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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 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.
References
Ciszek, B., Jozwiak, R., Sobieszczuk, E., et al.: Stroke Bricks - spatial brain regions to assess ischemic stroke location. Folia Morphol. 76(4), 568–573 (2017)
Ip, H.L., Liebeskind, D.S.: The future of ischemic stroke: flow from prehospital neuroprotection to definitive reperfusion. Interv. Neurol 2, 105–117 (2013)
Pulsinelli, W.A.: Selective Neuronal Vulnerability and Infarction in Cerebrovascular Disease. Primer on cerebrovascular diseases, Gulf Professional Publishing, ed. by Welch K.M.A. Caplan L.R. et al., pp. 104–107 (1997)
Oliveira, M.S., Fernandes, P.T., Avelar, W.M., et al.: Texture analysis of computed tomography images of acute ischemic stroke patients. Braz. J. Med. Biol. Res. 42(11), 1076–1079 (2009)
Thompson, J.R.: Empirical Model Building: Data, Models, and Reality. Wiley, Hoboken, New Jersey (2011)
Lu, Y.M., Do, M.N.: Multidimensional directional filter banks and surfacelets. IEEE Trans. Image Proc. 16(4), 918–931 (2007)
Yang, J., Zhang, Y., Yin, W.: A fast alternating direction method for TVL1-L2 signal reconstruction from partial Fourier data. IEEE J. Sel. Top. Signal Process. 4(2), 288–297 (2010)
Muir, K.W., Baird-Gunning, J., Walker, L., et al.: Can the ischemic penumbra be identified on noncontrast CT of acute stroke? Stroke 38, 2485–2490 (2007)
Levy, A.V., Brodie, J.D., Russell, A.G., et al.: The metabolic centroid method for PET brain image analysis. J. Cereb. Blood Flow Metab. 9, 388–397 (1989)
Soh, L., Tsatsoulis, C.: Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. IEEE Trans. Geosci. Remote. Sens. 37(2), 780–795 (1999)
Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features of image classification. IEEE Trans. Syst. Man Cyber SMC 3(6), 610–621 (1973)
Clausi, D.A.: An analysis of co-occurrence texture statistics as a function of grey level quantization. Can. J. Remote. Sens. 28(1), 45–62 (2002)
Karmakar, P., Teng, S.W., Zhang, D. et al.: Improved Tamura features for image classification using kernel based descriptors. In: Proceedings of the IEEE International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 1–8 (2017)
Scalzo, F., Hao, Q., Alger, J.R., Hu, X., Liebeskind, D.S.: Regional prediction of tissue fate in acute ischemic stroke. Ann. Biomed. Eng. 40(10), 2177–2187 (2012)
Krieger, D.W., Demchuk, A.M., Kasner, S.E., Jauss, M., Hantson, L.: Early clinical and radiological predictors of fatal brain swelling in ischemic stroke. Stroke 30, 287–292 (1999)
Canazza, A., Minati, L., Boffano, C., Parati, E., Binks, S.: Experimental models of brain ischemia: a review of techniques, magnetic resonance imaging, and investigational cell-based therapies. Front Neurol 5(19), 1–15 (2014)
Acknowledgment
This publication was funded by the National Science Centre (Poland) based on the decision DEC-2011/03/B/ST7/03649.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-23762-2_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-23761-5
Online ISBN: 978-3-030-23762-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)