## Abstract

Summary:

Registration of medical images is an active field of current research.The problem is to find a transformation which aligns two given images. The re-sulting displacement field may be described as a linear combination of pre-selectedbasis functions (parametric approach), or, as in our case, it may be computed as aminimizer of a functional (non-parametric or variational approach). This functionalcombines a similarity measure and a smoothness term. The first one puts the com-parison of the images into quantifiable terms whereas the latter one regularizes thedisplacement field. The minimizing task is tackled by computing the Gâteaux deriv-ative of the functional resulting in a set of nonlinear partial differential equationsfor the displacement field. These equations are linearized by means of a fixed–pointiteration scheme and discretized by a standard finite difference approach.A conventional variational method results in a globally smooth displacementfield. However, a variety of clinical applications involve topology changes betweenthe two images as for instance brain shift or tumor appearance or resection. Forsuch applications a generalization of the standard method is needed which allowsfor localized discontinuities in the displacement field.The variational image registration approach presented here assumes a segmenta-tion of the images into corresponding subdomains. At the interfaces between neigh-bouring subdomains the influence of the smoothness term can be suppressed byintroducing a spatially dependent weighting function. By choosing it appropriatelythis allows for opening or closing of a gap between image regions.We demonstrate the capability of this new registration method by means of aone-dimensional synthetic example and a two-dimensional MR head image. However,our method can be applied to images of arbitrary dimensionality.

Registration of medical images is an active field of current research.The problem is to find a transformation which aligns two given images. The re-sulting displacement field may be described as a linear combination of pre-selectedbasis functions (parametric approach), or, as in our case, it may be computed as aminimizer of a functional (non-parametric or variational approach). This functionalcombines a similarity measure and a smoothness term. The first one puts the com-parison of the images into quantifiable terms whereas the latter one regularizes thedisplacement field. The minimizing task is tackled by computing the Gâteaux deriv-ative of the functional resulting in a set of nonlinear partial differential equationsfor the displacement field. These equations are linearized by means of a fixed–pointiteration scheme and discretized by a standard finite difference approach.A conventional variational method results in a globally smooth displacementfield. However, a variety of clinical applications involve topology changes betweenthe two images as for instance brain shift or tumor appearance or resection. Forsuch applications a generalization of the standard method is needed which allowsfor localized discontinuities in the displacement field.The variational image registration approach presented here assumes a segmenta-tion of the images into corresponding subdomains. At the interfaces between neigh-bouring subdomains the influence of the smoothness term can be suppressed byintroducing a spatially dependent weighting function. By choosing it appropriatelythis allows for opening or closing of a gap between image regions.We demonstrate the capability of this new registration method by means of aone-dimensional synthetic example and a two-dimensional MR head image. However,our method can be applied to images of arbitrary dimensionality.

Originalsprache | Englisch |
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Titel | Image Processing Based on Partial Differential Equations |

Redakteure/-innen | Xue-Cheng Tai, Knut-Andreas Lie, Tony F. Chan, Stanley Osher |

Seitenumfang | 15 |

Erscheinungsort | Berlin, Heidelberg |

Herausgeber (Verlag) | Springer Berlin Heidelberg |

Erscheinungsdatum | 2007 |

Seiten | 363-377 |

ISBN (Print) | 978-3-540-33266-4 |

ISBN (elektronisch) | 978-3-540-33267-1 |

DOIs | |

Publikationsstatus | Veröffentlicht - 2007 |

Veranstaltung | International Conference on PDE-Based Image Processing and Related Inverse Problems 2005 - CMA, Norwegen Dauer: 08.08.2005 → 12.08.2005 |