Groupwise multi-channel image registration

Jean-Marie Guyader (Corresponding author), Wyke Huizinga, Valerio Fortunati, Dirk Poot, Jifke Veenland, Maarten Paulides, Wiro Niessen, Stefan Klein

Research output: Contribution to journalArticleAcademicpeer-review

5 Citations (Scopus)

Abstract

Multi-channel image registration is an important challenge in medical image analysis. Multi-channel images result from modalities such as dual-energy CT or multispectral microscopy. Besides, multi-channel feature images can be derived from acquired images, for instance, by applying multi-scale feature banks to the original images to register. Multi-channel registration techniques have been proposed, but most of them are applicable to only two multi-channel images at a time. In the present study, we propose to formulate multi-channel registration as a groupwise image registration problem. In this way, we derive a method that allows the registration of two or more multi-channel images in a fully symmetric manner (i.e. all images play the same role in the registration procedure), and therefore has transitive consistency by definition. The method that we introduce is applicable to any number of multi-channel images, any number of channels per image, and it allows to take into account correlation between any pair of images and not just corresponding channels. In addition, it is fully modular in terms of dissimilarity measure, transformation model, regularisation method, and optimisation strategy. For two multimodal datasets, we computed feature images from the initially acquired images, and applied the proposed registration technique to the newly created sets of multi-channel images. MIND descriptors were used as feature images, and we chose total correlation as groupwise dissimilarity measure. Results show that groupwise multi-channel image registration is a competitive alternative to the pairwise multi-channel scheme, in terms of registration accuracy and insensitivity towards registration reference spaces.

Original languageEnglish
Article number8373696
Pages (from-to)1171-1180
Number of pages10
JournalIEEE Journal of Biomedical and Health Informatics
Volume23
Issue number3
Early online date6 Jun 2018
DOIs
Publication statusPublished - May 2019
Externally publishedYes

Bibliographical note

DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

Keywords

  • dissimilarity measure
  • Feature images
  • groupwise image registration
  • multi-channel registration

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