Fully automated biomedical image segmentation by self-organized model adaptation

  title={Fully automated biomedical image segmentation by self-organized model adaptation},
  author={Axel Wism{\"u}ller and Frank Vietze and Johannes Behrends and Anke Meyer-B{\"a}se and Maximilian Reiser and Helge J. Ritter},
  journal={Neural networks : the official journal of the International Neural Network Society},
  volume={17 8-9},
In this paper, we present a fully automated image segmentation method based on an algorithm that provides adaptive plasticity in function approximation problems: the deformable (feature) map (DM) algorithm. The DM approach reduces a class of similar function approximation problems to the explicit supervised one-shot training of a single data set. This is followed by a subsequent, appropriate similarity transformation, which is based on a self-organized deformation of the underlying… CONTINUE READING
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