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On Removing Interpolation and Resampling Artifacts in Rigid Image Registration

Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.
IEEE Engineering in Medicine and Biology Society
Publication Date:
IEEE Trans Image Process
Volume Number:
Issue Number:
IEEE Trans Image Process. 2013 Feb;22(2):816-27.
PubMed ID:
Aliasing, Image Registration, Image Resampling, Interpolation Artifact
Appears in Collections:
R01 AG022381/AG/NIA NIH HHS/United States
RC1 AT005728/AT/NCCAM NIH HHS/United States
K25 EB013649/EB/NIBIB NIH HHS/United States
KL2 RR025757/RR/NCRR NIH HHS/United States
P41 RR006009/RR/NCRR NIH HHS/United States
P41 RR014075/RR/NCRR NIH HHS/United States
R01 EB006758/EB/NIBIB NIH HHS/United States
R01 NS052585/NS/NINDS NIH HHS/United States
R01 NS070963/NS/NINDS NIH HHS/United States
R01 RR016594/RR/NCRR NIH HHS/United States
R21 NS072652/NS/NINDS NIH HHS/United States
S10 RR019307/RR/NCRR NIH HHS/United States
S10 RR023043/RR/NCRR NIH HHS/United States
S10 RR023401/RR/NCRR NIH HHS/United States
U01 MH093765/MH/NIMH NIH HHS/United States
U24 RR021382/RR/NCRR NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Aganj I., Yeo B.T.T., Sabuncu M.R., Fischl B. On Removing Interpolation and Resampling Artifacts in Rigid Image Registration. IEEE Trans Image Process. 2013 Feb;22(2):816-27. PMID: 23076044. PMCID: PMC3694571.
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We show that image registration using conventional interpolation and summation approximations of continuous integrals can generally fail because of resampling artifacts. These artifacts negatively affect the accuracy of registration by producing local optima, altering the gradient, shifting the global optimum, and making rigid registration asymmetric. In this paper, after an extensive literature review, we demonstrate the causes of the artifacts by comparing inclusion and avoidance of resampling analytically. We show the sum-of-squared-differences cost function formulated as an integral to be more accurate compared with its traditional sum form in a simple case of image registration. We then discuss aliasing that occurs in rotation, which is due to the fact that an image represented in the Cartesian grid is sampled with different rates in different directions, and propose the use of oscillatory isotropic interpolation kernels, which allow better recovery of true global optima by overcoming this type of aliasing. Through our experiments on brain, fingerprint, and white noise images, we illustrate the superior performance of the integral registration cost function in both the Cartesian and spherical coordinates, and also validate the introduced radial interpolation kernel by demonstrating the improvement in registration.

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