One of the main sources of difficulty when comparing anatomical data from a number of subjects is anatomical variability. Even after automatic linear registration [1], there remains a residual non-linear component of spatial mis-registration among brains that is largely due to normal anatomical morphometric variability that can account for a difference in position of up to 1–2cm for the same anatomical landmark between subjects.

We have developed a completely automatic method, based on multi-scale, three dimensional (3D) cross-correlation, to non-linearly register two MRI volumes together, thus reducing the residual mis-registration remaining after linear alignment.

Spatial registration is completed automatically as a two step process. The first [1] accounts for the linear part of the transformation by using correlation between Gaussian-blurred features extracted from both volumes. In the second step, ANIMAL estimates the 3D deformation field [2] required to account for this variability. The deformation field is built by sequentially stepping through the target volume in a 3D grid pattern. At each grid-node i, the deformation vector required to achieve local registration between the two volumes is found by optimization of 3 translational parameters (txi,tyi,tzi) that maximize the objective function evaluated only in the neighbourhood region surrounding the node. The algorithm is applied iteratively in a multi-scale hierarchy, so that image blurring and grid size are reduced after each iteration, thus refining the fit.

A Perl script (nlfit) implements the multi-resolution fitting strategy that has been used to map more than 600 brains into stereotaxic space at the Montreal Neurological Institute. At the heart of this procedure is a new version of minctracc, the program that automatically finds the best non-linear transformation to map one volumetric data set (stored in MINC format) on to another. The program uses optimization over a user selectable number of parameters to identify the best transformation mapping voxel values of the first data set into the second.

Getting Started

Download the package mni-autoreg from


We would appreciate that at least the following articles be referenced to describe the registration method in all publications of data analyzed using the MNI_ANIMAL package:

1 D. L. Collins, P. Neelin, T. M. Peters and A. C. Evans, `Automatic 3D Inter-Subject Registration of MR Volumetric Data in Standardized Talairach Space,′ Journal of Computer Assisted Tomography, 18(2) pp192–205, 1994.

2 D. L. Collins, C. J. Holmes, T. M. Peters, and A. C. Evans, `Automatic 3D model-based neuroanatomical segmentation,′ Human Brain Mapping, vol. 3, no. 3, pp. 190–208, 1995.

Further Information

Good luck! Please contact Louis Collins if anything goes wrong with the installation or testing.


Copyright (C) 1993-2010 Louis Collins and Greg Ward, 
McConnell Brain Imaging Centre, 
Montreal Neurological Institute, 
McGill University. 

Permission to use, copy, modify, and distribute this software and its documentation
for any purpose and without fee is hereby granted, provided that the above
copyright notice appear in all copies. The authors and McGill University
make no representations about the suitability of this software for any purpose. 
It is provided "as is" without express or implied warranty. The authors 
are not responsible for any data loss, equipment damage, property loss, or injury
to subjects or patients resulting from the use or misuse of this softwarepackage. 
The results of any MNI_ANIMAL registration should always  be carefully checked by 
visual inspection in all three spatial dimensions. MNI_ANIMAL registrations should
never be used in a setting where an incorrect result could injure a patient or 
subject or lead to an incorrect diagnosis. This package is provided for research 
use only.

We make no warranty that this package is free of conceptual or 
programming errors. The fact that this package has been provided to
you does not imply or assure any additional support beyond what is
provided in this document. Likewise, it does not guarantee that you
will be notified of any programming errors that are discovered or that
you will be provided with future updated versions of the software. The
authors disclaim all liability for direct or indirect damages resulting
from your use of Montreal Neurological Institute Automated NonLinear Registration