The Experts below are selected from a list of 105 Experts worldwide ranked by ideXlab platform

James S Duncan - One of the best experts on this subject based on the ideXlab platform.

  • integrated intensity and Point feature nonrigid registration
    Medical Image Computing and Computer-Assisted Intervention, 2001
    Co-Authors: Xenophon Papademetris, Andrea P Jackowski, Robert T Schultz, Lawrence H Staib, James S Duncan
    Abstract:

    In this work, we present a method for the integration of fea- ture and intensity information for non rigid registration. Our method is based on a free-form deformation model, and uses a normalized mu- tual information intensity similarity metric to match intensities and the robust Point matching framework to estimate feature (Point) correspon- dences. The intensity and feature components of the registration are posed in a single energy functional with associated weights. We com- pare our method to both Point-based and intensity-based registrations. In particular, we evaluate registration accuracy as measured by Point Landmark distances and image intensity similarity on a set of seventeen normal subjects. These results suggest that the integration of intensity and Point-based registration is highly effective in yielding more accurate registrations.

  • MICCAI (1) - Integrated Intensity and Point-Feature Nonrigid Registration.
    Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Inte, 2001
    Co-Authors: Xenophon Papademetris, Andrea P Jackowski, Robert T Schultz, Lawrence H Staib, James S Duncan
    Abstract:

    In this work, we present a method for the integration of fea- ture and intensity information for non rigid registration. Our method is based on a free-form deformation model, and uses a normalized mu- tual information intensity similarity metric to match intensities and the robust Point matching framework to estimate feature (Point) correspon- dences. The intensity and feature components of the registration are posed in a single energy functional with associated weights. We com- pare our method to both Point-based and intensity-based registrations. In particular, we evaluate registration accuracy as measured by Point Landmark distances and image intensity similarity on a set of seventeen normal subjects. These results suggest that the integration of intensity and Point-based registration is highly effective in yielding more accurate registrations.

Horst Bischof - One of the best experts on this subject based on the ideXlab platform.

  • automatic Point Landmark matching for regularizing nonlinear intensity registration application to thoracic ct images
    Medical Image Computing and Computer-Assisted Intervention, 2006
    Co-Authors: Martin Urschler, Christopher Zach, Hendrik Ditt, Horst Bischof
    Abstract:

    Nonlinear image registration is a prerequisite for a variety of medical image analysis tasks. A frequently used registration method is based on manually or automatically derived Point Landmarks leading to a sparse displacement field which is densified in a thin-plate spline (TPS) framework. A large problem of TPS interpolation/approximation is the requirement for evenly distributed Landmark correspondences over the data set which can rarely be guaranteed by Landmark matching algorithms. We propose to overcome this problem by combining the sparse correspondences with intensity-based registration in a generic nonlinear registration scheme based on the calculus of variations. Missing Landmark information is compensated by a stronger intensity term, thus combining the strengths of both approaches. An explicit formulation of the generic framework is derived that constrains an intra-modality intensity data term with a regularization term from the corresponding Landmarks and an anisotropic image-driven displacement regularization term. An evaluation of this algorithm is performed comparing it to an intensity- and a Landmark-based method. Results on four synthetically deformed and four clinical thorax CT data sets at different breathing states are shown.

  • MICCAI (2) - Automatic Point Landmark matching for regularizing nonlinear intensity registration: application to thoracic CT images
    Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Inte, 2006
    Co-Authors: Martin Urschler, Christopher Zach, Hendrik Ditt, Horst Bischof
    Abstract:

    Nonlinear image registration is a prerequisite for a variety of medical image analysis tasks. A frequently used registration method is based on manually or automatically derived Point Landmarks leading to a sparse displacement field which is densified in a thin-plate spline (TPS) framework. A large problem of TPS interpolation/approximation is the requirement for evenly distributed Landmark correspondences over the data set which can rarely be guaranteed by Landmark matching algorithms. We propose to overcome this problem by combining the sparse correspondences with intensity-based registration in a generic nonlinear registration scheme based on the calculus of variations. Missing Landmark information is compensated by a stronger intensity term, thus combining the strengths of both approaches. An explicit formulation of the generic framework is derived that constrains an intra-modality intensity data term with a regularization term from the corresponding Landmarks and an anisotropic image-driven displacement regularization term. An evaluation of this algorithm is performed comparing it to an intensity- and a Landmark-based method. Results on four synthetically deformed and four clinical thorax CT data sets at different breathing states are shown.

Xenophon Papademetris - One of the best experts on this subject based on the ideXlab platform.

  • integrated intensity and Point feature nonrigid registration
    Medical Image Computing and Computer-Assisted Intervention, 2001
    Co-Authors: Xenophon Papademetris, Andrea P Jackowski, Robert T Schultz, Lawrence H Staib, James S Duncan
    Abstract:

    In this work, we present a method for the integration of fea- ture and intensity information for non rigid registration. Our method is based on a free-form deformation model, and uses a normalized mu- tual information intensity similarity metric to match intensities and the robust Point matching framework to estimate feature (Point) correspon- dences. The intensity and feature components of the registration are posed in a single energy functional with associated weights. We com- pare our method to both Point-based and intensity-based registrations. In particular, we evaluate registration accuracy as measured by Point Landmark distances and image intensity similarity on a set of seventeen normal subjects. These results suggest that the integration of intensity and Point-based registration is highly effective in yielding more accurate registrations.

