Performing large scale hypothesis testing on brain imaging data to identify group-wise differences (e.g., between healthy and diseased subjects) typically leads to a large number of tests (one per voxel). Multiple testing adjustment (or correction) is necessary to control false positives, which may lead to lower detection power in detecting true positives. Motivated by the use of socalled 'independent filtering' techniques in statistics (for genomics applications), this paper investigates the use of independent filtering for manifold-valued data (e.g., Diffusion Tensor Imaging, Cauchy Deformation Tensors) which are broadly used in neuroimaging studies. Inspired by the concept of variance of a Riemannian Gaussian distribution, a type of non-specific data-dependent Riemannian variance filter is proposed. In practice, the filter will select a subset of the full set of voxels for performing the statistical test, leading to a more appropriate multiple testing correction. Our experiments on synthetic/simulated manifoldvalued data show that the detection power is improved when the statistical tests are performed on the voxel locations that 'pass' the filter. Given the broadening scope of applications where manifold-valued data are utilized, the scheme can serve as a general feature selection scheme.
|Title of host publication
|Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017
|IEEE Computer Society
|Number of pages
|Published - 2017 Aug 22
|30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017 - Honolulu, United States
Duration: 2017 Jul 21 → 2017 Jul 26
|IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
|30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017
|17/7/21 → 17/7/26
Bibliographical notePublisher Copyright:
© 2017 IEEE.
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering