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Combining Binary Classifiers for Automatic Cartilage Segmentation in Knee MRI

  • Jenny Folkesson
  • Ole Fogh Olsen
  • Paola Pettersen
  • Erik Dam
  • Claus Christiansen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3765)

Abstract

We have developed a method for segmenting tibial and femoral medial cartilage in MR knee scans by combining two k Nearest Neighbors (kNN) classifiers for the cartilage classes with a rejection threshold for the background class. We show that with this threshold, two binary classifiers are sufficient compared to three binary classifiers in the traditional one-versus-all approach. We also show that the combination of binary classifiers produces better results than a kNN classifier that is trained to partition the voxels directly into three classes. The resulting sensitivity, specificity and Dice volume overlap of our method are 84.2%, 99.9% and 0.81 respectively. Compared to state-of-the-art segmentation methods, our method outperforms a fully automatic method and is comparable to a semi-automatic method.

Keywords

Automatic Segmentation Candidate Feature Active Shape Model Tibial Cartilage Femoral Cartilage 
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 2005

Authors and Affiliations

  • Jenny Folkesson
    • 1
  • Ole Fogh Olsen
    • 1
  • Paola Pettersen
    • 2
  • Erik Dam
    • 1
    • 2
  • Claus Christiansen
    • 2
  1. 1.Image Analysis GroupIT University of CopenhagenDenmark
  2. 2.Center for Clinical and Basic ResearchBallerupDenmark

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