Semi-Automatic Rough Classification of Multichannel Medical Imaging Data

  • Ahmed Elmoasry
  • Mohamed Sadek Maswadah
  • Lars Linsen
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)

Abstract

Rough set theory is an approach to handle vagueness or uncertainty. We propose methods that apply rough set theory in the context of segmentation (or partitioning) of multichannel medical imaging data. We put this approach into a semi-automatic framework, where the user specifies the classes in the data by selecting respective regions in 2D slices. Rough set theory provides means to compute lower and upper approximations of the classes. The boundary region between the lower and the upper approximations represents the uncertainty of the classification.We present an approach to automatically compute segmentation rules from the rough set classification using a k-means approach. The rule generation removes redundancies, which allows us to enhance the original feature space attributes with a number of further feature and object space attributes. The rules can be transferred from one 2D slice to the entire 3D data set to produce a 3D segmentation result. The result can be refined by the user by interactively adding more samples (from the same or other 2D slices) to the respective classes. Our system allows for a visualization of both the segmentation result and the uncertainty of the individual class representations. The methods can be applied to single- as well as multichannel (or multimodal) imaging data. As a proof of concept, we applied it to medical imaging data with RGB color channels.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Mohamed N. Ahmed, Sameh M. Yamany, Nevin Mohamed, Aly A. Farag, and Thomas Moriarty. A modified fuzzy c-means algorithm for bias field estimation and segmentation ofmri data. IEEE Transactions on Medical Imaging, 2002.Google Scholar
  2. 2.
    J. C. Bezdek. Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press - New York, 1981.Google Scholar
  3. 3.
    Songcan Chen and Daoqiang Zhang. Robust image segmentation using fcm with spatial constraints based on new kernel-induced distance measure. IEEE Transactions on Systems, Man, and Cybernetics, 2004.Google Scholar
  4. 4.
    Keh-Shih Chuang, Hong-Long Tzeng, Sharon Chen, Jay Wu, and Tzong-Jer Chen. Fuzzy c-means clustering with spatial information for image segmentation. Computerized Medical Imaging and Graphics, 2006.Google Scholar
  5. 5.
    Dunn, J.C.. A Fuzzy Relative of the ISODATA Process and its Use in Detecting Compact, Well Separated Clusters, J. Cyber., 2004, 3, 32-57.MathSciNetCrossRefGoogle Scholar
  6. 6.
    Hassanien, Aboul Ella, Abraham, Ajith, Peters, James F., and Kacprzyk, Janusz. Rough Sets in Medical Imaging: Foundations and Trends. Computational Intelligence in Medical Imaging: Techniques and Applications, G. Schaefer, A. Hassanien, J. Jiang, Eds. 2009.Google Scholar
  7. 7.
    Hirano S., and Tsumoto S., Segmentation of Medical Images Based on Approximationsin Rough Set Theory, Rough Sets and Current Trends in Computing., Third International Conference, RSCTC 2002, Malvern, PA, USA, 2002, 950-951.Google Scholar
  8. 8.
    P. Heckbert, Color image quantization for frame buffer display, Computer Graphics (Proceedings of ACM SIGGRAPH 82), 297-307.Google Scholar
  9. 9.
    Hirano S. and Tsumoto S., Rough representation of a region of interest in medical images., International Journal of Approximate Reasoning, (2005), vol. 40, 2334.Google Scholar
  10. 10.
    Komorowski J., Pawlak Z., Polkowski L. and Skowron A. Rough sets: A tutorial. In S. K. Pal and A. Skowron, editors, Rough FuzzyHybridization. A New Trend in Decision-Making. Springer-Verlag. 1999, 398.Google Scholar
  11. 11.
    A.W.C.Liew, S.H.Leung, and W.H.Lau. Fuzzy image clustering incorporating spatial continuity. IEE Proc.-Vis. Image Signal Process, 2000.Google Scholar
  12. 12.
    Lingras P. Applications of rough set based k-means,kohonen, ga clustering. Transactions on Rough Sets, 7:120-139., 2007.Google Scholar
  13. 13.
    J. B. MacQueen. Some methods for classification and analysis of multivariate observations. Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability, 1967, volume 1, 281-297.Google Scholar
  14. 14.
    Pal, Sankar K. (2001) Fuzzy image processing and recognition: Uncertainties handling and applications, Internat. J. Image Graphics 1 (2001) (2), 169-195.Google Scholar
  15. 15.
    Pal, Sankar K., B. Uma Shankar, Pabitra Mitra: Granular computing, rough entropy and object extraction. Pattern Recognition Letters 26(16): 2509-2517, 2005.Google Scholar
  16. 16.
    Pawlak Z. Classification of Objects by Means of Attributes. Institute for Computer Science, Polish Academy of Sciences. 1981 Report 429.Google Scholar
  17. 17.
    Pawlak Z. Rough sets. International J. Comp. Inform. Science. 1982 vol. (11), (341-356).Google Scholar
  18. 18.
    Pawlak Z. Rough sets: Theoretical aspects of reasoning about Data Dordrecht, Kluwer Academic Publishers., 1991.Google Scholar
  19. 19.
    Pawlak Z. Grzymala-Busse J. Slowinski R. and Ziarko W. Rough Sets. Communications of the ACM. 1995, vol. 38(11), (88-95).Google Scholar
  20. 20.
    Pawlak Z. and Skowron A. Rudiments of rough sets. Information Sciences. 2007 vol. 177, 3-27.MathSciNetCrossRefMATHGoogle Scholar
  21. 21.
    Peters, J.F., Pedrycz, W. Rough Sets: Mathematical Foundations. Physica-Verlag. 2002.Google Scholar
  22. 22.
    Peters, J.F. Classification of Perceptual Objects by Means of Features. International Journal of Information Technology and Intelligent Computing. 2007.Google Scholar
  23. 23.
    Peters, J.F., Pedrycz, W. Computational intelligence, in Electrical and Electronics EngineeringEncyclopedia. NY: John Wiley and Sons, Ltd., 2008.Google Scholar
  24. 24.
    Sushmita M. An evolutionary rough partitive clustering., Pattern Recognition Letters., 2004, volume 25, 1439-1449.Google Scholar
  25. 25.
    Swiniarski, Roman W., and Larry Hargis., Rough sets as a front end of neural-networks texture classifiers., Neurocomputing., 2001, volume 36(-4), 85-102.Google Scholar
  26. 26.
    Yun J., Zhanhuai L., Yong W. and Longbo Z., A Better Classifier Based on Rough Set and Neural Network for Medical Images., Proceedings of the Sixth IEEE International Conferenceon Data Mining, 2006, 853-857.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ahmed Elmoasry
    • 1
  • Mohamed Sadek Maswadah
    • 2
  • Lars Linsen
    • 1
  1. 1.Jacobs UniversityBremenGermany
  2. 2.South Valley UniversityQenaEgypt

Personalised recommendations