Registration using sparse free-form deformations

Wenzhe Shi; Xiahai Zhuang; Luis Pizarro; Wenjia Bai; Haiyan Wang; Kai Pin Tung; Philip Edwards; Daniel Rueckert

doi:10.1007/978-3-642-33418-4_81

Registration using sparse free-form deformations

Wenzhe Shi, Xiahai Zhuang, Luis Pizarro, Wenjia Bai, Haiyan Wang, Kai Pin Tung, Philip Edwards, Daniel Rueckert

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

27 Scopus citations

Abstract

Non-rigid image registration using free-form deformations (FFD) is a widely used technique in medical image registration. The balance between robustness and accuracy is controlled by the control point grid spacing and the amount of regularization. In this paper, we revisit the classic FFD registration approach and propose a sparse representation for FFDs using the principles of compressed sensing. The sparse free-form deformation model (SFFD) can capture fine local details such as motion discontinuities without sacrificing robustness. We demonstrate the capabilities of the proposed framework to accurately estimate smooth as well as discontinuous deformations in 2D and 3D image sequences. Compared to the classic FFD approach, a significant increase in registration accuracy can be observed in natural images (61%) as well as in cardiac MR images (53%) with discontinuous motions.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings
Editors	Bjoern H. Menze, Georg Langs, Antonio Criminisi, Albert Montillo, Georg Langs, Le Lu, Nicholas Ayache, Hervé Delingette, Zhuowen Tu, Bjoern H. Menze, Polina Golland, Kensaku Mori
Publisher	Springer Verlag
Pages	659-666
Number of pages	8
ISBN (Print)	9783642334177
DOIs	https://doi.org/10.1007/978-3-642-33418-4_81
State	Published - 2012
Externally published	Yes
Event	15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012 - Nice, France Duration: 5 Oct 2012 → 5 Oct 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7511 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012
Country/Territory	France
City	Nice
Period	5/10/12 → 5/10/12

Access to Document

10.1007/978-3-642-33418-4_81

Cite this

Shi, W., Zhuang, X., Pizarro, L., Bai, W., Wang, H., Tung, K. P., Edwards, P., & Rueckert, D. (2012). Registration using sparse free-form deformations. In B. H. Menze, G. Langs, A. Criminisi, A. Montillo, G. Langs, L. Lu, N. Ayache, H. Delingette, Z. Tu, B. H. Menze, P. Golland, & K. Mori (Eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings (pp. 659-666). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7511 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-642-33418-4_81

Shi, Wenzhe ; Zhuang, Xiahai ; Pizarro, Luis et al. / Registration using sparse free-form deformations. Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings. editor / Bjoern H. Menze ; Georg Langs ; Antonio Criminisi ; Albert Montillo ; Georg Langs ; Le Lu ; Nicholas Ayache ; Hervé Delingette ; Zhuowen Tu ; Bjoern H. Menze ; Polina Golland ; Kensaku Mori. Springer Verlag, 2012. pp. 659-666 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{77da79514f314fbaad8bed92bd9882e7,

title = "Registration using sparse free-form deformations",

abstract = "Non-rigid image registration using free-form deformations (FFD) is a widely used technique in medical image registration. The balance between robustness and accuracy is controlled by the control point grid spacing and the amount of regularization. In this paper, we revisit the classic FFD registration approach and propose a sparse representation for FFDs using the principles of compressed sensing. The sparse free-form deformation model (SFFD) can capture fine local details such as motion discontinuities without sacrificing robustness. We demonstrate the capabilities of the proposed framework to accurately estimate smooth as well as discontinuous deformations in 2D and 3D image sequences. Compared to the classic FFD approach, a significant increase in registration accuracy can be observed in natural images (61%) as well as in cardiac MR images (53%) with discontinuous motions.",

author = "Wenzhe Shi and Xiahai Zhuang and Luis Pizarro and Wenjia Bai and Haiyan Wang and Tung, {Kai Pin} and Philip Edwards and Daniel Rueckert",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2012.; 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012 ; Conference date: 05-10-2012 Through 05-10-2012",

year = "2012",

doi = "10.1007/978-3-642-33418-4_81",

language = "English",

isbn = "9783642334177",

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

publisher = "Springer Verlag",

pages = "659--666",

editor = "Menze, {Bjoern H.} and Georg Langs and Antonio Criminisi and Albert Montillo and Georg Langs and Le Lu and Nicholas Ayache and Herv{\'e} Delingette and Zhuowen Tu and Menze, {Bjoern H.} and Polina Golland and Kensaku Mori",

