Image registration is a challenging task in the world of medical imaging.Particularly, accurate edge registration plays a central role in a variety ofclinical 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 gradientintensity and MIND self-similarity metric. Experimental results show thesuperiority of this method in edge registration tasks, while preserving theoriginal MIND performance for other image features and textures.
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