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Image reconstruction by means of chaotic iterative algorithm

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Języki publikacji
EN
Abstrakty
EN
The image reconstruction problem from projection data for some particular schemes of reconstruction is considered. There are used the Algebraic Reconstruction Technique (ART) and the chaotic algebraic iterative algorithm CHART to reconstruct high-construct objects from incomplete data. The influence of various parameters of these algorithms, such as the relaxation coefficient , the number of iterations, the number of projection data, and the noise in the projection data on reconstruction quality for different schemes of reconstruction is investigated. Numerical simulation results for a number of modeling high-contrast objects are presented and discussed.
Rocznik
Strony
59--70
Opis fizyczny
Bibliogr. 17 poz., rys., tab.
Twórcy
autor
Bibliografia
  • [1] Herman G.T., Image Reconstruction from Projections, The Fundamentals of Computerized Tomography, Academic Press, New York 1980.
  • [2] Natterer F., The Mathematics of Computerized Tomography, John Wiley & Sons, New York 1986.
  • [3] Smith B., Cone-beam tomography: recent advances and a tutorial review, Optical Engineering, 1990, 29(5), 524-534.
  • [4] Patella D., Introduction to ground surface self-potential tomography, Geophysical Prospecting, 1997, 45, 653-681.
  • [5] Williams R.A.; Atkinson K., Luke S.P., Barlow R.K., Dyer B.C., Smith J., Manning M., Applications for tomographic technology in mining, minerals and food engineering, Particle and Particle Systems Characterization 2004, 12(2), 105-111.
  • [6] Kak A., Slaney M., Principles of Computerized Tomographic Imaging, IEEE Press, New York 1988.
  • [7] Gordon R., Bender R., Herman G.T., Algebraic reconstruction techniques (ART) for three-dimensional electron microscopy and X-ray photography, J. Theoretical Biology, 1970, 29, 471-481.
  • [8] Herman G.T., Lent A., Rowland S., ART: Mathematics and application (a report on the mathematical foundations and on the applicability to real data of the Algebraic Reconstruction Techniques), Journal of Theoretical Biology 1973, 43, 1-32.
  • [9] Herman G.T., A relaxation method for reconstructing objects from noisy x-rays, Math. Programming 1975, 8, 1-19.
  • [10] Eggermont P.P.B., Herman G.T., Lent A., Iterative algorithms for large partitioned linear systems with applications to image reconstruction, Linear Algebra and Its Appl. 1981, 40, 37-67.
  • [11] Liang Z.-P., Lauterbur P.C., Principles of Magnetic Resonance Imaging, Piscataway, IEEE Press, New York 2000.
  • [12] Harshbarger T.B., Twieg D.B., Iterative reconstruction of single-shot spiral MRI with off resonance, IEEE Trans. Med. Imag. 1999, 18, 196-205.
  • [13] Censor Y., Zenios S.A., Parallel Optimization, Theory, Algorithms, and Applications, Oxford University Press, New York 1997.
  • [14] Kaczmarz S., Angenäherte auflösung von systemn linearer gleichungen, Bull. Int. Acad. Polon. Sci. Lett. Ser. A, 1937, 35, 335-357.
  • [15] Gubareni N., Katkov A., Szopa J., Parallel asynchronous team algorithm for image reconstruction, Proceedings of the 15-th IMACS World Congress on Scientistic Computation, Modelling and Applied Mathematics, Berlin, Germany, 1997, Computational Mathematics, 1997, Vol. I (A. Sydow, ed.), Achim. Wessenschaft & Technik Verlag, Berlin, 553-558.
  • [16] Gubareni, N., Generalized model of asynchronous iterations for image reconstruction, Proceedings of the 3-rd Int. Conf. on Parallel Processing and Applied Mathematics PPAM'99, 266-275, Kazimierz Dolny, Poland, September 14-17, 1999, Techn. Univ. of Częstochowa, Częstochowa.
  • [17] Elsne L., Koltracht I., Neumann M., On the convergence of asynchronous paracontractions withapplication to tomographic reconstruction from incomplete data, Linear Algebra and its Appl. 1990, 130, 83-98.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPC6-0004-0033
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