OIV.ImageViz.Engines.GeometryAndMatching.Registration.SoImageRegistrationTransform computes the best transformation for the co-registration of two images, using an iterative optimization algorithm.

The goal of registration is to find a transformation aligning a model image, which is moving while being processed, with a reference image, which remains fixed, starting from an initial transformation and by optimizing a similarity criterion between both images.

The estimated transformation can be a single translation, rigid (translation and rotation only), rigid with scale factors (isotropic or anisotropic along axis directions) or affine (including shear transformation).

The optimizer behavior depends on the optimizerStep parameter which affects the search extent, precision and computation time. A small optimizerStep is recommended when a pre-alignment has been performed in order to be more precise and avoid sending the transformation at a wrong location.

Two different optimization strategies are used for coarsest and finest resolution levels. The Extensive Direction optimizer is used at coarse levels. This optimizer is well suited for coarse resolution levels and potentially search registration further. A Quasi Newton optimizer is used on the finest level computed excepted if there is only one level. This optimizer is more suited for finer resolution levels in order to refine the transformation.

By default, the coarsestResampling and optimizerStep parameters are automatically estimated from the reference image properties. If the model and reference have different resolution or size, for instance in multi-modality case, these settings may be inappropriate and lead the registration to fail. In this case, the autoParameterMode parameter should be set to false and both parameters should be manually set to relevant values so that the coarsest resolution level generates a representative volume (i.e., not made of too few voxels) the displacement step is precise enough to not skip the searched transformation.

The OIV.ImageViz.Engines.GeometryAndMatching.Registration.SoImagePreAlignmentTransform3d engine can be used beforehand to estimate a rough initial transformation.

If the two input images have been carefully pre-aligned, it is not recommended to perform the registration at a too low sub-resolution level. It would not only perform useless computations but could also send the transformation at a wrong location and thus miss the right transformation. Consequently, the following recommendations can be applied in this case:

• Do not use automatic parameters, i.e., set autoParameterMode to false.

• Set the coarsest resolution level at the half of the original resolution, i.e., set the coarsestResampling parameter to (2,2,2).

• Set the optimizerStep parameter with half of the reference image voxel size for finest resolution and reference voxel size for coarsest resolution.

This engine can notify some information during the processing (progression, similarity) and can be interrupted. Intercepting these events slows down the algorithm execution.

References

The Correlation Ratio metric is explained in the following publication:

• A. Roche, G. Malandain, X. Pennec, and N. Ayache. "The Correlation Ratio as a New Similarity Measure for Multimodal Image Registration". INRIA Sophia Antipolis, EPIDAURE project, 1998.

The Normalized Mutual Information metric is based on the following publication:

• C. Studholme, D. L. G. Hill, D.J. Hawkes. "An Overlap Invariant Entropy Measure of 3D Medical Image Alignment". In: Pattern Recognition vol. 32, pp. 71-86, 1999.

Further references include:

• P. A. Viola. "Alignment by Maximization of Mutual Information". Massachusetts Institute of Technology, Diss., 1995.

• A. Collignon, F. Maes, D. Delaere, D. Vandermeulen, P. Suetens, G. Marchal. "Automated Multi-modality Image Registration Based on Information Theory". In: IPMI. Dordrecht, Niederlande: Kluwer Academics, pp. 263-274, 1995.

FILE FORMAT/DEFAULT

ImageRegistrationTransform3d {

inMovingImage	NULL
inFixedImage	NULL
initialTransform	SbMatrix.identity()
autoParameterMode	true
optimizerStep	OIV.Inventor.SbVec2f( 4.0f, 1.0f / 2.0f )
intensityRangeReference	OIV.Inventor.SbVec2f( 0.0f, 65535.0f )
intensityRangeModel	OIV.Inventor.SbVec2f( 0.0f, 65535.0f )
rangeModeReference	MIN_MAX
rangeModeModel	MIN_MAX
coarsestResampling	SbVec3i32( 8, 8, 8 )
transformType	RIGID
ignoreFinestLevel	false
metricType	CORRELATION

}

Notice: This engine requires to preliminarily load the whole input data sets into memory to be computed. As a consequence, the maximum memory parameter must be either set to 0 or greater than the data set memory size:

maxMemory parameter of the OIV.ImageViz.SbImageDataAdapterHelper.GetAppropriateAdapter(System.String, System.UInt64) function

maxMemory attribute of the OIV.ImageViz.Nodes.Images.SoFileDataAdapter class

If this condition is not respected an exception will be raised when launching the execution of this engine: "engine cannot be computed because inputs are not in memory images."

If the input data sets cannot fit in memory, this engine will fail during its computation.

Reference