Rough Power Set Tree for Feature Selection and Classification: Case Study on MRI Brain Tumor

  • Waleed Yamany
  • Nashwa El-Bendary
  • Hossam M. Zawbaa
  • Aboul Ella Hassanien
  • Václav Snášel
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 237)

Abstract

This article presents a feature selection and classification system for 2D brain tumors from Magnetic resonance imaging (MRI) images. The proposed feature selection and classification approach consists of four main phases. Firstly, clustering phase that applies the K-means clustering algorithm on 2D brain tumors slices. Secondly, feature extraction phase that extracts the optimum feature subset via using the brightness and circularity ratio. Thirdly, reduct generation phase that uses rough set based on power set tree algorithm to choose the reduct. Finally, classification phase that applies Multilayer Perceptron Neural Network algorithm on the reduct. Experimental results showed that the proposed classification approach achieved a high recognition rate compared to other classifiers including Naive Bayes, AD-tree and BF-tree.

Keywords

Rough sets power trees K-mean clustering classification 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Moftah, H.M., Hassanien, A.E., Shoman, M.: 3D brain tumor segmentation scheme using K-means clustering and connected component labeling algorithms. In: IEEE International Conference in Intelligent Systems Design and Applications (ISDA), Cairo, Egypt, pp. 320–324 (2010)Google Scholar
  2. 2.
    Dash, M., Liu, H.: Feature selection for Classification. Intelligent Data Analysis 1(3), 131–156 (1997)CrossRefGoogle Scholar
  3. 3.
    Chen, Y., Miao, D., Wang, R., Wu, K.: A Rough Set Approach to Feature Selection Based on Power Set Tree. Knowledge-Based System 24, 275–281 (2011)CrossRefGoogle Scholar
  4. 4.
    Guo, Q.L., Zhang, M.: Implement web learning environment based on data mining. Knowledge-Based Systems 22(6), 439–442 (2009)CrossRefGoogle Scholar
  5. 5.
    Pawlak, Z.: Rough sets. International Journal of Computer and Information Science 11(5), 341–356 (1982)MathSciNetCrossRefMATHGoogle Scholar
  6. 6.
    Swiniarski, R.W., Skowron, A.: Rough set methods in feature selection and recognition. Pattern Recognition Letters 24, 833–849 (2003)CrossRefMATHGoogle Scholar
  7. 7.
    Pawlak, Z.: Rough sets: Theoretical aspects of reasoning about data. Kluwer (1991)Google Scholar
  8. 8.
    Hassanien, A.E., Kim, T.-H.: Breast cancer MRI diagnosis approach using support vector machine and pulse coupled neural networks. Journal of Applied Logic 10, 277–284 (2012)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Pawlak, Z., Grzymala-Busse, J., Slowinski, R., Ziarko, W.: Rough Sets. Communications of the ACM 38(11), 88–95 (1995)CrossRefGoogle Scholar
  10. 10.
    Jensen, R.: Combining rough and fuzzy sets for feature selection. Ph.D. Thesis, University of Edinburgh (2005)Google Scholar
  11. 11.
    Polkowski, L.: Rough Sets: Mathematical Foundations. Physica-Verlag (2003)Google Scholar
  12. 12.
    Lee, G.N., Fujita, H.: K-means Clustering for Classifying Unlabelled MRI Data. In: Proceedings of the 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications, pp. 92–98 (2007)Google Scholar
  13. 13.
    Liu, H., Motoda, H.: Feature Selection for Knowledge Discovery and Data Mining. Kluwer, Boston (1998)CrossRefMATHGoogle Scholar
  14. 14.
    Mi, J.S., Wu, W.Z., Zhang, W.X.: Approaches to knowledge reduction based on variable precision rough set model. Information Sciences 159(3-4), 255–272 (2004)MathSciNetCrossRefMATHGoogle Scholar
  15. 15.
