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Unsupervised Detection of Local Errors in Image Registration

Valery Vishnevsky, Tobias Gass, Gabor Székely, Christine Tanner, Orcun Goksel
IEEE Int Symp Biomedical Imaging (ISBI)
New York, USA, April 2015

Abstract

Image registration is used extensively in medical imaging. Visual assessment of its quality is time consuming and not necessarily accurate. Automatic estimation of registration accuracy is desired for many clinical applications. Current methods rely on learning a relationship between image features and registration error. In this paper we propose an unsupervised method for the detection of local registration errors of a user-specified magnitude. Our method analyses the consistency error of registration circuits, does not require image intensity information, and achieves an error detection accuracy of 82% for 3D liver MRI registration of breathing phases.

Link to publisher's page
@InProceedings{eth_biwi_01257,
  author = {Valery Vishnevsky and Tobias Gass and Gabor Székely and Christine Tanner and Orcun Goksel},
  title = {Unsupervised Detection of Local Errors in Image Registration},
  booktitle = {IEEE Int Symp Biomedical Imaging (ISBI)},
  year = {2015},
  month = {April},
  pages = {841-844},
  keywords = {}
}