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

TL;DR

This paper introduces a symmetric, intensity interpolation-free affine image registration framework that combines intensity and spatial information, demonstrating superior robustness and accuracy in both synthetic and real biomedical image applications.

Contribution

The proposed method is a novel symmetric registration framework that integrates intensity and spatial data without intensity interpolation, improving robustness and accuracy over existing similarity measures.

Findings

Outperforms common similarity measures in robustness and accuracy

Demonstrates effectiveness on 2D and 3D biomedical images

Low computational cost suitable for real-world applications

Abstract

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.