  Voxel-based analysis methods localize brain structural differences by
performing voxel-wise statistical comparisons on two groups of images aligned
to a common space. This procedure requires highly accurate registration as well
as a sufficiently large dataset. However, in practice, the registration
algorithms are not perfect due to noise, artifacts, and complex structural
variations. The sample size is also limited due to low disease prevalence,
recruitment difficulties, and demographic matching issues. To address these
issues, in this paper, we propose a method, called block-based statistic (BBS),
for robust group comparison. BBS consists of two major components: Block
matching and permutation test. Specifically, based on two group of images
aligned to a common space, we first perform block matching so that structural
misalignments can be corrected. Then, based on results given by block matching,
we conduct robust non-parametric statistical inference based on permutation
test. Extensive experiments were performed on synthetic data and the real
diffusion MR data of mild cognitive impairment patients. The experimental
results indicate that BBS significantly improves statistical power,
notwithstanding the small sample size.