    Prastawa, M., Bullitt, E., Gerig, G.: Simulation of brain tumors in MR images for evaluation of segmentation efficacy. Medical Image Analysis 13(2), 297–311 (2009)CrossRefGoogle Scholar
  16. 16.
    Cannas, L.M.: A framework for feature selection in high-dimensional domains. Ph.D. Thesis, University of Cagliari (2012)Google Scholar
  17. 17.
    Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press (1995)Google Scholar
  18. 18.
    Latha, P.P., Sharmila, J.S.: A dynamic 3D clustering algorithm for the ligand binding disease causing targets. IJCSNS International Journal of Computer Science and Network Security 9 (February 2009)Google Scholar
  19. 19.
    Dubey, R.B., Hanmandlu, M., Gupta, S.K., Gupta, S.K.: An advanced technique for volumetric analysis. International Journal of Computer Applications 1(1) (2010)Google Scholar
  20. 20.
    Zawbaa, H.M., El-Bendary, N., Hassanien, A.E., Kim, T.-H.: Event Detection Based Approach for Soccer Video Summarization Using Machine learning. International Journal of Multimedia and Ubiquitous Engineering (IJMUE) 7(2) (2012)Google Scholar
  21. 21.
    Yu, B., Zhu, D.H.: Automatic Thesaurus Construction for Spam Filtering Using Revised: Back Propagation Neural Network. Journal Expert Systems with Applications 37(1), 24–30 (2010)CrossRefGoogle Scholar
  22. 22.
    Wrblewski, J.: Finding minimal reducts using genetic algorithms. In: Proceedings of Second Annual Join Conference on Information Sciences, Wrightsville Beach, NC, September 28-October 1, pp. 186–189 (1995)Google Scholar
  23. 23.
    Zhai, L.Y., Khoo, L.-P., Fok, S.-C.: Feature extraction using rough set theory and genetic algorithms: an application for the simplification of product quality evaluation. Computers and Industrial Engineering 43, 661–676 (2002)CrossRefGoogle Scholar
  24. 24.
    Caruana, R., Niculescu-Mizil, A.: An empirical comparison of supervised learning algorithms. In: Proceedings of the 23rd International Conference on Machine Learning (2006)Google Scholar
  25. 25.
    Samala, R.K., Potunuru, V.S., Zhang, J., Cabrera, S.D., Qian, W.: Comparative Study of Feature Measures for Histopathological Images of the Lung. In: Digital Image Processing and Analysis, Tucson, Arizona United States, June 7-8. Medical Image Processing II (2010)Google Scholar
  26. 26.
    Wilkinson, L., Dallal, G.E.: Tests of significance in forward selection regression with an F-to enter stopping rule. Technometrics 23, 377–380 (1981)Google Scholar
  27. 27.
    Pearl, J.: Heuristics: Intelligent Search Strategies for Computer Problem Solving, p. 48. Addison-Wesley (1984)Google Scholar
  28. 28.
    Pfahringer, B., Holmes, G., Kirkby, R.: Optimizing the Induction of Alternating Decision Trees. In: Cheung, D., Williams, G.J., Li, Q. (eds.) PAKDD 2001. LNCS (LNAI), vol. 2035, pp. 477–487. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  29. 29.
    Shi, H.: Best-first decision tree learning. University of Waikato (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Waleed Yamany
    • 1
    • 6
  • Nashwa El-Bendary
    • 2
    • 6
  • Hossam M. Zawbaa
    • 3
    • 6
  • Aboul Ella Hassanien
    • 4
    • 6
  • Václav Snášel
    • 5
  1. 1.Faculty of Computers and InformationFayoum UniversityFayoumEgypt
  2. 2.Arab Academy for Science, Technology, and Maritime TransportCairoEgypt
  3. 3.Faculty of Computers and InformationBeniSuef UniversityBeniSuefEgypt
  4. 4.Faculty of Computers and InformationCairo UniversityCairoEgypt
  5. 5.VSB-Technical University of OstravaOstravaCzech Republic
  6. 6.Scientific Research Group in Egypt (SRGE)GizaEgypt

Personalised recommendations