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Multiscale extraction of diagnostic content applied for CT brain examination

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Języki publikacji
EN
Abstrakty
EN
This paper presents the estimation methods of subtle hypodense changes of brain tissue in noncontrast CT scans. The purpose of reported research is improved detection of direct signs of hyperacute ischemic stroke. Proposed tool is nonlinear approximation in base of multiscale functions with respective thresholding. Different rationales for best basis selection were considered. Several local bases including wavelets, curvelets, contourlets and wedgelets were considered and characterized with a criterion of as fast as possible approximation error decay. Adaptive thresholding was suggested for defining of nonlinear approximation space for different image models. Procedures of estimation and extraction of diagnostic information were experimentally verified. Improved diagnosis of acute stroke eases was reported.
Twórcy
autor
autor
  • Institute of Radioelectronics, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warszawa, Poland, arturp@ire.pw.edu.pl
Bibliografia
  • 1. Donoho D.L., Vetterli M., DeVore R.A., Daubechies I.: Data compression and harmonic analysis. IEEE Trans. Inform. Theory, Special Issue, Inform. Theory: 1948–1998 Commemorative Issue 1998, 44, 6, 2435–2476.
  • 2. von Kummer R.: The impact of CT on acute stroke treatment, in: P. Lyden (Ed.), Thrombolytic Therapy for Stroke, Humana Press, New Jersey, USA, 2005, 249-278.
  • 3. Bendszus M., Urbach H., Meyer B., Schultheiss R., Solymosi L.: Improved CT diagnosis of acute middle cerebral artery territory infarcts with density-difference analysis. Neuroradiology 1997, 39, 2, 127–131.
  • 4. DeVore R.A.: Nonlinear approximation. Acta Numerica 1998, 7, 51–150.
  • 5. Capobianco Guido R., Pereira J.C. (guest editors): Wavelet-based algorithms for medical problems. Special issue of Computers in Biology and Medicine 2007, 37, 4.
  • 6. Daubechies I.: Ten lectures on wavelets. SIAM 1995.
  • 7. Mallat S.: A wavelet tour of signal processing, chapter IX. Second Edition. Academic Press 1999.
  • 8. Adams R.A., Fournier J.J.: Sobolev Spaces. Academic Press 2003.
  • 9. Frazier M., Jawerth B.: Decomposition of Besov spaces. Indiana Univ. Math. J. 1985, 34, 777–789.
  • 10. Welland, G.V. (Ed.): Beyond Wavelets. Studies in Computational Mathematics 10, Academic Press 2003.
  • 11. Donoho D.L.: Wedgelets: nearly-minimax estimation of edges. Tech. Retort, Statist. Depart., Stanford University 1997.
  • 12. Starck J.-L., Candes E.J., Donoho D.L.: The curvelet transform for image denoising. IEEE Tran. Image Proc. 2002, 11, 6, 670–684.
  • 13. Candes E.J., Demanet L., Donoho D.L., Ying L.: Fast discrete curvelet transforms. Technical Report, Cal. Tech. 2005.
  • 14. Do M.N., Vetterli M.: Contourlets. In: Beyond Wavelets, G. V. Welland, (Ed.) New York: Academic Press 2003.
  • 15. Przelaskowski A., Bargieł P., Sklinda K., Zwierzynska E.: Ischemic stroke modeling: multiscale extraction of hypodense signs. Proc 11th Int Conf Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, LNCS 4482, 171–181, 2007.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPZ1-0059-0002
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