Maximum a Posteriori Local Histogram Estimation for Image Registration

  • Matthew Toews
  • D. Louis Collins
  • Tal Arbel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3750)


Image similarity measures for registration can be considered within the general context of joint intensity histograms, which consist of bin count parameters estimated from image intensity samples. Many approaches to estimation are ML (maximum likelihood), which tends to be unstable in the presence sparse data, resulting in registration that is driven by spurious noisy matches instead of valid intensity relationships. We propose instead a method of MAP (maximum a posteriori) estimation, which is well-defined for sparse data, or even in the absence of data. This estimator can incorporate a variety of prior assumptions, such as global histogram characteristics, or use a maximum entropy prior when no such assumptions exist. We apply our estimation method to deformable registration of MR (magnetic resonance) and US (ultrasound) images for an IGNS (image-guided guided neurosurgery) application, where our MAP estimation method results in more stable and accurate registration than a traditional ML approach.


Mutual Information Image Registration Sparse Data Local Histogram Joint Histogram 
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

  • Matthew Toews
    • 1
  • D. Louis Collins
    • 2
  • Tal Arbel
    • 1
  1. 1.Center for Intelligent MachinesMcGill UniversityMontréalCanada
  2. 2.McConnell Brain Imaging Center, Montreal Neurological InstituteMcGill UniversityMontréalCanada

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