A Biologically-Motivated Approach to Image Representation and Its Application to Neuromorphology

  • Luciano da F. Costa
  • Andrea G. Campos
  • Leandro F. Estrozi
  • Luiz G. Rios-Filho
  • Alejandra Bosco
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1811)


A powerful framework for the representation, characterization and analysis of two-dimensional shapes, with special attention given to neurons, is presented. This framework is based on a recently reported approach to scale space skeletonization and respective reconstructions by using label propagation and the exact distance transform. This methodology allows a series of remarkable properties, including the obtention of high quality skeletons, scale space representation of the shapes under analysis without border shifting, selection of suitable spatial scales, and the logical hierarchical decomposition of the shapes in terms of basic components. The proposed approach is illustrated with respect to neuromorphometry, including a novel and fully automated approach to automated dendrogram extraction and the characterization of the main properties of the dendritic arborization which, if necessary, can be done in terms of the branching hierarchy. The reported results fully corroborate the simplicity and potential of the proposed concepts and framework for shape characterization and analysis.


Retinal Ganglion Cell Image Representation Original Shape Label Propagation Comparative Neurology 
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  1. 1.
    Cesar Jr., R.M. and L. da F. Costa. Towards Effective Planar Shape Representation with Multiscale Digital Curvature Analysis based on Signal Processing Techniques, Pattern Recognition, 29 (1996) 1559–1569CrossRefGoogle Scholar
  2. 2.
    Costa, L. da F., R. M. Cesar Jr, R. C. Coelho, and J. S. Tanaka. 1998. Perspective on the Analysis and Synthesis of Morphologically Realistic Neural Networks, in Modeling in the Neurosciences (R. Poznanski Ed). Invited Paper, (1999) 505–528, Harwood Academic PublishersGoogle Scholar
  3. 3.
    Costa, L. da F. and T. J. Velte. Automatic characterization and classification of ganglion cells from the salamander retina, Journal of Comparative Neurology, 404(1) (1999) 33–51CrossRefGoogle Scholar
  4. 4.
    F. Attneave. Some Informational Aspects of Visual Perception, Psychological Review, 61 (1954) 183–193CrossRefGoogle Scholar
  5. 5.
    Costa, L. da F. Multidimensional scale-space shape analysis. Santorini, In: Proceedings International Workshop on Synthetic-Natural Hybrid Coding and Three Dimensional Imaging, Santorini-Greece, (1999) 214–7Google Scholar
  6. 6.
    Costa, L. da F. & Estrozi, L. F. Multiresolution Shape Representation without Border Shifting, Electronics Letters, 35 (1999) 1829–1830CrossRefGoogle Scholar
  7. 7.
    Wright, M.W. The Extended Euclidean Distance Transform-Dissertation submitted for the Degree of Doctor of Philosophy at the University of Cambridge, June 1995Google Scholar
  8. 8.
    Leyton, M. Symmetry-Curvature Duality, Computer Vision, Graphics and Image Processing, 38 (1987) 327–341CrossRefGoogle Scholar
  9. 9.
    Blum, H. Biological Shape and Visual Science (Part I), J. Theor. Biol., 38 (1973) 205–287CrossRefMathSciNetGoogle Scholar
  10. 10.
    Lindberg, T. Scale-space theory in computer vision. Kluwer Academic Publishers, 1994Google Scholar
  11. 11.
    Ogniewicz, R. L. Discrete Voronoi Skeletons, Hartung-Gorre Verlag, Germany, 1993Google Scholar
  12. 12.
    Tricot, C. 1995. Curves and Fractal Dimension, Springer-Verlag, PariszbMATHGoogle Scholar
  13. 13.
    Lantuéjoul, C. Skeletonization in quantitative metallography, In Issues of Digital Image Processing, R. M. Haralick and J.-C. Simon Eds., Sijthoff and Noordhoff, 1980Google Scholar
  14. 14.
    Fukuda, Y.; Hsiao, C. F.; Watanabe; M. and Ito, H.; Morphological Correlates of Physiologically Identified Y-, X-, and W-Cells in Cat Retina, Journal of Neurophysiology, 52(6) (1984) 999–1013Google Scholar
  15. 15.
    Leventhal, A. G. and Schall, D., Structural Basis of Orientation Sensitivity of Cat Retinal Ganglion Cells, The Journal of Comparative Neurology, 220 (1983) 465–475CrossRefGoogle Scholar
  16. 16.
    Linden, R. Dendritic Competition in the Developing Retina: Ganglion Cell Density Gradients and Laterally Displaced Dendrites, Visual Neuroscience, 10 (1993) 313–324CrossRefGoogle Scholar
  17. 17.
    Morigiwa, K.; Tauchi, M. and Fukuda, Y., Fractal analysis of Ganglion Cell Dendritic Branching Patterns of the Rat and Cat Retinae, Neurocience Research, Suppl. 10, (1989) S131–S140Google Scholar
  18. 18.
    Saito, H. A., Morphology of Physiologically Identified X-, Y-, and W-Type Retinal Ganglion Cells of the Cat, The Journal of Comparative Neurology, 221 (1983) 279–288CrossRefGoogle Scholar
  19. 19.
    Smith Jr., T. G.; Marks, W. B.; Lange, G. D.; Sheriff Jr., W. H. and Neale, E. A.; A Fractal Analysis of Cell Images, Journal of Neuroscience Methods, 27 (1989) 173–180CrossRefGoogle Scholar
  20. 20.
    Van Ooyen, A. & Van Pelt, J., Complex Periodic Behavior in a Neural Network Model with Activity-Dependent Neurite Outgrowth, Journal of Theoretical Biology, 179, (1996) 229–242CrossRefGoogle Scholar
  21. 21.
    Van Oss, C. & van Ooyen, A., 1997. Effects of Inhibition on Neural Network Development Through Active-Dependent Neurite Outgrowth, Journal of Theoretical Biology, 185 (1997) 263–280CrossRefGoogle Scholar
  22. 22.
    Vaney, D. I. Territorial Organization of Direction-Selective Ganglion Cells in Rabbit Retina, The Journal of Neuroscience, 14(11) (1994) 6301–6316Google Scholar
  23. 23.
    Velte, T.J. and Miller, R. F., Dendritic Integration in Ganglion Cells of the Mudpuppy Retina, Visual Neuroscience, 12 (1995) 165–175Google Scholar

Copyright information

© Springer-Verlag Berlin-Heidelberg 2000

Authors and Affiliations

  • Luciano da F. Costa
    • 1
  • Andrea G. Campos
    • 1
  • Leandro F. Estrozi
    • 1
  • Luiz G. Rios-Filho
    • 1
  • Alejandra Bosco
    • 2
  1. 1.Cybernetic Vision Research GroupIFSC — University of São PauloSão Carlos, SPBrazil
  2. 2.Neuroscience Research InstituteUniversity of California at Santa BarbaraSanta BarbaraUSA

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