Abstract
This article presents a very high-resolution voxel-based morphometric method, by using a mass-preserving deformation mechanism and a fully automated spatial normalization approach, referred to as HAMMER. By using a hierarchical attribute-based deformation strategy, HAMMER partly overcomes limitations of several existing spatial normalization methods, and it achieves a level of accuracy that makes possible morphometric measurements of spatial specificity close to the voxel dimensions. The proposed method is validated by a series of experiments, with both simulated and real brain images.
Original language | English |
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Pages (from-to) | 28-41 |
Number of pages | 14 |
Journal | NeuroImage |
Volume | 18 |
Issue number | 1 |
DOIs | |
Publication status | Published - 2003 |
Bibliographical note
Funding Information:We thank Dr. Susan Resnick and the BLSA for providing the data sets. This work was supported in part by NIH Grant R01 AG14971 and by NIH Contract AG-93-07.
ASJC Scopus subject areas
- Neurology
- Cognitive Neuroscience