Improving the Classification Accuracy of the Classic RF Method by Intelligent Feature Selection and Weighted Voting of Trees with Application to Medical Image Segmentation

  • Mohammad Yaqub
  • M. Kassim Javaid
  • Cyrus Cooper
  • J. Alison Noble
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7009)


Enhancement of the Random Forests to segment 3D objects in different 3D medical imaging modalities. More accurate voxel classification is achieved by intelligently selecting "good" features and neglecting irrelevant ones; this also leads to a faster training. Moreover, weighting each tree in the forest is proposed to provide an unbiased and more accurate probabilistic decision during the testing stage. Validation is performed on adult brain MRI and 3D fetal femoral ultrasound datasets. Comparisons between the classic Random Forests and the proposed new one show significant improvement on segmentation accuracy. We also compare our work with other techniques to show its applicability.


Random forests machine learning feature selection brain MRI segmentation 3D fetal ultrasound segmentation 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mohammad Yaqub
    • 1
    • 2
  • M. Kassim Javaid
    • 2
  • Cyrus Cooper
    • 2
  • J. Alison Noble
    • 1
  1. 1.Institute of Biomedical Engineering, Dept. of Engineering ScienceUniversity of OxfordUK
  2. 2.Nuffield Dept. of Orthopaedics, Rheumatology & Musculoskeletal SciencesUniversity of OxfordUK

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