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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.

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