Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.21/3029
Título: 3D-2D image registration by nonlinear regression
Autor: Gouveia, A. R.
Metz, C.
Freire, Luís
Klein, S.
Palavras-chave: 2D/3D Image registration
Image guided interventions
Regression
Feature extraction
Image registration
Neural networks
Optimization
Robustness
Training
X-ray imaging
Data: 2012
Editora: IEEE
Citação: Gouveia AR, Metz C, Freire L, Klein S. 3D-2D image registration by nonlinear regression. In 9th IEEE International Symposium on Biomedical Imaging (ISBI). IEEE; 2012. p. 1343-6.
Resumo: We propose a 3D-2D image registration method that relates image features of 2D projection images to the transformation parameters of the 3D image by nonlinear regression. The method is compared with a conventional registration method based on iterative optimization. For evaluation, simulated X-ray images (DRRs) were generated from coronary artery tree models derived from 3D CTA scans. Registration of nine vessel trees was performed, and the alignment quality was measured by the mean target registration error (mTRE). The regression approach was shown to be slightly less accurate, but much more robust than the method based on an iterative optimization approach.
Peer review: yes
URI: http://hdl.handle.net/10400.21/3029
ISBN: 978-1-4577-1857-1
Versão do Editor: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6235814&queryText%3D3D-2D+image+registration+by+nonlinear+regression
Aparece nas colecções:ESTeSL - Capítulos ou partes de livros

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