Multispectral image registration based on local canonical correlation analysis

Mattias P. Heinrich, Bartłomiej W. Papiez, Julia A. Schnabel, Heinz Handels

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

9 Scopus citations

Abstract

Medical scans are today routinely acquired using multiple sequences or contrast settings, resulting in multispectral data. For the automatic analysis of this data, the evaluation of multispectral similarity is essential. So far, few concepts have been proposed to deal in a principled way with images containing multiple channels. Here, we present a new approach based on a well known statistical technique: canonical correlation analysis (CCA). CCA finds a mapping of two multidimensional variables into two new bases, which best represent the true underlying relations of the signals. In contrast to previously used metrics, it is therefore able to find new correlations based on linear combinations of multiple channels. We extend this concept to efficiently model local canonical correlation (LCCA) between image patches. This novel, more general similarity metric can be applied to images with an arbitrary number of channels. The most important property of LCCA is its invariance to affine transformations of variables. When used on local histograms, LCCA can also deal with multimodal similarity. We demonstrate the performance of our concept on challenging clinical multispectral datasets.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings
PublisherSpringer Verlag
Pages202-209
Number of pages8
EditionPART 1
ISBN (Print)9783319104034
DOIs
StatePublished - 2014
Externally publishedYes
Event17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - Boston, MA, United States
Duration: 14 Sep 201418 Sep 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume8673 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014
Country/TerritoryUnited States
CityBoston, MA
Period14/09/1418/09/14

Keywords

  • MRI
  • canonical correlation
  • multichannel
  • multimodal

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