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Integrated Four Dimensional Registration and Segmentation of Dynamic Renal MR Images

  • Ting Song
  • Vivian S. Lee
  • Henry Rusinek
  • Samson Wong
  • Andrew F. Laine
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)

Abstract

In this paper a novel approach for the registration and segmentation of dynamic contrast enhanced renal MR images is presented. This integrated method is motivated by the observation of the reciprocity between registration and segmentation in 4D time-series images. Fully automated Fourier-based registration with sub-voxel accuracy and semi-automated time-series segmen-tation were intertwined to improve the accuracy in a multi-step fashion. We have tested our algorithm on several real patient data sets. Clinical validation showed remarkable and consistent agreement between the proposed method and manual segmentation by experts.

Keywords

Manual Segmentation Translation Error Gradient Vector Flow Refine Segmentation Kidney Segmentation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ting Song
    • 1
  • Vivian S. Lee
    • 2
  • Henry Rusinek
    • 2
  • Samson Wong
    • 2
  • Andrew F. Laine
    • 1
  1. 1.Department of Biomedical EngineeringColumbia UniversityNew YorkU.S.A.
  2. 2.Department of RadiologyNew York University Medical CenterNew YorkU.S.A.

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