Learning best features for deformable registration of MR brains

Guorong Wu; Feihu Qi; Dinggang Shen

doi:10.1007/11566489_23

Learning best features for deformable registration of MR brains

Guorong Wu, Feihu Qi, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

12 Citations (Scopus)

Abstract

This paper presents a learning method to select best geometric features for deformable brain registration. Best geometric features are selected for each brain location, and used to reduce the ambiguity in image matching during the deformable registration. Best geometric features are obtained by solving an energy minimization problem that requires the features of corresponding points in the training samples to be similar, and the features of a point to be different from those of nearby points. By incorporating those learned best features into the framework of HAMMER registration algorithm, we achieved about 10% improvement of accuracy in estimating the simulated deformation fields, compared to that obtained by HAMMER. Also, on real MR brain images, we found visible improvement of registration in cortical regions.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005 - 8th International Conference, Proceedings
Publisher	Springer Verlag
Pages	179-187
Number of pages	9
ISBN (Print)	3540293264, 9783540293262
DOIs	https://doi.org/10.1007/11566489_23
Publication status	Published - 2005
Externally published	Yes
Event	8th International Conference on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005 - Palm Springs, CA, United States Duration: 2005 Oct 26 → 2005 Oct 29

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3750 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	8th International Conference on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005
Country/Territory	United States
City	Palm Springs, CA
Period	05/10/26 → 05/10/29

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/11566489_23

Cite this

Wu, G., Qi, F., & Shen, D. (2005). Learning best features for deformable registration of MR brains. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005 - 8th International Conference, Proceedings (pp. 179-187). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3750 LNCS). Springer Verlag. https://doi.org/10.1007/11566489_23

Learning best features for deformable registration of MR brains. / Wu, Guorong; Qi, Feihu; Shen, Dinggang.

Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005 - 8th International Conference, Proceedings. Springer Verlag, 2005. p. 179-187 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3750 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Wu, G, Qi, F & Shen, D 2005, Learning best features for deformable registration of MR brains. in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005 - 8th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3750 LNCS, Springer Verlag, pp. 179-187, 8th International Conference on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005, Palm Springs, CA, United States, 05/10/26. https://doi.org/10.1007/11566489_23

Wu G, Qi F, Shen D. Learning best features for deformable registration of MR brains. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005 - 8th International Conference, Proceedings. Springer Verlag. 2005. p. 179-187. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11566489_23

Wu, Guorong ; Qi, Feihu ; Shen, Dinggang. / Learning best features for deformable registration of MR brains. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005 - 8th International Conference, Proceedings. Springer Verlag, 2005. pp. 179-187 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{b35c14602a8b46799f876db4e00df4e3,

title = "Learning best features for deformable registration of MR brains",

abstract = "This paper presents a learning method to select best geometric features for deformable brain registration. Best geometric features are selected for each brain location, and used to reduce the ambiguity in image matching during the deformable registration. Best geometric features are obtained by solving an energy minimization problem that requires the features of corresponding points in the training samples to be similar, and the features of a point to be different from those of nearby points. By incorporating those learned best features into the framework of HAMMER registration algorithm, we achieved about 10% improvement of accuracy in estimating the simulated deformation fields, compared to that obtained by HAMMER. Also, on real MR brain images, we found visible improvement of registration in cortical regions.",

author = "Guorong Wu and Feihu Qi and Dinggang Shen",

note = "Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 8th International Conference on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005 ; Conference date: 26-10-2005 Through 29-10-2005",

year = "2005",

doi = "10.1007/11566489_23",

language = "English",

isbn = "3540293264",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "179--187",

booktitle = "Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005 - 8th International Conference, Proceedings",

}

TY - GEN

T1 - Learning best features for deformable registration of MR brains

AU - Wu, Guorong

AU - Qi, Feihu

AU - Shen, Dinggang

PY - 2005

Y1 - 2005

N2 - This paper presents a learning method to select best geometric features for deformable brain registration. Best geometric features are selected for each brain location, and used to reduce the ambiguity in image matching during the deformable registration. Best geometric features are obtained by solving an energy minimization problem that requires the features of corresponding points in the training samples to be similar, and the features of a point to be different from those of nearby points. By incorporating those learned best features into the framework of HAMMER registration algorithm, we achieved about 10% improvement of accuracy in estimating the simulated deformation fields, compared to that obtained by HAMMER. Also, on real MR brain images, we found visible improvement of registration in cortical regions.

AB - This paper presents a learning method to select best geometric features for deformable brain registration. Best geometric features are selected for each brain location, and used to reduce the ambiguity in image matching during the deformable registration. Best geometric features are obtained by solving an energy minimization problem that requires the features of corresponding points in the training samples to be similar, and the features of a point to be different from those of nearby points. By incorporating those learned best features into the framework of HAMMER registration algorithm, we achieved about 10% improvement of accuracy in estimating the simulated deformation fields, compared to that obtained by HAMMER. Also, on real MR brain images, we found visible improvement of registration in cortical regions.

UR - http://www.scopus.com/inward/record.url?scp=33744819193&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33744819193&partnerID=8YFLogxK

U2 - 10.1007/11566489_23

DO - 10.1007/11566489_23

M3 - Conference contribution

C2 - 16685958

AN - SCOPUS:33744819193

SN - 3540293264

SN - 9783540293262

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 179

EP - 187

BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005 - 8th International Conference, Proceedings

PB - Springer Verlag

T2 - 8th International Conference on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005

Y2 - 26 October 2005 through 29 October 2005

ER -

Learning best features for deformable registration of MR brains

Abstract

Publication series

Other

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this