matlab rigid registration between two images.

rigid registration is achieved by minimization of the sum of squared intensity differences (SSD) between two images.

Matlab routines

11/2005:

ftp://ftp.cea.fr/pub/dsv/madic/download/INRIAlign/INRIAlign2.zip

• README.txt
• INRIAlign_1.01.zip
• INRIAlign_1.0.zip

Author

Alexis Roche, (during his Phd, at the EPIDAURE Group, INRIA Sophia Antipolis). Alexis is now with the CEA SHFJ, anatomo-fonctional neuro-imaging unit, Orsay, France.

Some References

• L. Freire, A. Roche and J.-Fr. Mangin. What is the best similarity measure for motion correction in fMRI? IEEE Transactions in Medical Imaging, p. 470-484, 2002.
• L. Freire and J.-F. Mangin. Motion correction algorithms may create spurious brain activations in the absence of subject motion. Neuroimage 14(3), p. 709-722, september 2001.
• P.J. Rousseeuw and A.M. Leroy. Robust Regression and Outlier Detection. Wiley Series in Probability and Mathematical Statistics. 1987.

The INRIAlign toolbox enhances the standard SPM realignment routine (see topic: spm_realign_ui in SPM99 documentation). In the latter, rigid registration is achieved by minimization of the sum of squared intensity differences (SSD) between two images. As noted by several SPM users, SSD based registration may be biased by a variety of image artifacts and also by activated areas. To get around this problem, INRIAlign reduces the influence of large intensity differences by weighting errors using a non-quadratic, slowly-increasing function (rho function). This is basically the principle of an M-estimator.