# Fast and Robust Symmetric Image Registration Based on Distances Combining Intensity and Spatial Information

arXiv: https://arxiv.org/abs/1807.11599 · HTML: https://mlsanity.com/paper/1807.11599/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2018.

## Built on by (most cited first, 7 of 7)

- [Light-weight Deformable Registration using Adversarial Learning with Distilling Knowledge](https://mlsanity.com/paper/2110.01293/) (2021)
- [Is Image-to-Image Translation the Panacea for Multimodal Image Registration? A Comparative Study](https://mlsanity.com/paper/2103.16262/) (2021)
- [Automated Learning for Deformable Medical Image Registration by Jointly Optimizing Network Architectures and Objective Functions](https://mlsanity.com/paper/2203.06810/) (2022)
- [Fast computation of mutual information in the frequency domain with applications to global multimodal image alignment](https://mlsanity.com/paper/2106.14699/) (2021)
- [Medical Image Registration and Its Application in Retinal Images: A Review](https://mlsanity.com/paper/2403.16502/) (2024)
- [Can representation learning for multimodal image registration be improved by supervision of intermediate layers?](https://mlsanity.com/paper/2303.00403/) (2023)
- [Paired Image to Image Translation for Strikethrough Removal From Handwritten Words](https://mlsanity.com/paper/2201.09633/) (2022)

## Abstract (arXiv)

Intensity-based image registration approaches rely on similarity measures to guide the search for geometric correspondences with high affinity between images. The properties of the used measure are vital for the robustness and accuracy of the registration. In this study a symmetric, intensity interpolation-free, affine registration framework based on a combination of intensity and spatial information is proposed. The excellent performance of the framework is demonstrated on a combination of synthetic tests, recovering known transformations in the presence of noise, and real applications in biomedical and medical image registration, for both 2D and 3D images. The method exhibits greater robustness and higher accuracy than similarity measures in common use, when inserted into a standard gradient-based registration framework available as part of the open source Insight Segmentation and Registration Toolkit (ITK). The method is also empirically shown to have a low computational cost, making it practical for real applications. Source code is available.