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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)

Abstract

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.

Keywords

Image nonlinear approximation multiscale image representation computer aided diagnosis ischemic stroke 

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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

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