A non-linear gray-level appearance model improves active shape model segmentation

@inproceedings{Ginneken2001ANG,
  title={A non-linear gray-level appearance model improves active shape model segmentation},
  author={Bram van Ginneken and Alejandro F. Frangi and Joes Staal and Bart M. ter Haar Romeny and Max A. Viergever},
  year={2001}
}
Active Shape Models (ASMs), a knowledge-based segmentation algorithm developed by Cootes and Taylor [1, 2], have become a standard and popular method for detecting structures in medical images. In ASMs – and various comparable approaches – the model of the object’s shape and of its gray-level variations is based the assumption of linear distributions. In this work, we explore a new way to model the gray-level appearance of the objects, using a k-nearest-neighbors ( kNN ) classifier and a set of… CONTINUE READING
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