Hierarchical attribute-guided symmetric diffeomorphic registration for MR brain images.

Guorong Wu, Minjeong Kim, Qian Wang, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Citations (Scopus)

Abstract

Deformable registration has been widely used in neuroscience studies for spatial normalization of brain images onto a standard space. Due to high anatomical variances across individual brains, registration performance could be limited when trying to estimate entire deformation pathway either from template to subject or subject to template. Symmetric image registration offers an effective way to simultaneously deform template and subject images towards each other until they meet at the middle point. Although some intensity-based registration algorithms have nicely incorporated this concept of symmetric deformation, the intensity matching between two images may not necessarily imply the correct matching of anatomical correspondences. In this paper, we integrate both strategies of hierarchical attribute matching and symmetric diffeomorphic deformation for building a new symmetric-diffeomorphic registration algorithm for MR brain images. The performance of our proposed method has been extensively evaluated and further compared with top-ranked image registration methods (SyN and diffeomorphic Demons) on brain MR images. In all experiments, our registration method achieves the best registration performance, compared to all other state-of-the-art registration methods.

Original languageEnglish
Title of host publicationMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Pages90-97
Number of pages8
Volume15
EditionPt 2
Publication statusPublished - 2012 Dec 1

Fingerprint

Brain
Neurosciences

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Wu, G., Kim, M., Wang, Q., & Shen, D. (2012). Hierarchical attribute-guided symmetric diffeomorphic registration for MR brain images. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention (Pt 2 ed., Vol. 15, pp. 90-97)

Hierarchical attribute-guided symmetric diffeomorphic registration for MR brain images. / Wu, Guorong; Kim, Minjeong; Wang, Qian; Shen, Dinggang.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 15 Pt 2. ed. 2012. p. 90-97.

Research output: Chapter in Book/Report/Conference proceedingChapter

Wu, G, Kim, M, Wang, Q & Shen, D 2012, Hierarchical attribute-guided symmetric diffeomorphic registration for MR brain images. in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 2 edn, vol. 15, pp. 90-97.
Wu G, Kim M, Wang Q, Shen D. Hierarchical attribute-guided symmetric diffeomorphic registration for MR brain images. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 2 ed. Vol. 15. 2012. p. 90-97
Wu, Guorong ; Kim, Minjeong ; Wang, Qian ; Shen, Dinggang. / Hierarchical attribute-guided symmetric diffeomorphic registration for MR brain images. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 15 Pt 2. ed. 2012. pp. 90-97
@inbook{ba816cc2eef049ada259a3058d9dc97b,
title = "Hierarchical attribute-guided symmetric diffeomorphic registration for MR brain images.",
abstract = "Deformable registration has been widely used in neuroscience studies for spatial normalization of brain images onto a standard space. Due to high anatomical variances across individual brains, registration performance could be limited when trying to estimate entire deformation pathway either from template to subject or subject to template. Symmetric image registration offers an effective way to simultaneously deform template and subject images towards each other until they meet at the middle point. Although some intensity-based registration algorithms have nicely incorporated this concept of symmetric deformation, the intensity matching between two images may not necessarily imply the correct matching of anatomical correspondences. In this paper, we integrate both strategies of hierarchical attribute matching and symmetric diffeomorphic deformation for building a new symmetric-diffeomorphic registration algorithm for MR brain images. The performance of our proposed method has been extensively evaluated and further compared with top-ranked image registration methods (SyN and diffeomorphic Demons) on brain MR images. In all experiments, our registration method achieves the best registration performance, compared to all other state-of-the-art registration methods.",
author = "Guorong Wu and Minjeong Kim and Qian Wang and Dinggang Shen",
year = "2012",
month = "12",
day = "1",
language = "English",
volume = "15",
pages = "90--97",
booktitle = "Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention",
edition = "Pt 2",

}

TY - CHAP

T1 - Hierarchical attribute-guided symmetric diffeomorphic registration for MR brain images.

AU - Wu, Guorong

AU - Kim, Minjeong

AU - Wang, Qian

AU - Shen, Dinggang

PY - 2012/12/1

Y1 - 2012/12/1

N2 - Deformable registration has been widely used in neuroscience studies for spatial normalization of brain images onto a standard space. Due to high anatomical variances across individual brains, registration performance could be limited when trying to estimate entire deformation pathway either from template to subject or subject to template. Symmetric image registration offers an effective way to simultaneously deform template and subject images towards each other until they meet at the middle point. Although some intensity-based registration algorithms have nicely incorporated this concept of symmetric deformation, the intensity matching between two images may not necessarily imply the correct matching of anatomical correspondences. In this paper, we integrate both strategies of hierarchical attribute matching and symmetric diffeomorphic deformation for building a new symmetric-diffeomorphic registration algorithm for MR brain images. The performance of our proposed method has been extensively evaluated and further compared with top-ranked image registration methods (SyN and diffeomorphic Demons) on brain MR images. In all experiments, our registration method achieves the best registration performance, compared to all other state-of-the-art registration methods.

AB - Deformable registration has been widely used in neuroscience studies for spatial normalization of brain images onto a standard space. Due to high anatomical variances across individual brains, registration performance could be limited when trying to estimate entire deformation pathway either from template to subject or subject to template. Symmetric image registration offers an effective way to simultaneously deform template and subject images towards each other until they meet at the middle point. Although some intensity-based registration algorithms have nicely incorporated this concept of symmetric deformation, the intensity matching between two images may not necessarily imply the correct matching of anatomical correspondences. In this paper, we integrate both strategies of hierarchical attribute matching and symmetric diffeomorphic deformation for building a new symmetric-diffeomorphic registration algorithm for MR brain images. The performance of our proposed method has been extensively evaluated and further compared with top-ranked image registration methods (SyN and diffeomorphic Demons) on brain MR images. In all experiments, our registration method achieves the best registration performance, compared to all other state-of-the-art registration methods.

UR - http://www.scopus.com/inward/record.url?scp=84872949720&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84872949720&partnerID=8YFLogxK

M3 - Chapter

C2 - 23286036

AN - SCOPUS:84872949720

VL - 15

SP - 90

EP - 97

BT - Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

ER -