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SACMiner: A New Classification Method Based on Statistical Association Rules to Mine Medical Images

  • Carolina Y. V. Watanabe
  • Marcela X. Ribeiro
  • Caetano TrainaJr.
  • Agma J. M. Traina
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 73)

Abstract

The analysis of images to decision making has become more accurate thanks to the technological progress on acquiring medical images. In this scenario, new approaches have been developed and employed in the computer-aided diagnosis in order to be a second opinion to the physician. In this work, we present SACMiner, which is a new method of classification that takes advantage of statistical association rules. It works with continuous attributes and avoids introducing the bottleneck and inconsistencies in the learning model due to a discretization step, which is required in the most of the associative classification methods. Two new algorithms are employed in this method: the StARMiner* and the V-classifier. StARMiner* mines association rules over continuous feature values and the V-classifier decides which class best represents a test image, based on the statistical association rules mined. The results comparing SACMiner with other traditional classifiers show that the proposed method is well-suited in the task of classifying medical images.

Keywords

Statistical association rules Computer-aided diagnosis Associative classifier Medical images 

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References

  1. 1.
    Arimura, H., Magome, T., Yamashita, Y., Yamamoto, D.: Computer-aided diagnosis systems for brain diseases in magnetic resonance images. Algorithms 2(3), 925–952 (2009)CrossRefGoogle Scholar
  2. 2.
    Dua, S., Singh, H., Thompson, H.W.: Associative classification of mammograms using weighted rules. Expert Syst. Appl. 36(5) (2009)Google Scholar
  3. 3.
    Watanabe, C.Y.V., Ribeiro, M.X., Traina Jr., C., Traina, A.J.M.: Statistical Associative Classification of Mammograms - The SACMiner Method. In: Proceedings of the 12th International Conference on Enterprise Information Systems, vol. 2, pp. 121–128 (2010)Google Scholar
  4. 4.
    Ribeiro, M.X., Balan, A.G.R., Felipe, J.C., Traina, A.J.M., Traina Jr., C.: Mining Statistical Association Rules to Select the Most Relevant Medical Image Features. In: First International Workshop on Mining Complex Data (IEEE MCD 2005), Houston, USA, pp. 91–98. IEEE Computer Society Press, Los Alamitos (2005)Google Scholar
  5. 5.
    Thabtah, F.: A review of associative classification mining. Knowledge Engineering Review 22(1), 37–65 (2007)CrossRefGoogle Scholar
  6. 6.
    Agrawal, R., Imielinski, T., Swami, A.N.: Mining Association Rules between Sets of Items in Large Databases. In: Proceedings of the 1993 ACM SIGMOD ICMD, Washington, D.C., pp. 207–216 (1993)Google Scholar
  7. 7.
    Dong, G., Zhang, X., Wong, L., Li, J.: CAEP: Classification by aggregating emerging patterns. In: Arikawa, S., Nakata, I. (eds.) DS 1999. LNCS (LNAI), vol. 1721, pp. 30–42. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  8. 8.
    Yin, X., Han, J.: Cpar: Classification based on predictive association rules. In: SIAM International Conference on Data Mining, pp. 331–335 (2003)Google Scholar
  9. 9.
    Ordonez, C., Omiecinski, E.: Discovering association rules based on image content. In: IEEE Forum on Research and Technology Advances in Digital Libraries (ADL 1999), Baltimore, USA, pp. 38–49 (1999)Google Scholar
  10. 10.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Intl. Conf. on VLDB, Santiago de Chile, Chile, pp. 487–499 (1994)Google Scholar
  11. 11.
    Wang, X., Smith, M., Rangayyan, R.: Mammographic information analysis through association-rule mining. In: IEEE CCGEI, pp. 1495–1498 (2004)Google Scholar
  12. 12.
    Antonie, M.L., Zaïane, O.R., Coman, A.: Associative Classifiers for Medical Images. In: Zaïane, O.R., Simoff, S.J., Djeraba, C. (eds.) MDM/KDD 2002 and KDMCD 2002. LNCS (LNAI), vol. 2797, pp. 68–83. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  13. 13.
    Ribeiro, M.X., Bugatti, P.H., Traina, A.J.M., Traina Jr., C., Marques, P.M.A., Rosa, N.A.: Supporting content-based image retrieval and computer-aided diagnosis systems with association rule-based techniques. Data & Knowledge Engineering (2009)Google Scholar
  14. 14.
    Aumann, Y., Lindell, Y.: A statistical theory for quantitative association rules. In: ACM Press (ed.) The Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, California, United States, pp. 261–270 (1999)Google Scholar
  15. 15.
    Srikant, R., Agrawal, R.: Mining quantitative association rules in large relational tables. In: ACM SIGMOD International Conference on Management of Data, Montreal, Canada, pp. 1–12. ACM Press, New York (1996)Google Scholar
  16. 16.
    Quinlan, R.: C4.5: Programs for Machine Learning, San Mateo, CA (1993)Google Scholar
  17. 17.
    Domingos, P., Pazzani, M.: On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning 29(2-3), 103–130 (1997)CrossRefGoogle Scholar
  18. 18.
    Holte, R.C.: Very Simple Classification Rules Perform Well on Most Commonly Used Datasets. Machine Learning 11, 63–91 (1993)CrossRefGoogle Scholar
  19. 19.
    Balan, A.G.R., Traina, A.J.M., Traina Jr., C., Marques, P.M.d.A.: Fractal Analysis of Image Textures for Indexing and Retrieval by Content. In: 18th IEEE Intl. Symposium on Computer-Based Medical Systems - CBMS, Dublin, Ireland, pp. 581–586. IEEE Computer Society, Los Alamitos (2005)Google Scholar
  20. 20.
    Asuncion, A., Newman, D.: UCI Machine Learning Repository (2007)Google Scholar
  21. 21.
    Silva, J.E.d., Sá, J.P.M., Jossinet, J.: Classification of breast tissue by electrical impedance spectroscopy. Medical and Biological Engineering and Computing 38, 26–30 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Carolina Y. V. Watanabe
    • 1
  • Marcela X. Ribeiro
    • 2
  • Caetano TrainaJr.
    • 3
  • Agma J. M. Traina
    • 3
  1. 1.Department of Computer ScienceFederal University of RondôniaPorto VelhoBrazil
  2. 2.Department of Computer ScienceFederal University of São CarlosSão CarlosBrazil
  3. 3.University of São PauloDepartment of Computer ScienceSão CarlosBrazil

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