  • MICCAI (1) - Integrated Intensity and Point-Feature Nonrigid Registration.
    Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Inte, 2001
    Co-Authors: Xenophon Papademetris, Andrea P Jackowski, Robert T Schultz, Lawrence H Staib, James S Duncan
    Abstract:

    In this work, we present a method for the integration of fea- ture and intensity information for non rigid registration. Our method is based on a free-form deformation model, and uses a normalized mu- tual information intensity similarity metric to match intensities and the robust Point matching framework to estimate feature (Point) correspon- dences. The intensity and feature components of the registration are posed in a single energy functional with associated weights. We com- pare our method to both Point-based and intensity-based registrations. In particular, we evaluate registration accuracy as measured by Point Landmark distances and image intensity similarity on a set of seventeen normal subjects. These results suggest that the integration of intensity and Point-based registration is highly effective in yielding more accurate registrations.

Martin Urschler - One of the best experts on this subject based on the ideXlab platform.

  • automatic Point Landmark matching for regularizing nonlinear intensity registration application to thoracic ct images
    Medical Image Computing and Computer-Assisted Intervention, 2006
    Co-Authors: Martin Urschler, Christopher Zach, Hendrik Ditt, Horst Bischof
    Abstract:

    Nonlinear image registration is a prerequisite for a variety of medical image analysis tasks. A frequently used registration method is based on manually or automatically derived Point Landmarks leading to a sparse displacement field which is densified in a thin-plate spline (TPS) framework. A large problem of TPS interpolation/approximation is the requirement for evenly distributed Landmark correspondences over the data set which can rarely be guaranteed by Landmark matching algorithms. We propose to overcome this problem by combining the sparse correspondences with intensity-based registration in a generic nonlinear registration scheme based on the calculus of variations. Missing Landmark information is compensated by a stronger intensity term, thus combining the strengths of both approaches. An explicit formulation of the generic framework is derived that constrains an intra-modality intensity data term with a regularization term from the corresponding Landmarks and an anisotropic image-driven displacement regularization term. An evaluation of this algorithm is performed comparing it to an intensity- and a Landmark-based method. Results on four synthetically deformed and four clinical thorax CT data sets at different breathing states are shown.

  • MICCAI (2) - Automatic Point Landmark matching for regularizing nonlinear intensity registration: application to thoracic CT images
    Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Inte, 2006
    Co-Authors: Martin Urschler, Christopher Zach, Hendrik Ditt, Horst Bischof
    Abstract:

    Nonlinear image registration is a prerequisite for a variety of medical image analysis tasks. A frequently used registration method is based on manually or automatically derived Point Landmarks leading to a sparse displacement field which is densified in a thin-plate spline (TPS) framework. A large problem of TPS interpolation/approximation is the requirement for evenly distributed Landmark correspondences over the data set which can rarely be guaranteed by Landmark matching algorithms. We propose to overcome this problem by combining the sparse correspondences with intensity-based registration in a generic nonlinear registration scheme based on the calculus of variations. Missing Landmark information is compensated by a stronger intensity term, thus combining the strengths of both approaches. An explicit formulation of the generic framework is derived that constrains an intra-modality intensity data term with a regularization term from the corresponding Landmarks and an anisotropic image-driven displacement regularization term. An evaluation of this algorithm is performed comparing it to an intensity- and a Landmark-based method. Results on four synthetically deformed and four clinical thorax CT data sets at different breathing states are shown.

Robert T Schultz - One of the best experts on this subject based on the ideXlab platform.

  • integrated intensity and Point feature nonrigid registration
    Medical Image Computing and Computer-Assisted Intervention, 2001
    Co-Authors: Xenophon Papademetris, Andrea P Jackowski, Robert T Schultz, Lawrence H Staib, James S Duncan
    Abstract:

    In this work, we present a method for the integration of fea- ture and intensity information for non rigid registration. Our method is based on a free-form deformation model, and uses a normalized mu- tual information intensity similarity metric to match intensities and the robust Point matching framework to estimate feature (Point) correspon- dences. The intensity and feature components of the registration are posed in a single energy functional with associated weights. We com- pare our method to both Point-based and intensity-based registrations. In particular, we evaluate registration accuracy as measured by Point Landmark distances and image intensity similarity on a set of seventeen normal subjects. These results suggest that the integration of intensity and Point-based registration is highly effective in yielding more accurate registrations.

  • MICCAI (1) - Integrated Intensity and Point-Feature Nonrigid Registration.
    Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Inte, 2001
    Co-Authors: Xenophon Papademetris, Andrea P Jackowski, Robert T Schultz, Lawrence H Staib, James S Duncan
    Abstract:

    In this work, we present a method for the integration of fea- ture and intensity information for non rigid registration. Our method is based on a free-form deformation model, and uses a normalized mu- tual information intensity similarity metric to match intensities and the robust Point matching framework to estimate feature (Point) correspon- dences. The intensity and feature components of the registration are posed in a single energy functional with associated weights. We com- pare our method to both Point-based and intensity-based registrations. In particular, we evaluate registration accuracy as measured by Point Landmark distances and image intensity similarity on a set of seventeen normal subjects. These results suggest that the integration of intensity and Point-based registration is highly effective in yielding more accurate registrations.