TY - GEN
T1 - Riemannian Variance Filtering
T2 - 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017
AU - Zheng, Ligang
AU - Kim, Hyunwoo J.
AU - Adluru, Nagesh
AU - Newton, Michael A.
AU - Singh, Vikas
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/8/22
Y1 - 2017/8/22
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85030217705&partnerID=8YFLogxK
U2 - 10.1109/CVPRW.2017.99
DO - 10.1109/CVPRW.2017.99
M3 - Conference contribution
AN - SCOPUS:85030217705
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
SP - 699
EP - 708
BT - Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017
PB - IEEE Computer Society
Y2 - 21 July 2017 through 26 July 2017
ER -