A New Coarse-to-Fine Framework for 3D Brain MR Image Registration

  • Terrence Chen
  • Thomas S. Huang
  • Wotao Yin
  • Xiang Sean Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3765)


Registration, that is, the alignment of multiple images, has been one of the most challenging problems in the field of computer vision. It also serves as an important role in biomedical image analysis and its applications. Although various methods have been proposed for solving different kinds of registration problems in computer vision, the results are still far from ideal when it comes to real world biomedical image applications. For instance, in order to register 3D brain MR images, current state of the art registration methods use a multi-resolution coarse-to-fine algorithm, which typically involves starting with low resolution images and working progressively through to higher resolutions, with the aim to avoid the local maximum "traps". However, these methods do not always successfully avoid the local maximum. Consequently, various rather sophisticated optimization methods are developed to attack this problem. In this paper, we propose a novel viewpoint on the coarse-to-fine registration, in which coarse and fine images are distinguished by different scales of the objects instead of different resolutions of the images. Based on this new perspective, we develop a new image registration framework by combining the multi-resolution method with novel multi-scale algorithm, which could achieve higher accuracy and robustness on 3D brain MR images. We believe this work has great contribution to biomedical image analysis and related applications.


Reference Image Image Registration Registration Method Contour Image Voxel Dimension 
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 2005

Authors and Affiliations

  • Terrence Chen
    • 1
  • Thomas S. Huang
    • 1
  • Wotao Yin
    • 2
  • Xiang Sean Zhou
    • 3
  1. 1.University of Illinois at Urbana ChampaignUrbanaUSA
  2. 2.Columbia UniversityNew YorkUSA
  3. 3.Siemens Corporate ResearchPrincetonUSA

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