Sparse Bayesian Registration

Abstract : We propose a Sparse Bayesian framework for non-rigid registration. Our principled approach is flexible, in that it efficiently finds an optimal, sparse model to represent deformations among any preset, widely over-complete range of basis functions. It addresses open challenges in state-of-the-art registration, such as the automatic joint estimate of model parameters (e.g. noise and regularization levels). We demonstrate the feasibility and performance of our approach on cine MR, tagged MR and $3$D US cardiac images, and show state-of-the-art results on benchmark data sets evaluating accuracy of motion and strain.
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Polina Golland; Nobuhiko Hata; Christian Barillot; Joachim Hornegger; Robert Howe. MICCAI - 17th International Conference on Medical Image Computing and Computer Assisted Intervention, Sep 2014, Boston, United States. Springer, 8673, pp.235-242, 2014, LNCS - Lecture Notes in Computer Science, Springer. 〈10.1007/978-3-319-10404-1_30〉
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Loic Le Folgoc, Hervé Delingette, Antonio Criminisi, Nicholas Ayache. Sparse Bayesian Registration. Polina Golland; Nobuhiko Hata; Christian Barillot; Joachim Hornegger; Robert Howe. MICCAI - 17th International Conference on Medical Image Computing and Computer Assisted Intervention, Sep 2014, Boston, United States. Springer, 8673, pp.235-242, 2014, LNCS - Lecture Notes in Computer Science, Springer. 〈10.1007/978-3-319-10404-1_30〉. 〈hal-01006605〉

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