PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Description of biomedical textures by statistical properties of morphological spectra

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A class of mathematical model s of biological textures based on the multi-variable probability distributions of their morphological spectra is described. It is shown that a large class of such distributions can be presented by sufficient statistics consisting of the coefficients of their expansion into the series of multi-variable Hermite polynomials. The sufficient statistics can then be simplified by rejection of higher-order terms. The general concepts of mathematical models construction are illustrated by examples of textures of several biological tissues (aorta walls, liver and blood). The role of statistics based on absolute values of morphological spectral components and of their cross-correlation coefficients is underlined.
Twórcy
  • Nałęcz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, ul. Ks. Trojdena 4, 02-109 Warsaw, Poland, juliusz.kulikowski@ibib.waw.pl
Bibliografia
  • 1. Zaremba M.B., Palenichka R.M., Missaoui R.: Multi-Scale Morphological Modeling of a Class of Structural Texture. Machine Graphics and Vision 2005, 14, 2, 2005, 171-199.
  • 2. Arasteh S., Hung C-C.: Color and Texture Image Segmentation Using UniformLocal Binary Patterns. Machine Graphics and Vision 2006, 15, 3/4, 265-274.
  • 3. Manjunath B., Chellappa R.: Unsupervised Texture Segmentation Using Markov Random Fields. IEEE Trans. Pattern Analysis and Machine Intelligence 1991, 13, 5, 478-482.
  • 4. Laine A., Fan J.: Texture Classification by Wavelet Packet Signatures. IEEE Trans. Pattern Analysis and Machine Intelligence 1993, 15, 11, 1186-1191.
  • 5. Valkealathi K., Oja E.: Reduced Multidimensional Co-Occurrence Histograms in Texture Classification. IEEE Trans. Pattern Analysis and Machine Intelligence 1998, 20, 1, 90-94.
  • 6. Strzelecki M., Materka A.: Markov Random Fields as Models of Textured Biomedical Images. Proc. of 20th National Conf. Circuit Theory and Electronic Networks KTOiUE'97, Kołobrzeg 1997, 493-498.
  • 7. Smith T.G., Lange G.D.: Biological Cellular Morphometry - Fractal Dimensions, Lacunarity and Multifractals. In: G.A. Losa, D. Merlini et al. (Eds.). Fractals in Biology and Medicine, Vol. II, Birkhauser, Basel 1998, 30-49.
  • 8. Kulikowski J.L., Przytulska M., Wierzbicka D.: Recognition of Textures Based on Analysis of Multilevel Morphological Spectra. GESTS Int. Transactions on Computer Science and Engineering, 2007, 38, 1, 99-107.
  • 9. Kulikowski J.L., Przytulska M., Wierzbicka D.: Morphological Spectra as Tools for Texture Analysis. In: M. Kurzyński, E. Puchała et al. (Eds.) Computer Recognition Systems 2, Springer-Verlag, Heidelberg, 2007, 510-517.
  • 10. Zalmanzon L.A.: Fourier, Walsh and Haar Transformations, their Applications in Control, Communication and other Domains (in Russian). Nauka, Moscow 1989.
  • 11. Tichonov V.I., Tolkachev A.A.: Abnormal Fluctuations Affecting on Linear Systems (in Russian). Doklady AN SSSR, OTN, No 12, 1956.
  • 12. Kulikowski J.L., Przytulska M.: Biomedical Image Segmentation Based on Morphological Spectra. 4th European Congress of International Federation for Medical and Biomedical Engineering. Antwerpen, Belgium 2008.
  • 13. Przytulska M., Kulikowski J.L., Bajera A., Królicki L.: Comparison of SPECT Cerebral Image Examination Methods Based on Luminance Level and Morphological Spectra Evaluation. Biocybernetics and Biomedical Engineering 2009, 29, 1, 29-42.
  • 14. Pratt W.K.: Digital Image Processing. John Wiley & Sons, New York 2008.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPZ1-0059-0018
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.