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Semi-automatic quantitative histogram analysis of the CT and MRI images

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper presents results on voxel histogram analysis for quantification of brain image sequence. We model the histogram as a sum of parameterized gaussian functions, where each function represents the distribution of samples for a single material in the volume. We find parameters for the collection of gaussian functions with the help of Levenberg-Marquardt method that make the model agrees with the histogram.
Rocznik
Strony
MT57--MT65
Opis fizyczny
Bibliogr. 8 poz., rys., tab., wykr.
Twórcy
autor
  • Laboratory of Information Technology, M. Curie-Sklodowska University, Pl. M.Curie-Sklodowskiej 1, 20-031 Lublin, Poland
  • Laboratory of Information Technology, M. Curie-Sklodowska University, Pl. M.Curie-Sklodowskiej 1, 20-031 Lublin, Poland
  • Laboratory of Information Technology, M. Curie-Sklodowska University, Pl. M.Curie-Sklodowskiej 1, 20-031 Lublin, Poland
  • Laboratory of Information Technology, M. Curie-Sklodowska University, Pl. M.Curie-Sklodowskiej 1, 20-031 Lublin, Poland
Bibliografia
  • [1] Bankman I.N., editor-in-chief, Handbook of Medical Imaging , Processing and analysis Academic Press, London, 2000.
  • [2] Duch W., Therapeutic·implications of computer model of brain activity for Alzheimer disease, J. of Medical Informatics and-Tech. Vol. 5, pp. IP27-IP34, 2000.
  • [3] Kennedy D., Filipek P., Caviness V ., Anatomic segmentation and volumetric calculations in nuclear magnetic resonance imaging, IEEE Trans. Med. Imaging Vol. 8, pp. 1-7, 1989.
  • [4] Laidlaw D., Material classification of magnetic resonance volume data, Msc thesis, California Institute of Technology, Pasadena, California, 1992.
  • [5] Press W. H, Flannery B. P., Teukolsky S.A, Vetterling W.T., Numerical recipes in C: The art of scientific computing, Cambridge UP, 1992.
  • [6] Rogowska J., Overview and fundamentals of medical image segmentation, in: Handbook of Medical Imaging, Processing and analysis, Academic Press. London, 2000.
  • [7] Wang Y., Adali T., Lau C., Kung S., Quantitative analysis of MR brain image sequences by adaptive self-organizing finite mixtures, Journal of VLSI Signal Processing Vol. 8, pp. 219-239, 1998.
  • [8] Wang Y., Adali T., Stochastic model based image analysis, to appear: Signal Processing for Magnetic Resonance Imaging and Spectroscopy (Chapter 14), Marcel Dekker, 2000.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-61146f63-898a-41ea-8d2a-1a53e3853590
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