Clinical Image Processing Department

AIR**

COCGVmi**

Intramodel Registration**

mpr/geomcal**

VIRE**

WAIR**

(*=shareware)
(**=freeware)

Name:	AIR
NIH Availability:	Free
Author:	Roger Woods
NIH Contact Person:	None
Supported Platforms:	Unix, PC or Macintosh based machines, (Source Available)
Brief Description:	The Automated Image Registration (AIR) package is primarily designed to solve several different registration problems that arise in tomographic data sets: intrasubject, intramodality registration (e.g., PET to PET or MRI to MRI) intrasubject, intermodality registration (e.g., PET to MRI) intersubject registration (e.g. subject to subject or subject to atlas template) Intrasubject registration of brain images uses a rigid-body model. Intermodality registration has been validated for some (MRI-PET), but not all modalities. Intersubject registration can be performed using any of a variety of linear or nonlinear models to register different subjects to one another or to an atlas template (for example, an averaged brain in "Talairach space"). These models may also be useful for intrasubject registration of organs that are more deformable than brain or for tracking intrasubject developmental changes over time. In addition to 3D models, AIR 3.0 includes homologous 2D deformation models that may be useful in selected circumstances (e.g., single slice fMRI data, pure 2D images, etc.). Once the registration parameters are known, the AIR package allows the data to be resampled to generate a final image using any of the following interpolation models: nearest neighbor trilinear windowed sinc mixed linear/windowed sinc unwindowed sinc chirp-z mixed linear/chirp-z
For more Information:	• AIR home page (disclaimer)

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Name:	COCGVmi
NIH Availability:	Free
Author:
NIH Contact Person:
Supported Platforms:	Unix based machines, (Source Available)
Brief Description:	Performs an initial registration using a Correspondence of Closest Features algorithm and a final registration using a Mutual Information based algorithm. Many I/O options. Extremely robust.
For more Information:	• COCGVmi Home Page (disclaimer) • Levin R, Frank J, DeCarli C. "Correspondence of Closest Edge Voxels -- a robust registration algorithm", Journal of MRI, vol. 7, no. 2, pages 410-415, March/April 1997.

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Name:	IntraModal Registration
NIH Availability:	Free
Author:	Philippe Thévenaz
NIH Contact Person:	None
Supported Platforms:	All (ANSI-C source available)
Brief Description:	This routine performs the registration (alignment) of two images or of two volumes. An optional binary mask can be specified to help the algorithm focus on a region of interest. The criterion is least-squares. The geometric deformation model can be translational, rotational, and affine. The interpolation model is based on splines, with the possibility for performing bi-, tri-linear spline interpolation and bi-, tri-cubic as well. The algorithm is based on a multi-resolution strategy for robustness, and on a Marquardt-Levenberg-like optimizer for speed.
For more Information:	• Intramodal Registration Home Page(disclaimer) • École Polytechnique Fédérale de Lausanne, Biomedical Imaging Group(disclaimer) P. Thévenaz, U.E. Ruttimann, M. Unser, "A Pyramid Approach to Subpixel Registration Based on Intensity," IEEE Transactions on Image Processing, vol. 7, no. 1, pp. 27-41, January 1998. P. Thévenaz, U.E. Ruttimann, M. Unser, "Iterative Multi-Scale Registration Without Landmarks," Proceedings of the 1995 IEEE International Conference on Image Processing (ICIP'95), Washington DC, USA, October 23-26, 1995, vol. III, pp. 228-231. M. Unser, A. Aldroubi, C.R. Gerfen, "A Multiresolution Image Registration Procedure Using Spline Pyramids," Proceedings of the SPIE Conference on Mathematical Imaging: Wavelet Applications in Signal and Image Processing, San Diego CA, USA, July 15-16, 1993, vol. 2034, pp. 160-170.

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Name:	mpr/geomcal
NIH Availability:	Free
Author:	The Computational Imaging Science Group which is a section of the Division of Radiological Sciences and Medical Engineering, at Guy's, King's and St Thomas' Schools of Medicine and Dentistry, King's College London.
NIH Contact Person:	None
Supported Platforms:	SUN
Brief Description:	mpr - registration by multiresolution optimisation of mutual information (6 degrees of freedom only) geomcal - registration by multiresolution optimisation of normalised mutual information (up to 12 degrees of freedom) Viewing software
For more Information:	• CSIG Home Page (disclaimer)

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Name:	VIRE (Volume Image Registration Environment)
NIH Availability:	Free
Author:	Goshtasby, Satter, Jackowski, Ding, Viswam Ketterying Medical Center
NIH Contact Person:	None
Supported Platforms:	Source written in IDL
Brief Description:	A method for registering multimodal volume images is developed. The method is currently optimized to register brain images, but it can be adjusted to register whole-body images also. In the current implementation, it is assumed that the images do not have nonlinear geometric differences. Therefore, it is suitable for registering multimodal brain images of the same individual taken a short time apart. Mutual information is used as the similarity measure in a template-matching process to determine a number of corresponding points in the images. From among the corresponding points, the four pairs that produce the least error when a linear function is fitted to them is determined. This process eliminates the outliers and inaccurate correspondences due to image noise. Spherical templates are used to increase the reliability of similarity measures when using images with rotational differences. The current method can be extended to register whole-body images with nonlinear geometric differences. This method has been tested on CT, MR, and PET images of the brain, with very encouraging results.
For more Information:	• VIRE Home Page (disclaimer) • VIRE Download Page (disclaimer)

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Name:	WAIR (Wavelet Analysis of Image Registration)
NIH Availability:	Free
Author:	Ivo D. Dinov, De Witt L. Sumners Department of Mathematics Florida State University
NIH Contact Person:	None
Supported Platforms:	Unix based machines, (Source Available)
Brief Description:	The WAIR software is a tool for quantitative analysis of various n-dimensional (n-D) image registration techniques. In particular, its applications include, but are not limited to, analyzing warp performance for stereotactic Human Brain anatomical and functional data. Using the Discrete Wavelet Transform (WT) we develop several image registration (warp) classification schemes. The first one, called "wavelet-space triangle analysis", is applicable for studying a family of warps on a single or multiple n-D data sets. For each data set the WAIR routine assigns a positive real number to every warp alignment in the family, and the best warp, for the given data, will be the one having the smallest value associated with it. The second classification method, called "cluster group classification", is applicable for analyzing the overall performance of a family of warps of a groups of data sets. Here, there is a single number assigned to each registration alignment, based on its group-clustering characteristics. Third goodness of warp approach, called SGC (spread group classification), is applicable for analyzing functional brain data. It gives preference to registration techniques that spread apart baseline versus activation functional signal for group data.
For more Information:	• WAIR Home Page (disclaimer)

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For more information about the Clinical Center,
e-mail Contact Us, or call Clinical Center Communications, 301-496-2563.

Warren Grant Magnuson Clinical Center
National Institutes of Health
Bethesda, Maryland 20892-7511