Abstract
This paper describes a new statistical analysis framework for diffusion-based white matter studies. The framework is based on a recent unbiased normalization algorithm for diffusion tensor images. Taking advantage of the fact that most human white matter tracts are thin sheet-like structures, this framework uses deformable medial models to represent six of the major tracts in a white matter atlas derived for a given set of images. The medial representation allows one to average tensor-based features along directions perpendicular to the tracts, thus reducing data dimensionality and accounting for errors in normalization. Unlike earlier work in the area of tract-based spatial statistics (Smith et al., 2006), this framework enables the analysis of individual white matter structures, and provides a range of possibilities for computing statistics and visualizing differences between cohorts. The framework is demonstrated in a study of white matter differences in pediatric chromosome 22q deletion syndrome.
Original language | English (US) |
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Title of host publication | Proceedings of the IEEE International Conference on Computer Vision |
DOIs | |
State | Published - 2007 |
Event | 2007 IEEE 11th International Conference on Computer Vision, ICCV - Rio de Janeiro, Brazil Duration: Oct 14 2007 → Oct 21 2007 |
Other
Other | 2007 IEEE 11th International Conference on Computer Vision, ICCV |
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Country | Brazil |
City | Rio de Janeiro |
Period | 10/14/07 → 10/21/07 |
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition