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Unsupervised detection of local errors in image registration

Valeriy Vishnevskiy, Tobias Gass, Gabor Székely, Christine Tanner, Orcun Goksel
2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)
New York, USA, April 2015


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.

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  author = {Valeriy Vishnevskiy and Tobias Gass and Gabor Székely and Christine Tanner and Orcun Goksel},
  title = { Unsupervised detection of local errors in image registration},
  booktitle = {2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)},
  year = {2015},
  month = {April},
  pages = {841-844},
  publisher = {IEEE},
  keywords = {biomedical MRI;image registration;liver;medical image processing;pneumodynamics;unsupervised learning;3D liver MRI registration;breathing phases;clinical applications;consistency error;image features;image registration;local errors;local registration error detection;medical imaging;registration accuracy;registration circuits;unsupervised detection;user-specified magnitude;visual assessment;Accuracy;Estimation;Image registration;Liver;Magnetic resonance imaging;Mathematical model;Three-dimensional displays;Registration accuracy;consistency;error detection;registration circuits}