Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-40843-4_6
Title: Hybrid multimodal deformable registration with a data-driven deformation prior
Authors: Lu, Y.
Sun, Y. 
Liao, R.
Ong, S.H. 
Issue Date: 2013
Source: Lu, Y.,Sun, Y.,Liao, R.,Ong, S.H. (2013). Hybrid multimodal deformable registration with a data-driven deformation prior. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8090 LNCS : 45-54. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-40843-4_6
Abstract: Deformable registration for images with different contrast-enhancement and hence different structure appearance is extremely challenging due to the ill-posed nature of the problem. Utilizing prior anatomical knowledge is thus necessary to eliminate implausible deformations. Landmark constraints and statistically constrained models have shown encouraging results. However, these methods do not utilize the segmentation information that may be readily available. In this paper, we explore the possibility of utilizing such information. We propose to generate an anatomical correlation-regularized deformation field prior by registration of point sets using mixture of Gaussians based on a thin-plate spline parametric model. The point sets are extracted from the segmented object surface and no explicit landmark matching is required. The prior is then incorporated with an intensity-based similarity measure in the deformable registration process using the variational framework. The proposed prior does not require any training data set thus excluding any inter-subject variations compared to learning-based methods. In the experiments, we show that our method increases the registration robustness and accuracy on 12 sets of TAVI patient data, 8 myocardial perfusion MRI sequences, and one simulated pre- and post- tumor resection MRI. © 2013 Springer-Verlag.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/70501
ISBN: 9783642408427
ISSN: 03029743
DOI: 10.1007/978-3-642-40843-4_6
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