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Mining Statistical Association Rules to Select the Most Relevant Medical Image Features

  • Marcela X. Ribeiro
  • Andre G. R. Balan
  • Joaquim C. Felipe
  • Agma J. M. Traina
  • Caetano TrainaJr.
Part of the Studies in Computational Intelligence book series (SCI, volume 165)

Abstract

In this chapter we discuss how to take advantage of association rule mining to promote feature selection from low-level image features. Feature selection can significantly improve the precision of content-based queries in image databases by removing noisy and redundant features. A new algorithm named StARMiner is presented. StARMiner aims at finding association rules relating low-level image features to high-level knowledge about the images. Such rules are employed to select the most relevant features. We present a case study in order to highlight how the proposed algorithm performs in different situations, regarding its ability to select the most relevant features that properly distinguish the images. We compare the StARMiner algorithm with other well-known feature selection algorithms, showing that StARMiner reaches higher precision rates. The results obtained corroborate the assumption that association rule mining can effectively support dimensionality reduction in image databases.

Keywords

Feature Vector Feature Selection Association Rule Relevance Feedback Association Rule Mining 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Marcela X. Ribeiro
    • 1
  • Andre G. R. Balan
    • 1
  • Joaquim C. Felipe
    • 2
  • Agma J. M. Traina
    • 1
  • Caetano TrainaJr.
    • 1
  1. 1.Department of Computer ScienceUniversity of São Paulo at São CarlosBrazil
  2. 2.Department of Physics and MathematicsUniversity of São PauloBrazil

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