J. Anisotropic Diffusion in image processing. Teubner-Verlag, Stuttgart, 1998.zbMATHGoogle Scholar
  12. [12]
    Westin C.-F. and Bhalerao A. and Knutsson H. and Kikinis R. Using Local 3D Structure for Segmentation of Bone from Computer Tomography Images. In CVPR, pages 794–800, Puerto Rico, June 1997.Google Scholar
  13. [13]
    Westin C.F., Richolt J., Moharir V., Kikinis R. Affine adaptive filtering of ct data. Medical Image Analysis, 4:161–177, 2000.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Miguel A. Rodriguez-Florido
    • 1
  • Karl Krissian
    • 2
  • Juan Ruiz-Alzola
    • 1
  • Carl-Fredrik Westin
    • 3
  1. 1.Departamento de Señales y ComunicacionesUniversidad de Las Palmas de Gran Canaria — Las PalmasSpain
  2. 2.Departamento de Informática y SistemasUniversidad de Las Palmas de Gran Canaria — Las PalmasSpain
  3. 3.Surgical Planning Lab.BWH — Harvard Medical SchoolBostonUSA

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