  We present a novel algorithm for Fast Registration Of image Groups (FROG),
applied to large 3D image groups. Our approach extracts 3D SURF keypoints from
images, computes matched pairs of keypoints and registers the group by
minimizing pair distances in a hubless way i.e. without computing any central
mean image. Using keypoints significantly reduces the problem complexity
compared to voxel-based approaches, and enables us to provide an in-core global
optimization, similar to the Bundle Adjustment for 3D reconstruction. As we aim
to register images of different patients, the matching step yields many
outliers. Then we propose a new EM-weighting algorithm which efficiently
discards outliers. Global optimization is carried out with a fast gradient
descent algorithm. This allows our approach to robustly register large
datasets. The result is a set of diffeomorphic half transforms which link the
volumes together and can be subsequently exploited for computational anatomy
and landmark detection. We show experimental results on whole-body CT scans,
with groups of up to 103 volumes. On a benchmark based on anatomical landmarks,
our algorithm compares favorably with the star-groupwise voxel-based ANTs and
NiftyReg approaches while being much faster. We also discuss the limitations of
our approach for lower resolution images such as brain MRI.
