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)


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.


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.


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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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