Detalhes do Documento

3D-2D image registration by nonlinear regression

Autor(es): Gouveia, A. R. ; Metz, C. ; Freire, Luís ; Klein, S.

Data: 2012

Identificador Persistente: http://hdl.handle.net/10400.21/3029

Origem: Repositório Científico do Instituto Politécnico de Lisboa

Assunto(s): 2D/3D Image registration; Image guided interventions; Regression; Feature extraction; Image registration; Neural networks; Optimization; Robustness; Training; X-ray imaging; 2D/3D Image registration; 2D/3D Image registration; Image guided interventions; Image guided interventions; Regression; Regression; Feature extraction; Feature extraction; Image registration; Image registration; Neural networks; Neural networks; Optimization; Optimization; Robustness; Robustness; Training; Training; X-ray imaging; X-ray imaging


Descrição

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.

Tipo de Documento Capitulo
Idioma Inglês
Contribuidor(es) RCIPL
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