Removal of local and biased global maxima in intensity-based registration

Salvado, Olivier and Wilson, David L. (2007) Removal of local and biased global maxima in intensity-based registration. Medical Image Analysis, 11 2: 183-196. doi:10.1016/j.media.2006.12.001


Author Salvado, Olivier
Wilson, David L.
Title Removal of local and biased global maxima in intensity-based registration
Journal name Medical Image Analysis   Check publisher's open access policy
ISSN 1361-8415
1361-8423
Publication date 2007-04
Year available 2006
Sub-type Article (original research)
DOI 10.1016/j.media.2006.12.001
Volume 11
Issue 2
Start page 183
End page 196
Total pages 14
Place of publication Amsterdam, Netherlands
Publisher Elsevier BV
Language eng
Abstract Intensity based registration (e.g., mutual information) suffers from a scalloping artifact giving rise to local maxima and sometimes a biased global maximum in a similarity objective function. Here, we demonstrate that scalloping is principally due to the noise reduction filtering that occurs when image samples are interpolated. Typically at a much smaller scale (100 times less in our test cases), there are also fluctuations in the similarity objective function due to interpolation of the signal and to sampling of a continuous, band-limited image signal. Focusing on the larger problem from noise, we show that this phenomenon can even bias global maxima, giving inaccurate registrations. This phenomenon is readily seen when one registers an image onto itself with different noise realizations but is absent when the same noise realization is present in both images. For linear interpolation, local maxima and global bias are removed if one filters the interpolated image using a new constant variance filter for linear interpolation (cv-lin filter), which equalizes the variance across the interpolated image. We use 2D synthetic and MR images and characterize the effect of cv-lin on similarity objective functions. With a reduction of local and biased maxima, image registration becomes more robust and accurate. An efficient implementation adds insignificant computation time per iteration, and because optimization proceeds more smoothly, sometimes fewer iterations are needed.
Keyword Registration
Linear interpolation
Variance
MRI
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ
Additional Notes Available online 21 December 2006

 
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