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Ischemic Stroke Modeling: Multiscale Extraction of Hypodense Signs

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Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC 2007)

Abstract

Multiscale extraction of the subtlest signs of hypodensity, which were often undetected in standard CT scan review was the subject of our research. Proposed method is as follows: evidence-based description of hypodense changes, the investigation of hypodensity across scales, basing on a set of over 20 hyperacute stroke exams, the improvement of wavelet-based display of ischemic stroke. Considered problems were: –extension of the brain tissues for marginal and missing space after deskulling and segmenting of unusual areas; –best basis selection;–non-perfect reconstruction across scales as an extraction of hypo-attenuating tendency. Increased visibility of hypodense signs on CT scans performed in patients with hyperacute stroke was noticed in subjective rating. In opinion of radiologists and image processing experts, enhanced perception of hypodense area was noticed for all test exams. The rate of unique, clear and doubtless extraction of hypodense area was 92% in 13 tested cases of hyperacute ischemic stroke.

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© 2007 Springer-Verlag Berlin Heidelberg

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Przelaskowski, A., Bargiel, P., Sklinda, K., Zwierzynska, E. (2007). Ischemic Stroke Modeling: Multiscale Extraction of Hypodense Signs. In: An, A., Stefanowski, J., Ramanna, S., Butz, C.J., Pedrycz, W., Wang, G. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2007. Lecture Notes in Computer Science(), vol 4482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72530-5_20

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  • DOI: https://doi.org/10.1007/978-3-540-72530-5_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72529-9

  • Online ISBN: 978-3-540-72530-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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