Granular Attribute Selection: A Case Study of Rough Set Approach to MRI Segmentation

  • Sebastian Widz
  • Dominik Ślęzak
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8251)


We introduce a new rough set inspired approach to attribute selection. We consider decision systems with attributes specified by means of two layers: 1) general meta-attribute descriptions, and 2) their specific realizations obtained by setting up parameters of procedures calculating attribute values. We adopt methods designed for finding rough set reducts within the sets of attributes grouped into clusters, where each cluster contains potentially infinite amount of attributes realizing a single meta-attribute. As a case study, we discuss a rough set framework for multi-spectral Magnetic Resonance Image (MRI) segmentation.


Rough set reducts Attribute hierarchies MRI segmentation 


  1. 1.
    Kuncheva, L.I., Diez, J.J.R., Plumpton, C.O., Linden, D.E.J., Johnston, S.J.: Random Subspace Ensembles for fMRI Classification. IEEE Transactions on Medical Imaging 29(2), 531–542 (2010)CrossRefGoogle Scholar
  2. 2.
    Pawlak, Z.: Rough Sets – Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers (1991)Google Scholar
  3. 3.
    Kruczyk, M., Baltzer, N., Mieczkowski, J., Dramiński, M., Koronacki, J., Komorowski, J.: Random Reducts: A Monte Carlo Rough Set-based Method for Feature Selection in Large Datasets. Fundamenta Informaticae 127(1-4), 273–288 (2013)Google Scholar
  4. 4.
    Nguyen, S.H., Bazan, J., Skowron, A., Nguyen, H.S.: Layered Learning for Concept Synthesis. In: Peters, J.F., Skowron, A., Grzymała-Busse, J.W., Kostek, B.z., Swiniarski, R.W., Szczuka, M.S. (eds.) Transactions on Rough Sets I. LNCS, vol. 3100, pp. 187–208. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Janusz, A., Ślęzak, D.: Rough Set Methods for Attribute Clustering and Selection. Applied Artificial Intelligence (2014)Google Scholar
  6. 6.
    Koo, J.J., Evans, A.C., Gross, W.J.: 3-D Brain MRI Tissue Classification on FPGAs. IEEE Transactions on Image Processing 18(12), 2735–2746 (2009)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Maji, P., Pal, S.K.: Maximum Class Separability for Rough-Fuzzy C-Means Based Brain MR Image Segmentation. In: Peters, J.F., Skowron, A., Rybiński, H. (eds.) Transactions on Rough Sets IX. LNCS, vol. 5390, pp. 114–134. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  8. 8.
    Widz, S., Ślęzak, D.: Approximation Degrees in Decision Reduct-based MRI Segmentation. In: Howard, D., Rhee, P.K. (eds.) Frontiers in the Convergence of Bioscience and Information Technologies 2007, FBIT 2007, Jeju Island, Korea, October 11-13, pp. 431–436. IEEE Computer Society (2007)Google Scholar
  9. 9.
    Yao, Y., Zhao, Y., Wang, J.: On Reduct Construction Algorithms. In: Gavrilova, M.L., Tan, C.J.K., Wang, Y., Yao, Y., Wang, G. (eds.) Transactions on Computational Science II. LNCS, vol. 5150, pp. 100–117. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Kowalski, M., Stawicki, S.: SQL-Based Heuristics for Selected KDD Tasks over Large Data Sets. In: Ganzha, M., Maciaszek, L.A., Paprzycki, M. (eds.) Federated Conference on Computer Science and Information Systems, FedCSIS 2012, September 9-12, pp. 303–310. IEEE, Wrocław (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Sebastian Widz
    • 1
  • Dominik Ślęzak
    • 2
  1. 1.Systems Research InstitutePolish Academy of SciencesWarsawPoland
  2. 2.Institute of MathematicsUniversity of WarsawWarsawPoland

Personalised recommendations