The Experts below are selected from a list of 237 Experts worldwide ranked by ideXlab platform
Sebastien Ourselin - One of the best experts on this subject based on the ideXlab platform.
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Global image registration using a Symmetric block-matching approach
Journal of medical imaging, 2014Co-Authors: Marc Modat, David M Cash, Pankaj Daga, Gawin P Winston, John S Duncan, Sebastien OurselinAbstract: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.
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a Symmetric block matching Framework for global registration
Proceedings of SPIE, 2014Co-Authors: Marc Modat, David M Cash, Pankaj Daga, Gawin P Winston, John S Duncan, Sebastien OurselinAbstract:Most registration algorithms suffer from a directionality bias that has been shown to largely impact on subsequent analyses. Several approaches have been proposed in the literature to address this bias in the context of non-linear registration but little work has been done in the context of global registration. We propose a Symmetric approach based on a block-matching technique and least trimmed square regression. The proposed method is suitable for multi-modal registration and is robust to outliers in the input images. The Symmetric Framework is compared to the original aSymmetric block-matching technique, outperforming it in terms accuracy and robustness.
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Medical Imaging: Image Processing - A Symmetric block-matching Framework for global registration
Proceedings of SPIE, 2014Co-Authors: Marc Modat, David M Cash, Pankaj Daga, Gawin P Winston, John S Duncan, Sebastien OurselinAbstract:Most registration algorithms suffer from a directionality bias that has been shown to largely impact on subsequent analyses. Several approaches have been proposed in the literature to address this bias in the context of non-linear registration but little work has been done in the context of global registration. We propose a Symmetric approach based on a block-matching technique and least trimmed square regression. The proposed method is suitable for multi-modal registration and is robust to outliers in the input images. The Symmetric Framework is compared to the original aSymmetric block-matching technique, outperforming it in terms accuracy and robustness.
Morimitsu Tanimoto - One of the best experts on this subject based on the ideXlab platform.
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Towards the minimal seesaw model via CP violation of neutrinos
Journal of High Energy Physics, 2017Co-Authors: Yusuke Shimizu, Kenta Takagi, Morimitsu TanimotoAbstract:We study the minimal seesaw model, where two right-handed Majorana neutrinos are introduced, focusing on the CP violating phase. In addition, we take the trimaximal mixing pattern for the neutrino flavor where the charged lepton mass matrix is diagonal. Owing to this Symmetric Framework, the 3 × 2 Dirac neutrino mass matrix is given in terms of a few parameters. It is found that the observation of the CP violating phase determines the flavor structure of the Dirac neutrino mass matrix in the minimal seesaw model. New minimal Dirac neutrino mass matrices are presented in the case of TM_1, which is given by the additional 2-3 family mixing to the tri-bimaximal mixing basis in the normal hierarchy of neutrino masses. Our model includes the Littlest seesaw model by King et al. as one of the specific cases. Furthermore, it is remarked that our 3 × 2 Dirac neutrino mass matrix is reproduced by introducing gauge singlet flavons with the specific alignments of the VEV’s. These alignments are derived from the residual symmetry of S_4 group.
Marc Modat - One of the best experts on this subject based on the ideXlab platform.
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Global image registration using a Symmetric block-matching approach
Journal of medical imaging, 2014Co-Authors: Marc Modat, David M Cash, Pankaj Daga, Gawin P Winston, John S Duncan, Sebastien OurselinAbstract: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.
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a Symmetric block matching Framework for global registration
Proceedings of SPIE, 2014Co-Authors: Marc Modat, David M Cash, Pankaj Daga, Gawin P Winston, John S Duncan, Sebastien OurselinAbstract:Most registration algorithms suffer from a directionality bias that has been shown to largely impact on subsequent analyses. Several approaches have been proposed in the literature to address this bias in the context of non-linear registration but little work has been done in the context of global registration. We propose a Symmetric approach based on a block-matching technique and least trimmed square regression. The proposed method is suitable for multi-modal registration and is robust to outliers in the input images. The Symmetric Framework is compared to the original aSymmetric block-matching technique, outperforming it in terms accuracy and robustness.
