Description of Registration Method for Asad

We optimize a convex function combining the Mutual Information and the average inter-feature Euclidean distance. The weights in the The convex function are determined automatically by minimizing the average inter-feature Euclidean distance and finding the ratio of the MI change with respect to the average Euclidean distance change. The geometrical features correspondences used are point-to-point. In the model (floating) data set, we perfrom 3D edgel extraction and linking followed by triangulation to construct a surface-based model. In the object (reference) data set, we extract object-control-points (OCPs) in the vicinity of a transformed subset of the model points. The average Euclidean distance minimized is the average distance between the extracted OCPs and the corresponding closest points on the surface-based model.

The optimization of the convex function is done using Powell's search algorithm. The model construction, OCPs extraction, and registration are fully automatic.

As a preprocessing step, we add new slices to make the data sets isotropic and then sub-sample them to reduce the number of voxels to be processed (e.g., an MRI-T1 data set with 256x256x26 voxels and voxel sizes of 1.25mmx1.25mmx4.0mm is converted into a 128x128x42 data set with voxel sizes of 2.5mmx2.5mmx2.476mm).

References:

1) Asad Abu-Tarif and George Nagy, "Multi-Modal 3D Registration: A Comparative Study." submitted for publication in ICPR2002

2) W. Wells III, P. Viola, H. Atsumi, S. Nakajima, and R. Kikinis, "Multi-Modal volume registration by Maximization of Mutual Information." Medical Image Analysis, Vol. 1, No. 1, 1996.

3) F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, and P. Suetens, "Multi-Modality Image Registration By Maximization Of Mutual Information." IEEE Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis, June 1996.

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