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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)

Abstract

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.

Keywords

Rough set reducts Attribute hierarchies MRI segmentation 

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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

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