  Image registration algorithms can be generally categorized into two groups:
non-rigid and rigid. Recently, many deep learning-based algorithms employ a
neural net to characterize non-rigid image registration function. However, do
they always perform better? In this study, we compare the state-of-art deep
learning-based non-rigid registration approach with rigid registration
approach. The data is generated from Kaggle Dog vs Cat Competition
\url{https://www.kaggle.com/c/dogs-vs-cats/} and we test the algorithms'
performance on rigid transformation including translation, rotation, scaling,
shearing and pixelwise non-rigid transformation. The Voxelmorph is trained on
rigidset and nonrigidset separately for comparison and we also add a gaussian
blur layer to its original architecture to improve registration performance.
The best quantitative results in both root-mean-square error (RMSE) and mean
absolute error (MAE) metrics for rigid registration are produced by
SimpleElastix and non-rigid registration by Voxelmorph. We select
representative samples for visual assessment.
