A Comparative Study for Non-rigid Image Registration and Rigid Image Registration

• URL: http://arxiv.org/abs/2001.03831v1
• Date: Sun, 12 Jan 2020 02:32:32 GMT
• Title: A Comparative Study for Non-rigid Image Registration and Rigid Image Registration
• Authors: Xiaoran Zhang, Hexiang Dong, Di Gao and Xiao Zhao
• Abstract summary: In this study, we compare the state-of-art deep learning-based non-rigid registration approach with rigid registration approach. The Voxelmorph is trained on rigidset and nonrigidset separately for comparison. 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.
• Score: 1.7878745477602331
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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