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Applying voting to segmentation of MR images

  • Lasse Riis Østergaard
  • Ole Vilhelm Larsen
Shape Representation and Image Segmentation
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1451)

Abstract

The performance of applying voting to MR segmentation is investigated. Three different segmentation methods (fuzzy c-means, Bayes, and k-nearest neighbour) are used as input to the voting algorithm. Using human expert segmented images as a reference an error rate of 7.1% is obtained when applying voting. When comparing to the other methods it is seen that the results of applying the voting algorithm are slightly improved in terms of the error rate, minimum and maximum error.

Keywords

Cluster Center Segmentation Method Vote Algorithm Unanimity Vote Threshold Vote 
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.

References

  1. 1.
    J.C. Bezdek, L.O. Hall, and L.P. Clarke: Review of MR image segmentation techniques using pattern recognition. Medical Physics, 20(4):1033–1048, 1993.CrossRefPubMedGoogle Scholar
  2. 2.
    M. Bro-Nielsen and S. Cotin: Real-time Volumetric Deformable Models for Surgery Simulation using Finite Elements and Condensation. Institute of Mathematical Modelling, Technical University, Denmark, 1995.Google Scholar
  3. 3.
    L. P. Clarke, R. P. Velthuizen, M. A. Camacho, J. J. Heine, M. Vaidyanathan, L. O. Hall, R. W. Thatcher, and M. L. Silbiger: MRI Segmentation: Methods and Applications. Magnetic Resonance Imaging, 13(3):343–368, 1995.CrossRefPubMedGoogle Scholar
  4. 4.
    R.O. Duda and P.E. Hart: Pattern Classification and Scene Analysis. Wiley-Interscience, 1973.Google Scholar
  5. 5.
    P.T.English and C. Moore: MRI for Radiographers. Springer-Verlag, 1995.Google Scholar
  6. 6.
    U. Kuhn, Kühnapfel, H.-G. Krumm, and B. Neisius: The Karlsruhe Endoscopic Surgery Trainer-A “Virtual Reality” based Training System for Minimal Invasive Surgery. CAR, 1996.Google Scholar
  7. 7.
    L. Lam and C.Y. Suen: Application of Majority Voting to Pattern recognition: An Analysis of its Behaviour and Performance. IEEE Transactions on Systems, Man, and Cyberbetics,27(5):553–568, 1997.Google Scholar
  8. 8.
    B. Parhami: Voting Algorithms. IEEE Transactions on Reliability, 43(4):617–629, 1994.CrossRefGoogle Scholar
  9. 9.
    A. Pommert, M. Riemer, T. Schiemann, R. Schubert, U. Tiede, and K.H. Höhne: Three-Dimensional Imaging in Medicine: Methods and Applications. In Computer-Integrated Surgery, pages 155–174. The MIT Press, 1996.Google Scholar
  10. 10.
    M. Vaidyanathan, L.P. Clarke, C. Heidtman, R.P. Velthuizen, and L.O. Hall: Normal Brain Volume Measurement using Multispectral MRI Segmentation. Magnetic Resonance Imaging, 15(1):87–97, 1997.CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Lasse Riis Østergaard
    • 1
  • Ole Vilhelm Larsen
    • 1
  1. 1.Dept. of Medical Informatics and Image AnalysisAalborg UniversityAalborgDenmark

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