Abstract
In this paper, new numerical interpretation of CT scans to aid stroke care is considered. Semantic models are used to explain the essence of the observed reality contained in complex structures and imaged features to have full cognition of the reality under investigation. Proposed concept and particular implementation of a cognitive seon is based on integrated, image-based descriptors of CT diagnostic imaging. Brain tissue impairment was numerically measured to characterize ischemic stroke severity, dynamics and extent. In consequence, emergent stroke decisions could be supported using the seon of the integrated descriptive components to predict stroke treatment output. Respective experiments with a database of 145 strokes and controls have confirmed the usefulness and significant efficiency of the specific seon while the correlation of the descriptive measurements to ground true of clinical assessments of stroke cases was satisfied. The most highlighted contribution is model-based interpretation of stroke problem with insightful data-driven parametrization.
Keywords
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- 1.
NIH Stroke Scale for quantifying stroke severity.
- 2.
Family of filter banks with angular resolution iteratively refined by invoking more levels of decomposition was used to efficiently capture and represent surface-like singularities.
- 3.
var – Variance, rms – Root-mean-square, iqr – Interquartile range.
- 4.
Grey-Level Co-occurrence Matrix.
- 5.
The ABCD2 is used to identify patients at high risk of stroke following a TIA.
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This publication was funded by the National Science Centre (Poland) based on the decision DEC-2011/03/B/ST7/03649.
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Przelaskowski, A., Sobieszczuk, E., Domitrz, I. (2019). Descriptive Seons: Measure of Brain Tissue Impairment. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds) Information Technology in Biomedicine. ITIB 2019. Advances in Intelligent Systems and Computing, vol 1011. Springer, Cham. https://doi.org/10.1007/978-3-030-23762-2_21
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DOI: https://doi.org/10.1007/978-3-030-23762-2_21
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