Feature-Based Alignment of Volumetric Multi-modal Images

  • Matthew Toews
  • Lilla Zöllei
  • William M. Wells
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7917)


This paper proposes a method for aligning image volumes acquired from different imaging modalities (e.g. MR, CT) based on 3D scale-invariant image features. A novel method for encoding invariant feature geometry and appearance is developed, based on the assumption of locally linear intensity relationships, providing a solution to poor repeatability of feature detection in different image modalities. The encoding method is incorporated into a probabilistic feature-based model for multi-modal image alignment. The model parameters are estimated via a group-wise alignment algorithm, that iteratively alternates between estimating a feature-based model from feature data, then realigning feature data to the model, converging to a stable alignment solution with few pre-processing or pre-alignment requirements. The resulting model can be used to align multi-modal image data with the benefits of invariant feature correspondence: globally optimal solutions, high efficiency and low memory usage. The method is tested on the difficult RIRE data set of CT, T1, T2, PD and MP-RAGE brain images of subjects exhibiting significant inter-subject variability due to pathology.


Multimodal Image Intensity Inversion Multimodal Image Registration Groupwise Registration Alignment Solution 
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 2013

Authors and Affiliations

  • Matthew Toews
    • 1
  • Lilla Zöllei
    • 2
  • William M. Wells
    • 1
  1. 1.Brigham and Women’s HospitalHarvard Medical SchoolUSA
  2. 2.A.A. Martinos Center, Massachussetts General HospitalHarvard Medical SchoolUSA

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