booktitle = "Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings",

}

Shi, W, Zhuang, X, Pizarro, L, Bai, W, Wang, H, Tung, KP, Edwards, P & Rueckert, D 2012, Registration using sparse free-form deformations. in BH Menze, G Langs, A Criminisi, A Montillo, G Langs, L Lu, N Ayache, H Delingette, Z Tu, BH Menze, P Golland & K Mori (eds), Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7511 LNCS, Springer Verlag, pp. 659-666, 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012, Nice, France, 5/10/12. https://doi.org/10.1007/978-3-642-33418-4_81

Registration using sparse free-form deformations. / Shi, Wenzhe; Zhuang, Xiahai; Pizarro, Luis et al.
Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings. ed. / Bjoern H. Menze; Georg Langs; Antonio Criminisi; Albert Montillo; Georg Langs; Le Lu; Nicholas Ayache; Hervé Delingette; Zhuowen Tu; Bjoern H. Menze; Polina Golland; Kensaku Mori. Springer Verlag, 2012. p. 659-666 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7511 LNCS).

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

TY - GEN

T1 - Registration using sparse free-form deformations

AU - Shi, Wenzhe

AU - Zhuang, Xiahai

AU - Pizarro, Luis

AU - Bai, Wenjia

AU - Wang, Haiyan

AU - Tung, Kai Pin

AU - Edwards, Philip

AU - Rueckert, Daniel

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2012.

PY - 2012

Y1 - 2012

N2 - Non-rigid image registration using free-form deformations (FFD) is a widely used technique in medical image registration. The balance between robustness and accuracy is controlled by the control point grid spacing and the amount of regularization. In this paper, we revisit the classic FFD registration approach and propose a sparse representation for FFDs using the principles of compressed sensing. The sparse free-form deformation model (SFFD) can capture fine local details such as motion discontinuities without sacrificing robustness. We demonstrate the capabilities of the proposed framework to accurately estimate smooth as well as discontinuous deformations in 2D and 3D image sequences. Compared to the classic FFD approach, a significant increase in registration accuracy can be observed in natural images (61%) as well as in cardiac MR images (53%) with discontinuous motions.

AB - Non-rigid image registration using free-form deformations (FFD) is a widely used technique in medical image registration. The balance between robustness and accuracy is controlled by the control point grid spacing and the amount of regularization. In this paper, we revisit the classic FFD registration approach and propose a sparse representation for FFDs using the principles of compressed sensing. The sparse free-form deformation model (SFFD) can capture fine local details such as motion discontinuities without sacrificing robustness. We demonstrate the capabilities of the proposed framework to accurately estimate smooth as well as discontinuous deformations in 2D and 3D image sequences. Compared to the classic FFD approach, a significant increase in registration accuracy can be observed in natural images (61%) as well as in cardiac MR images (53%) with discontinuous motions.

UR - http://www.scopus.com/inward/record.url?scp=84988876925&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-33418-4_81

DO - 10.1007/978-3-642-33418-4_81

M3 - Conference contribution

C2 - 23286105

AN - SCOPUS:84988876925

SN - 9783642334177

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

SP - 659

EP - 666

BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings

A2 - Menze, Bjoern H.

A2 - Langs, Georg

A2 - Criminisi, Antonio

A2 - Montillo, Albert

A2 - Langs, Georg

A2 - Lu, Le

A2 - Ayache, Nicholas

A2 - Delingette, Hervé

A2 - Tu, Zhuowen

A2 - Menze, Bjoern H.

A2 - Golland, Polina

A2 - Mori, Kensaku

PB - Springer Verlag

T2 - 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012

Y2 - 5 October 2012 through 5 October 2012

ER -

Shi W, Zhuang X, Pizarro L, Bai W, Wang H, Tung KP et al. Registration using sparse free-form deformations. In Menze BH, Langs G, Criminisi A, Montillo A, Langs G, Lu L, Ayache N, Delingette H, Tu Z, Menze BH, Golland P, Mori K, editors, Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings. Springer Verlag. 2012. p. 659-666. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-33418-4_81

Registration using sparse free-form deformations

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this