Approximation of Subtle Pathology Signs in Multiscale Domain for Computer-Aided Ischemic Stroke Diagnosis

  • Artur Przelaskowski
  • Rafał Jóźwiak
  • Grzegorz Ostrek
  • Katarzyna Sklinda
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5337)


Computed understanding of CT images used for aided stroke diagnosis was the subject of reported research. Subtle hypodense changes of brain tissue as direct ischemia signs was estimated and extracted to improve diagnosis. Fundamental value of semantic content representation approximated from source images was studied. Nonlinear approximation of subtle pathology signatures in multiscale domain was verified for several local bases including wavelets, curvelets, contourlets and wedgelets. Different rationales for best bases selection were considered. Target pathology estimation procedures were optimized with a criterion of maximally clear extraction of diagnostic information. Visual expression of emphasized hypodenstity was verified for a test set of 25 acute stroke examinations. Suggested methods of stroke nonlinear approximation in many scales may facilitate the early CT-based diagnosis.


Image nonlinear approximation multiscale image representation computer aided diagnosis ischemic stroke 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Adams, H.P., del Zoppo, G., Alberts, M.J.: Guidelines for the early management of adults with ischemic stroke. Stroke 38, 1655–1711 (2007)CrossRefGoogle Scholar
  2. 2.
    Mullins, M.E., Schaefer, P.W., Sorensen, A.G., Halpern, E.F.: CT and conventional and diffusionweighted MR imaging in acute stroke: study in 691 patients at presentation to the emergency department. Radiology 224, 353–360 (2002)CrossRefGoogle Scholar
  3. 3.
    von Kummer, R.: The impact of CT on acute stroke treatment. In: Lyden, P. (ed.) Thrombolytic Therapy for Stroke, pp. 249–278. Humana Press, New Jersey (2005)CrossRefGoogle Scholar
  4. 4.
    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 39(2), 127–131 (1997)CrossRefGoogle Scholar
  5. 5.
    Grimm, C., Hochmuth, A., Huppertz, H.J.: Voxel-based CT analysis for improved detection of early CT signs in cerebral infarction. Eur. Radiol., B315 (2005)Google Scholar
  6. 6.
    Chan, A.K., Peng, C.: Wavelets for sensing technologies. Artech House, Inc., Norwood (2003)Google Scholar
  7. 7.
    Doi, K.: Computer-aided diagnosis in medical imaging: Historical review, current status and future potential. Comp. Med. Imag. Graph 31, 198–211 (2007)CrossRefGoogle Scholar
  8. 8.
    DeVore, R.A.: Nonlinear approximation. Acta Numerica 7, 51–150 (1998)CrossRefzbMATHGoogle Scholar
  9. 9.
    Capobianco Guido, R., Pereira, J.C. (guest eds.): Wavelet-based algorithms for medical problems. Special issue of Computers in Biology and Medicine, vol. 37(4) (2007)Google Scholar
  10. 10.
    Daubechies, I.: Ten lectures on wavelets. SIAM, Philadelphia (1995)zbMATHGoogle Scholar
  11. 11.
    Welland, G.V. (ed.): Beyond Wavelets. Studies in Computational Mathematics, vol. 10. Academic Press, London (2003)zbMATHGoogle Scholar
  12. 12.
    Donoho, D.L.: Wedgelets: nearly-minimax estimation of edges. Tech Retort, Statist. Depart., Stanford University (1997)Google Scholar
  13. 13.
    Starck, J.-L., Candes, E.J., Donoho, D.L.: The curvelet transform for image denoising. IEEE Tran. Image Proc. 11(6), 670–684 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Candes, E.J., Demanet, L., Donoho, D.L., Ying, L.: Fast discrete curvelet transforms. Technical Report, Cal Tech. (2005)Google Scholar
  15. 15.
    Do, M.N., Vetterli, M.: Contourlets. In: Wavelets, B., Welland, G.V. (eds.). Academic Press, New York (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Artur Przelaskowski
    • 1
  • Rafał Jóźwiak
    • 1
  • Grzegorz Ostrek
    • 1
  • Katarzyna Sklinda
    • 2
  1. 1.Institute of RadioelectronicsWarsaw University of TechnologyWarsawPoland
  2. 2.Department of Radiology CMKP, CSK MSWiAWarsawPoland

Personalised recommendations