Year: 2014
Type: article
Abstract: Most medical image registration algorithms suffer from a directionality bias that has been shown to largely impact subsequent analyses. Several approaches have been proposed in the literature to address this bias in the context of nonlinear registration, but little work has been done for global registration. We propose a symmetric approach based on a block-matching technique and least-trimmed square regression. The proposed method is suitable for multimodal registration and is robust to outliers in the input images. The symmetric framework is compared with the original asymmetric block-matching technique and is shown to outperform it in terms of accuracy and robustness. The methodology presented in this article has been made available to the community as part of the NiftyReg open-source package.
Source: Journal of medical imaging
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funders: Wellcome Trust, National Institute for Health and Care Research, Brain Research Trust, University College London, Wolfson Foundation, University College London Hospitals NHS Foundation Trust, Medical Research Council, Engineering and Physical Sciences Research Council, FP7 Information and Communication Technologies
Awards: G1074010309, G150562299, G1737639950, G1775974636, G2167080140, Intelligent Imaging: Motion, Form and Function Across Scale, G4065409414, Computational models of neurodegenerative disease progression, G4421367155, NIHR Senior Investigator Award, G491565163, G6496510108, G740742673, G8642393552, EPICure@19 - the extremely preterm young adult
OpenAlex ID: W2026752633