Edge Preserving Multi-Modal Registration Based On Gradient Intensity Self-Similarity
This work addresses edge registration for medical imaging applications, but it is incremental as it builds on the existing MIND method.
The paper tackled the problem of inaccurate edge registration in multi-modal medical imaging by proposing a method that incorporates gradient intensity with the MIND self-similarity metric, resulting in improved edge alignment while maintaining performance on other features.
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.