@inproceedings{7a47bd9b7800419492951961957b46d7,
title = "Edge preserving multi-modal registration based on gradient intensity self-similarity",
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.",
keywords = "Image gradient, Image registration, Multi-modal similarity metric, Self-similarity",
author = "Tamar Rott and Dorin Shriki and Tamir Bendory",
note = "Publisher Copyright: {\textcopyright} Copyright 2015 IEEE All rights reserved.; 2014 28th IEEE Convention of Electrical and Electronics Engineers in Israel, IEEEI 2014 ; Conference date: 03-12-2014 Through 05-12-2014",
year = "2014",
doi = "https://doi.org/10.1109/EEEI.2014.7005886",
language = "الإنجليزيّة",
series = "2014 IEEE 28th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2014 IEEE 28th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2014",
address = "الولايات المتّحدة",
}