Standard

Groupwise multichannel image registration. / Guyader, Jean Marie; Huizinga, Wyke; Fortunati, Valerio; Poot, Dirk H.J.; Veenland, Jifke F.; Paulides, Margarethus M.; Niessen, Wiro J.; Klein, Stefan.

In: IEEE Journal of Biomedical and Health Informatics, Vol. 23, No. 3, 8373696, 2019, p. 1171-1180.

Research output: Contribution to journalArticleScientificpeer-review

Harvard

Guyader, JM, Huizinga, W, Fortunati, V, Poot, DHJ, Veenland, JF, Paulides, MM, Niessen, WJ & Klein, S 2019, 'Groupwise multichannel image registration' IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 3, 8373696, pp. 1171-1180. https://doi.org/10.1109/JBHI.2018.2844361

APA

Guyader, J. M., Huizinga, W., Fortunati, V., Poot, D. H. J., Veenland, J. F., Paulides, M. M., ... Klein, S. (2019). Groupwise multichannel image registration. IEEE Journal of Biomedical and Health Informatics, 23(3), 1171-1180. [8373696]. https://doi.org/10.1109/JBHI.2018.2844361

Vancouver

Guyader JM, Huizinga W, Fortunati V, Poot DHJ, Veenland JF, Paulides MM et al. Groupwise multichannel image registration. IEEE Journal of Biomedical and Health Informatics. 2019;23(3):1171-1180. 8373696. https://doi.org/10.1109/JBHI.2018.2844361

Author

Guyader, Jean Marie ; Huizinga, Wyke ; Fortunati, Valerio ; Poot, Dirk H.J. ; Veenland, Jifke F. ; Paulides, Margarethus M. ; Niessen, Wiro J. ; Klein, Stefan. / Groupwise multichannel image registration. In: IEEE Journal of Biomedical and Health Informatics. 2019 ; Vol. 23, No. 3. pp. 1171-1180.

BibTeX

@article{f32f5a71302f42cb89857594899b4232,
title = "Groupwise multichannel image registration",
abstract = "Multichannel image registration is an important challenge in medical image analysis. Multichannel images result from modalities such as dual-energy CT or multispectral microscopy. Besides, multichannel feature images can be derived from acquired images, for instance, by applying multiscale feature banks to the original images to register. Multichannel registration techniques have been proposed, but most of them are applicable to only two multichannel images at a time. In the present study, we propose to formulate multichannel registration as a groupwise image registration problem. In this way, we derive a method that allows the registration of two or more multichannel 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 multichannel 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 multichannel images. MIND descriptors were used as feature images, and we chose total correlation as groupwise dissimilarity measure. Results show that groupwise multichannel image registration is a competitive alternative to the pairwise multichannel scheme, in terms of registration accuracy and insensitivity towards registration reference spaces.",
keywords = "dissimilarity measure, Feature images, groupwise image registration, multi-channel registration",
author = "Guyader, {Jean Marie} and Wyke Huizinga and Valerio Fortunati and Poot, {Dirk H.J.} and Veenland, {Jifke F.} and Paulides, {Margarethus M.} and Niessen, {Wiro J.} and Stefan Klein",
year = "2019",
doi = "10.1109/JBHI.2018.2844361",
language = "English",
volume = "23",
pages = "1171--1180",
journal = "IEEE Journal of Biomedical and Health Informatics",
issn = "2168-2194",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "3",

}

RIS

TY - JOUR

T1 - Groupwise multichannel image registration

AU - Guyader, Jean Marie

AU - Huizinga, Wyke

AU - Fortunati, Valerio

AU - Poot, Dirk H.J.

AU - Veenland, Jifke F.

AU - Paulides, Margarethus M.

AU - Niessen, Wiro J.

AU - Klein, Stefan

PY - 2019

Y1 - 2019

N2 - Multichannel image registration is an important challenge in medical image analysis. Multichannel images result from modalities such as dual-energy CT or multispectral microscopy. Besides, multichannel feature images can be derived from acquired images, for instance, by applying multiscale feature banks to the original images to register. Multichannel registration techniques have been proposed, but most of them are applicable to only two multichannel images at a time. In the present study, we propose to formulate multichannel registration as a groupwise image registration problem. In this way, we derive a method that allows the registration of two or more multichannel 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 multichannel 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 multichannel images. MIND descriptors were used as feature images, and we chose total correlation as groupwise dissimilarity measure. Results show that groupwise multichannel image registration is a competitive alternative to the pairwise multichannel scheme, in terms of registration accuracy and insensitivity towards registration reference spaces.

AB - Multichannel image registration is an important challenge in medical image analysis. Multichannel images result from modalities such as dual-energy CT or multispectral microscopy. Besides, multichannel feature images can be derived from acquired images, for instance, by applying multiscale feature banks to the original images to register. Multichannel registration techniques have been proposed, but most of them are applicable to only two multichannel images at a time. In the present study, we propose to formulate multichannel registration as a groupwise image registration problem. In this way, we derive a method that allows the registration of two or more multichannel 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 multichannel 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 multichannel images. MIND descriptors were used as feature images, and we chose total correlation as groupwise dissimilarity measure. Results show that groupwise multichannel image registration is a competitive alternative to the pairwise multichannel scheme, in terms of registration accuracy and insensitivity towards registration reference spaces.

KW - dissimilarity measure

KW - Feature images

KW - groupwise image registration

KW - multi-channel registration

UR - http://www.scopus.com/inward/record.url?scp=85048177020&partnerID=8YFLogxK

U2 - 10.1109/JBHI.2018.2844361

DO - 10.1109/JBHI.2018.2844361

M3 - Article

VL - 23

SP - 1171

EP - 1180

JO - IEEE Journal of Biomedical and Health Informatics

T2 - IEEE Journal of Biomedical and Health Informatics

JF - IEEE Journal of Biomedical and Health Informatics

SN - 2168-2194

IS - 3

M1 - 8373696

ER -

ID: 53908469