Multiscale Integration for Pattern Recognition in Neuroimaging

  • Margarita Zaleshina
  • Alexander ZaleshinEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10122)


Multiscale, multilevel integration is a valuable method for the recognition and analysis of combined spatial-temporal characteristics of specific brain regions. Using this method, primary experimental data are decomposed both into sets of spatially independent images and into sets of time series. The results of this decomposition are then integrated into a single space using a coordinate system that contains metadata regarding the data sources. The following modules can be used as tools to optimize data processing: (a) the selection of reference points; (b) the identification of regions of interest; and (c) classification and generalization. Multiscale integration methods are applicable for achieving pattern recognition in computed tomography and magnetic resonance imaging, thereby allowing for comparative analyses of data processing results.


Multiscale integration Pattern recognition 


  1. 1.
    Andriole, K.P., Wolfe, J.M., Khorasani, R., Treves, S.T., Getty, D.J., Jacobson, F.L., Steigner, M.L., Pan, J.J., Sitek, A., Seltzer, S.E.: Optimizing analysis, visualization, and navigation of large image data sets: one 5000-section CT scan can ruin your whole day. Radiology 259, 346–362 (2011)CrossRefGoogle Scholar
  2. 2.
    Murthy, S.K.: Automatic construction of decision trees from data: a multi-disciplinary survey. Data Min. Knowl. Discov. 2, 345–389 (1998)CrossRefGoogle Scholar
  3. 3.
    Michie, D., Spiegelhalter, D.J., Taylor, C.C., Michie, E.D., Spiegelhalter, D.J., Taylor, C.C.: Machine learning, neural and statistical classification. Ellis Horwood Ser. Artif. Intell. 37(xiv), 289s (1994)zbMATHGoogle Scholar
  4. 4.
    Nazarova, M., Blagovechtchenski, E.: Modern brain mapping - what do we map nowadays? Front. Psychiatry. 6, 1–4 (2015)CrossRefGoogle Scholar
  5. 5.
    Vega-Pons, S., Avesani, P.: On pruning the search space for clustering ensemble problems. Neurocomputing 150, 481–489 (2015)CrossRefGoogle Scholar
  6. 6.
  7. 7.
    Lombaert, H., Arcaro, M., Ayache, N.: Brain transfer: spectral analysis of cortical surfaces and functional maps. In: Ourselin, S., Alexander, D.C., Westin, C.-F., Cardoso, M.J. (eds.) IPMI 2015. LNCS, vol. 9123, pp. 474–487. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-19992-4_37 CrossRefGoogle Scholar
  8. 8.
    Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52, 1059–1069 (2010)CrossRefGoogle Scholar
  9. 9.
    Diedrichsen, J.: A spatially unbiased atlas template of the human cerebellum. NeuroImage 33, 127–138 (2006)CrossRefGoogle Scholar
  10. 10.
    Stemmler, M., Mathis, A., Herz, A.: Connecting multiple spatial scales to decode the population activity of grid cells. Sci. Adv. 1, e1500816 (2015)CrossRefGoogle Scholar
  11. 11.
    McKeown, M.J., Makeig, S., Brown, G.G., Jung, T., Kindermann, S.S., Bell, A.J., Sejnowski, T.J.: Analysis of fMRI data by blind separation into independant spatial components. Hum. Brain Mapp. 6, 160–188 (1998)CrossRefGoogle Scholar
  12. 12.
    Barbeito, A., Painho, M., Cabral, P., O’neill, J.: A topological multilayer model of the human body. Geospat. Health 10, 199–204 (2015)CrossRefGoogle Scholar
  13. 13.
    Moser, E.I., Roudi, Y., Witter, M.P., Kentros, C., Bonhoeffer, T., Moser, M.-B.: Grid cells and cortical representation. Nat. Rev. Neurosci. 15, 466–481 (2014)CrossRefGoogle Scholar
  14. 14.
    Congedo, L.: Semi-automatic classification plugin for QGIS.
  15. 15.
    Brui, A., Dubinin, M.: Raster classification with DTclassifier for QGIS.

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Moscow Institute of Physics and TechnologyMoscowRussia

Personalised recommendations