Edge preserving multi-modal registration based on gradient intensity self-similarity

Tamar Rott, Dorin Shriki, Tamir Bendory

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publication2014 IEEE 28th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479959877
DOIs
StatePublished - 2014
Event2014 28th IEEE Convention of Electrical and Electronics Engineers in Israel, IEEEI 2014 - Eilat, Israel
Duration: 3 Dec 20145 Dec 2014

Publication series

Name2014 IEEE 28th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2014

Conference

Conference2014 28th IEEE Convention of Electrical and Electronics Engineers in Israel, IEEEI 2014
Country/TerritoryIsrael
CityEilat
Period3/12/145/12/14

Keywords

  • Image gradient
  • Image registration
  • Multi-modal similarity metric
  • Self-similarity

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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