Automatic Early Stroke Recognition Algorithm in CT Images

  • Grzegorz Ostrek
  • Artur Przelaskowski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7339)


The subject of the reported study was automatic recognition of early ischemic stroke lesions in CT scans. Proposed extraction method was based on the investigated specificity of tissue texture features in hypothetical penumbra regions.

Prediction of such regions was estimated by initial hypodensity enhancement procedure. Block-oriented areas of selected brain tissue were analyzed in both source and multiscale-processed data domains. The extraction and selection of well differentiating features were fundamental effort to verify research hypothesis that acute ischemic tissue is noticeably altered in CT imaging. Moreover, various classifiers were examined on large feature data sets. Limitations and shortcomings caused by a class imbalance problem were considered. Experimental verification of designed and implemented recognition procedures is the main input of this paper.


Support Vector Machine Automatic Recognition Perfect Reconstruction Class Imbalance Problem Propose Extraction Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    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)PubMedCrossRefGoogle Scholar
  2. 2.
    Chang, C.-C., Lin, C.-J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2(3), 1–27 (2011)Google Scholar
  3. 3.
    Chawla, M., Sharma, S., Sivaswamy, J., Kishore, L.T.: A method for automatic detection and classification of stroke from brain CT images. In: Conf. Proc. IEEE Eng. Med. Biol. Soc., pp. 3581–3584 (2009)Google Scholar
  4. 4.
    Hachaj, T., Ogiela, M.R.: CAD system for automatic analysis of CT perfusion maps. Opto-Electron. Rev. 19(1), 95–103 (2011)CrossRefGoogle Scholar
  5. 5.
    Hudyma, E., Terlikowski, G.: Computer-aided detecting of early strokes and its evaluation on the base of CT images. In: International Multiconference on Computer Science and Information Technology, pp. 251–254 (2008)Google Scholar
  6. 6.
    Kidwell, C.S., Chalela, J.A., Saver, J.L., et al.: Comparison of MRI and CT for detection of acute intracerebral hemorrhage. JAMA 292, 1823–1830 (2004)PubMedCrossRefGoogle Scholar
  7. 7.
    Libman, R.B., Wirkowski, E., Alvir, J., Rao, T.H.: Conditions that mimic stroke in the emergency department. Implications for acute stroke trials. Arch. Neurol. 52, 1119–1122 (1995)PubMedCrossRefGoogle Scholar
  8. 8.
    Oguar, A., Hayakawa, K., Miyati, T., Maeda, F.: Improvement on detectability of early ischemic changes for acute stroke using nonenhanced computed tomography: Effect of matrix size. Eur. J. Radiol. 76, 162–166 (2010)CrossRefGoogle Scholar
  9. 9.
    Osborn, A.G.: Diagnostic Neuroradiology. Mosby (1994)Google Scholar
  10. 10.
    Przelaskowski, A., Bargiel, P., Sklinda, K., Zwierzynska, E.: Ischemic Stroke Modeling: Multiscale Extraction of Hypodense Signs. In: An, A., Stefanowski, J., Ramanna, S., Butz, C.J., Pedrycz, W., Wang, G. (eds.) RSFDGrC 2007. LNCS (LNAI), vol. 4482, pp. 171–181. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    Przelaskowski, A., Ostrek, G., Sklinda, K., Walecki, J., Jóźwiak, R.: Stroke Slicer for CT-Based Automatic Detection of Acute Ischemia. In: Kurzynski, M., Wozniak, M. (eds.) Computer Recognition Systems 3. AISC, vol. 57, pp. 447–454. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  12. 12.
    Przelaskowski, A., Walecki, J., Sklinda, K., Ostrek, G.: Stroke Monitor as a Device Improving Diagnostic Value of Computed Tomography in Hyperacute Stroke. In: Katashev, A., Dekhtyar, Y., Spigulis, J., Magjarevic, R. (eds.) 14th Nordic-Baltic Conference on Biomedical Engineering and Medical Physics. IFMBE Proceedings, vol. 20, pp. 544–547. Springer (2008)Google Scholar
  13. 13.
    Sazonov, E.S., Fulk, G., Sazonova, N., Schuckers, S.: Automatic Recognition of Postures and Activities in Stroke Patients. In: Conf. Proc. IEEE Eng. Med. Biol. Soc., pp. 2200–2203 (2009)Google Scholar
  14. 14.
    Sienkiewicz-Jarosz, H., Ryglewicz, D.: Badanie biomarkerów w udarach mózgu. Udar Mózgu 9(2), 67–74 (2007)Google Scholar
  15. 15.
    Tadeusiewicz, R., Szczepaniak, P.S.: Basic Concepts of Knowledge-Based Image Understanding. In: Nguyen, N.T., Jo, G.-S., Howlett, R.J., Jain, L.C. (eds.) KES-AMSTA 2008. LNCS (LNAI), vol. 4953, pp. 42–52. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  16. 16.
    Tamura, H., Mori, S., Yamawaki, T.: Textural features corresponding to visual perception. IEEE Trans. Systems, Man. and Cybern. 8(6), 460–472 (1978)CrossRefGoogle Scholar
  17. 17.
    Tomura, N., Uemura, K., et al.: Early CT finding in cerebral infarction. Radiology 168, 463–467 (1988)PubMedGoogle Scholar
  18. 18.
    Weir, N.U., Buchan, A.M.: A study of the workload and effectiveness of a comprehensive acute stroke service. J. Neurol. Neurosurg. Psychiatry 76, 863–865 (2005)PubMedCrossRefGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Grzegorz Ostrek
    • 1
  • Artur Przelaskowski
    • 1
  1. 1.Institute of RadioelectronicsWarsaw University of TechnologyWarsawPoland

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