Complex Data Management in MRI Results Processing

  • Michal KvetEmail author
  • Monika Vajsová
  • Karol Matiaško
Part of the Studies in Computational Intelligence book series (SCI, volume 606)


Cancer is one of the most serious problem of current medicine. Effective diagnostics and proper treatment brings the possibility for patients to become healthy. Global management of MRI results—processing, visualizing is the main part of the developed project. Moreover, these data must be stored effectively to monitor the progress over the time. This document describes the principles of processing, detecting and storing data in column level temporal system with emphasis on index structures.


Magnetic Resonance Imaging Result Temporal Database Potential Anomaly Medical Magnetic Resonance Imaging Proton Precession 
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.



This publication is the result of the project implementation: Centre of excellence for systems and services of intelligent transport II., ITMS 26220120050 supported by the Research & Development Operational Programme funded by the ERDF and is also supported by the project VEGA 1/1116/11—Adaptive data distribution. Center of translational medicine, ITMS 26220220021 supported by the Research & Development Operational Programme funded by the ERDF. Podporujeme výskumné aktivity na Slovensku/Projekt je spolufinancovaný zo zdrojov EÚ.


  1. 1.
    Date, C.J.: Date on Database. Apress, Calif (2006)Google Scholar
  2. 2.
    Date, C.J., Darwen, H., Lorentzos, N.A.: Temporal Data and the Relational Model. Morgan Kaufmann, San Francisco (2003)Google Scholar
  3. 3.
    Hornak J.: The Basics of MRI, Interactive Learning Software. Henietta, New York (2008)Google Scholar
  4. 4.
    Jensen, C.S.: Introduction to Temporal Database ResearchGoogle Scholar
  5. 5.
    Johnston, T., Weis, R.: Managing Time in Relational Databases, Morgan Kaufmann, San Fransico (2010)Google Scholar
  6. 6.
    Kvet, M., Lieskovsk, A., Matiako, K.: Temporal data modelling, 2013. In: IEEE Conference ICCSE, pp. 452–459, 26–28 April 2013Google Scholar
  7. 7.
    Kvet, M., Matiako, K.: Epsilon temporal data in MRI results processing, 2014. In: IEEE Conference Digital Technologies, pp. 209–217, 9–11 July 2014Google Scholar
  8. 8.
    Kvet, M., Matiako, K.: Uni-temporal modelling extension at object versus attribute level, 2013. In: IEEE Conference EMS 2013, pp. 7–11, 20–22 Nov 2013Google Scholar
  9. 9.
    Kvet, M., Kvet, M., Matiako, K.: Application for brain tumour imaging. In: IEEE Conference IWSSIP 2014, pp. 47–50, 12–15 May 2014Google Scholar
  10. 10.
    Kvet, M., Matiako, K., Kvet, M.: Transaction management in fully temporal system, In: IEEE conference UKSim 2014, pp. 147–152, 26–28 March 2014Google Scholar
  11. 11.
    Kvet, M., Meina, J., Matiako, K.: Algorithm for brain tumour detections. Acta Electrotechnica et Informatica 12(2), 45–50 (2012)Google Scholar
  12. 12.
    Mat, J.: Transformation of relational databases to transaction-time temporal databases. In: ECBS-EERC, pp. 27–34 (2011)Google Scholar
  13. 13.
    Nekula, J.: Radiologie. Univerzita Palackho, Olomouc (2005). ISBN: 024410117Google Scholar
  14. 14.
    Pianykh, O.: Digital Imaging and Communications in Medicine. Springer, Berlin (2008). ISBN: 978-3-540-74570-9Google Scholar
  15. 15.
    Zeman, M.: Speciln Chirurgie. Galn (2006). ISBN: 8072622609Google Scholar
  16. 16.
  17. 17.
  18. 18.
  19. 19.

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Univerzity of ZilinaZilinaSlovakia

Personalised recommendations