Multiclassifier Fusion in Human Brain MR Segmentation: Modelling Convergence

  • Rolf A. Heckemann
  • Joseph V. Hajnal
  • Paul Aljabar
  • Daniel Rueckert
  • Alexander Hammers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)


Segmentations of MR images of the human brain can be generated by propagating an existing atlas label volume to the target image. By fusing multiple propagated label volumes, the segmentation can be improved. We developed a model that predicts the improvement of labelling accuracy and precision based on the number of segmentations used as input. Using a cross-validation study on brain image data as well as numerical simulations, we verified the model. Fit parameters of this model are potential indicators of the quality of a given label propagation method or the consistency of the input segmentations used.


Similarity Index Label Propagation Label Volume Decision Fusion Magnetic Resonance Image Volume 
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 2006

Authors and Affiliations

  • Rolf A. Heckemann
    • 1
  • Joseph V. Hajnal
    • 1
  • Paul Aljabar
    • 2
  • Daniel Rueckert
    • 2
  • Alexander Hammers
    • 3
  1. 1.Imaging Sciences Department, MRC Clinical Sciences CentreImperial College at Hammersmith Hospital CampusLondonUK
  2. 2.Department of ComputingImperial College LondonUK
  3. 3.Division of Neuroscience and Mental Health, MRC Clinical Sciences CentreImperial College at Hammersmith Hospital CampusLondonUK

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