Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Dimensionality reduction of medical image descriptors for multimodal image registration

Dimensionality reduction of medical image descriptors for multimodal image registration Abstract Defining similarity forms a challenging and relevant research topic in multimodal image registration. The frequently used mutual information disregards contextual information, which is shared across modalities. A recent popular approach, called modality independent neigh-bourhood descriptor, is based on local self-similarities of image patches and is therefore able to capture spatial information. This image descriptor generates vectorial representations, i.e. it is multidimensional, which results in a disadvantage in terms of computation time. In this work, we present a problem-adapted solution for dimensionality reduction, by using principal component analysis and Horn’s parallel analysis. Furthermore, the influence of dimensionality reduction in global rigid image registration is investigated. It is shown that the registration results obtained from the reduced descriptor have the same high quality in comparison to those found for the original descriptor. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Current Directions in Biomedical Engineering de Gruyter

Dimensionality reduction of medical image descriptors for multimodal image registration

Download PDF
5 pages

Loading next page...
 
/lp/de-gruyter/dimensionality-reduction-of-medical-image-descriptors-for-multimodal-RrbfdvLrS0

References (5)

Publisher
de Gruyter
Copyright
Copyright © 2015 by the
ISSN
2364-5504
eISSN
2364-5504
DOI
10.1515/cdbme-2015-0051
Publisher site
See Article on Publisher Site

Abstract

Abstract Defining similarity forms a challenging and relevant research topic in multimodal image registration. The frequently used mutual information disregards contextual information, which is shared across modalities. A recent popular approach, called modality independent neigh-bourhood descriptor, is based on local self-similarities of image patches and is therefore able to capture spatial information. This image descriptor generates vectorial representations, i.e. it is multidimensional, which results in a disadvantage in terms of computation time. In this work, we present a problem-adapted solution for dimensionality reduction, by using principal component analysis and Horn’s parallel analysis. Furthermore, the influence of dimensionality reduction in global rigid image registration is investigated. It is shown that the registration results obtained from the reduced descriptor have the same high quality in comparison to those found for the original descriptor.

Journal

Current Directions in Biomedical Engineering – de Gruyter

Published: Sep 1, 2015

There are no references for this article.