Segmentation with Incremental Classifiers

  • Guillaume Bernard
  • Michel Verleysen
  • John A. Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8157)


Radiotherapy treatment planning requires physicians to delineate the target volumes and organs at risk on 3D images of the patient. This segmentation task consumes a lot of time and can be partly automated with atlases (reference images segmented by experts). To segment any new image, the atlas is non-rigidly registered and the organ contours are then transferred. In practice, this approach suffers from the current limitations of non-rigid registration. We propose an alternative approach to extract and encode the physician’s expertise. It relies on a specific classification method that incrementally extracts information from groups of pixels in the images. The incremental nature of the process allows us to extract features that depend on partial classification results but also convey richer information. This paper is a first investigation of such an incremental scheme, illustrated with experiments on artificial images.


Random Forest Class Label Active Contour Feature Ranking Radiotherapy Treatment Planning 
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 2013

Authors and Affiliations

  • Guillaume Bernard
    • 1
    • 2
  • Michel Verleysen
    • 2
  • John A. Lee
    • 1
    • 2
  1. 1.IRECMolecular Imaging, Radiotherapy, and OncologyBelgium
  2. 2.Machine Learning Group – ICTEAMUniversité catholique de LouvainBelgium

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