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Medical Imaging: Image Processing - A Symmetric block-matching Framework for global registration
Proceedings of SPIE, 2014Co-Authors: Marc Modat, David M Cash, Pankaj Daga, Gawin P Winston, John S Duncan, Sebastien OurselinAbstract:Most registration algorithms suffer from a directionality bias that has been shown to largely impact on subsequent analyses. Several approaches have been proposed in the literature to address this bias in the context of non-linear registration but little work has been done in the context of global registration. We propose a Symmetric approach based on a block-matching technique and least trimmed square regression. The proposed method is suitable for multi-modal registration and is robust to outliers in the input images. The Symmetric Framework is compared to the original aSymmetric block-matching technique, outperforming it in terms accuracy and robustness.
David M Cash - One of the best experts on this subject based on the ideXlab platform.
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Global image registration using a Symmetric block-matching approach
Journal of medical imaging, 2014Co-Authors: Marc Modat, David M Cash, Pankaj Daga, Gawin P Winston, John S Duncan, Sebastien OurselinAbstract: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.
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a Symmetric block matching Framework for global registration
Proceedings of SPIE, 2014Co-Authors: Marc Modat, David M Cash, Pankaj Daga, Gawin P Winston, John S Duncan, Sebastien OurselinAbstract:Most registration algorithms suffer from a directionality bias that has been shown to largely impact on subsequent analyses. Several approaches have been proposed in the literature to address this bias in the context of non-linear registration but little work has been done in the context of global registration. We propose a Symmetric approach based on a block-matching technique and least trimmed square regression. The proposed method is suitable for multi-modal registration and is robust to outliers in the input images. The Symmetric Framework is compared to the original aSymmetric block-matching technique, outperforming it in terms accuracy and robustness.
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Medical Imaging: Image Processing - A Symmetric block-matching Framework for global registration
Proceedings of SPIE, 2014Co-Authors: Marc Modat, David M Cash, Pankaj Daga, Gawin P Winston, John S Duncan, Sebastien OurselinAbstract:Most registration algorithms suffer from a directionality bias that has been shown to largely impact on subsequent analyses. Several approaches have been proposed in the literature to address this bias in the context of non-linear registration but little work has been done in the context of global registration. We propose a Symmetric approach based on a block-matching technique and least trimmed square regression. The proposed method is suitable for multi-modal registration and is robust to outliers in the input images. The Symmetric Framework is compared to the original aSymmetric block-matching technique, outperforming it in terms accuracy and robustness.
John S Duncan - One of the best experts on this subject based on the ideXlab platform.
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Global image registration using a Symmetric block-matching approach
Journal of medical imaging, 2014Co-Authors: Marc Modat, David M Cash, Pankaj Daga, Gawin P Winston, John S Duncan, Sebastien OurselinAbstract: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.
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a Symmetric block matching Framework for global registration
Proceedings of SPIE, 2014Co-Authors: Marc Modat, David M Cash, Pankaj Daga, Gawin P Winston, John S Duncan, Sebastien OurselinAbstract:Most registration algorithms suffer from a directionality bias that has been shown to largely impact on subsequent analyses. Several approaches have been proposed in the literature to address this bias in the context of non-linear registration but little work has been done in the context of global registration. We propose a Symmetric approach based on a block-matching technique and least trimmed square regression. The proposed method is suitable for multi-modal registration and is robust to outliers in the input images. The Symmetric Framework is compared to the original aSymmetric block-matching technique, outperforming it in terms accuracy and robustness.
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Medical Imaging: Image Processing - A Symmetric block-matching Framework for global registration
Proceedings of SPIE, 2014Co-Authors: Marc Modat, David M Cash, Pankaj Daga, Gawin P Winston, John S Duncan, Sebastien OurselinAbstract:Most registration algorithms suffer from a directionality bias that has been shown to largely impact on subsequent analyses. Several approaches have been proposed in the literature to address this bias in the context of non-linear registration but little work has been done in the context of global registration. We propose a Symmetric approach based on a block-matching technique and least trimmed square regression. The proposed method is suitable for multi-modal registration and is robust to outliers in the input images. The Symmetric Framework is compared to the original aSymmetric block-matching technique, outperforming it in terms accuracy and robustness.
