Fast and Robust Symmetric Image Registration Based on Distances Combining Intensity and Spatial Information
This work addresses the need for fast and reliable image registration in biomedical and medical imaging, offering an incremental improvement over existing methods.
The authors tackled the problem of robust and accurate image registration by proposing a symmetric, intensity interpolation-free affine registration framework that combines intensity and spatial information. The method demonstrated greater robustness and higher accuracy than common similarity measures in synthetic and real biomedical image tests, with low computational cost.
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.