Morphological Spectra as Tools for Texture Analysis

  • Juliusz L. Kulikowski
  • Malgorzata Przytulska
  • Diana Wierzbicka
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 45)


There are presented formal properties of morphological spectra as a novel tool for analysis of textures. It is described a multi-level structure of the system of morphological spectra and the method of calculation of spectral components. Formal properties of morphological spectra: symmetries, ability to describe parallel shifts and rotations of analyzed images are presented. Several comments concerning practical aspects of using morphological spectra to analysis of textures are also given.


Texture Analysis IEEE Computer Society Spectral Component Formal Property Parallel Shift 
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.


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  1. 1.
    Zhu Y.M., Gao Y., Goutte R., Amiel M. (1992). “Textural Boundary Detection Using Local Spatial Frequency Analysis”. Proc. 11th IAPR International Conference on Pattern Recognition, Hague, vol. III. IEEE Computer Society Press, Los Alamitos; 53–56.CrossRefGoogle Scholar
  2. 2.
    J.L Kulikowski., D. Wierzbicka “A Method of Microvascular Systems Analysis Based on Statistical Texture Parameters Evaluation”. Biocybernetics and Biomedical Eng., vol. 23. No 3, 2003, pp. 21–37.Google Scholar
  3. 3.
    J.L. Kulikowski, M. Przytulska, D. Wierzbicka. “Recognition of Textures Based on Analysis of Multilevel Morphological Spectra”. IFMBE Proceedings, Vol. 14. World Congress on Medical Physics and Biomedical Engineering, Seoul, 2006, pp. 2164–2167.Google Scholar
  4. 4.
    J.F Haddon., J.F. Boyce (1992). “Texture Segmentation and Region Classification by Orthogonal Decomposition of Cooccurence Matrices”. Proc. 11th IAPR International Conference on Pattern Recognition, Hague, vol. I. IEEE Computer Society Press, Los Alamitos: 692–695.CrossRefGoogle Scholar
  5. 5.
    T. Ojala, M. Pietikajnen. “Unsupervised Texture Segmentation Using Feature Distributions, Texture Analysis Using Pairwise Interaction Maps”, Image Analysis and Processing, 9th International Conference, ICIAP’97, Florence, Proc. vol. I. (A. Del Bimbo ed.), 1997, pp. 311–318.Google Scholar
  6. 6.
    G. Loum, J. Lemoine et al. “An Application of Wavelet Transform to Texture Analysis”. Proc. of the 9th Scandinavian Conference on Image Analysis, vol. 1, Uppsala, 1995, pp. 583–590.Google Scholar
  7. 7.
    Y. Xiaohan, J. Yla-Jaaski. “Unsupervised Texture Segmentation Based On the Modified Markov Random Field Model”. Proc. 11th IAPR International Conference on Pattern Recognition, Hague, vol. III. IEEE Computer Society Press, Los Alamitos, 1992, pp. 88–91.CrossRefGoogle Scholar
  8. 8.
    T.G Smith., G.D. Lange. “Biological Cellular Morphometry-Fractal Dimensions, Lacunarity and Multifractals”. Fractals in Biology and Medicine, vol. II (Losa G.A., Merlini D. et al. Eds.). Birkhauser, Basel, 1998, pp. 30–49.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Juliusz L. Kulikowski
    • 1
  • Malgorzata Przytulska
    • 1
  • Diana Wierzbicka
    • 1
  1. 1.Institute of Biocybernetics and Biomedical Engineering Polish Academy of SciencesWarsawPoland

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