Edge Preserving Multi-Modal Registration Based On Gradient Intensity Self-Similarity

TL;DR

This paper introduces a novel image registration method that combines gradient intensity with MIND self-similarity to improve edge alignment accuracy in medical images without compromising overall registration quality.

Contribution

The proposed method enhances edge registration accuracy by integrating gradient intensity into the MIND-based registration framework, addressing limitations of existing self-similarity approaches.

Findings

Superior edge registration accuracy demonstrated in experiments

Preserves original MIND performance for textures and features

Effective in medical image registration tasks

Abstract

Image registration is a challenging task in the world of medical imaging. Particularly, accurate edge registration plays a central role in a variety of clinical conditions. The Modality Independent Neighbourhood Descriptor (MIND) demonstrates state of the art alignment, based on the image self-similarity. However, this method appears to be less accurate regarding edge registration. In this work, we propose a new registration method, incorporating gradient intensity and MIND self-similarity metric. Experimental results show the superiority of this method in edge registration tasks, while preserving the original MIND performance for other image features and textures.