Fully automated biomedical image segmentation by self-organized model adaptation

@article{Wismller2004FullyAB,
  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},
  year={2004},
  volume={17 8-9},
  pages={1327-44}
}
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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Segmentation with neural networks

  • A. Wismüller, F. Vietze, D. R. Dersch
  • Handbook of medical imaging,
  • 2000
Highly Influential
11 Excerpts

Eigenschaften neuronaler Vektorquantisierer. Doktorarbeit, Sektion Physik. München: Ludwig-MaximiliansUniversität

  • D. R. Dersch
  • 1995
Highly Influential
6 Excerpts

Co-planar stereotaxic atlas of the human brain. Stuttgart: Thieme

  • J. Talairach, P. Tournoux
  • 1988
Highly Influential
3 Excerpts

Overview and fundamentals of medical image segmentation

  • J. Rogowska
  • Handbook of medical imaging
  • 2000
2 Excerpts

The deformable feature map—Adaptive plasticity in function approximation

  • A. Wismüller, F. Vietze, D. R. Dersch, K. Hahn
  • 1998

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