Wavelet Analysis of Image Registration

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A tool for the quantitative analysis of various n-dimensional (n-D) image registration techniques. The series of 'C' subroutines which comprise the WAIR library can be easily incorporated into the user's site specific programs and adapted to their particular needs.

 

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WAIR
Screenshot 1

Features

  • Wavelet-space triangle analysis is applicable for studying a family of warps on 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 with the smallest value. It uses the original data prior to warping and the target of the warp in determining warp ranking in reduced wavelet space.
  • Cluster group classification (CGC) is applicable for analyzing the overall performance of a family of warps of a group of data sets. A single number is assigned to each registration alignment, based on its group-clustering characteristics.
  • Spread group classification (SGC) gives preference to registration techniques that spread apart baseline versus activation functional signal for group data.
  • The current Java-based GUI works on all platforms. Currently, the GUI is configured to make UNIX executable calls. For people working on a PC or Mac, you may have to open the source and re-configure the GUI commands for your system.

 


Wavelet Analysis of Image Registration Support

 

Download Details

Version: 2.0
Release Date:  
Developer(s): Ivo D. Dinov
License: LONI Software License
File Size: 16 Mb
 
SYSTEM REQUIREMENTS OS: IRIX, Memory: 128 Mb
Reference(s)
Dinov ID and Sumners DWL. "Applications of Frequency Dependent Wavelet Shrinkage to Analyzing Quality of Image Registration", SIAM J. Appl. Math. (SIAP), 62(2), pp. 367-384. 2001.
 
Dinov ID, Mega MS, Thompson PM, Woods RP, Sumners DWL, Sowell EL, Toga AW. "Quantitative Comparison and Analysis of Image Registration Using Frequency-Adaptive Wavelet Shrinkage", 6(1), 73-85, 2002 IEEE Trans. Information Technology in Biomedicine.
 
Acknowledgement(s)
This work was supported by:
NIH-NCRR 9P41EB015922-15 and 2-P41-RR-013642-15
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