Automatic point landmark matching for regularizing nonlinear intensity registration: Application to thoracic CT images

Martin Urschler, Christopher Zach, Hendrik Ditt, Horst Bischof

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Abstract

Nonlinear image registration is a prerequisite for a variety of medical image analysis tasks. A frequently used registration method is based on manually or automatically derived point landmarks leading to a sparse displacement field which is densified in a thin-plate spline (TPS) framework. A large problem of TPS interpolation/approximation is the requirement for evenly distributed landmark correspondences over the data set which can rarely be guaranteed by landmark matching algorithms. We propose to overcome this problem by combining the sparse correspondences with intensity-based registration in a generic nonlinear registration scheme based on the calculus of variations. Missing landmark information is compensated by a stronger intensity term, thus combining the strengths of both approaches. An explicit formulation of the generic framework is derived that constrains an intra-modality intensity data term with a regularization term from the corresponding landmarks and an anisotropic image-driven displacement regularization term. An evaluation of this algorithm is performed comparing it to an intensity- and a landmark-based method. Results on four synthetically deformed and four clinical thorax CT data sets at different breathing states are shown. © Springer-Verlag Berlin Heidelberg 2006.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention – MICCAI 2006
Subtitle of host publication9th International Conference, Copenhagen, Denmark, October 1-6, 2006. Proceedings, Part II
EditorsRasmus Larsen, Mads Nielsen, Jon Sporring
Place of PublicationBerlin Heidelberg
PublisherSpringer
Pages710-717
Number of pages8
Volume4191
ISBN (Electronic)978-3-540-44728-3
ISBN (Print)978-3-540-44727-6
DOIs
Publication statusPublished - 2006
Event9th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006 - Copenhagen, Denmark
Duration: 1 Oct 20066 Oct 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4191 LNCS - II
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference9th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006
CountryDenmark
CityCopenhagen
Period1/10/066/10/06

Fingerprint

Splines
Image registration
Image analysis
Interpolation

Fields of Expertise

  • Information, Communication & Computing

Cite this

Urschler, M., Zach, C., Ditt, H., & Bischof, H. (2006). Automatic point landmark matching for regularizing nonlinear intensity registration: Application to thoracic CT images. In R. Larsen, M. Nielsen, & J. Sporring (Eds.), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006: 9th International Conference, Copenhagen, Denmark, October 1-6, 2006. Proceedings, Part II (Vol. 4191, pp. 710-717). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4191 LNCS - II). Berlin Heidelberg: Springer. https://doi.org/10.1007/11866763_87

Automatic point landmark matching for regularizing nonlinear intensity registration : Application to thoracic CT images. / Urschler, Martin; Zach, Christopher; Ditt, Hendrik; Bischof, Horst.

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006: 9th International Conference, Copenhagen, Denmark, October 1-6, 2006. Proceedings, Part II. ed. / Rasmus Larsen; Mads Nielsen; Jon Sporring. Vol. 4191 Berlin Heidelberg : Springer, 2006. p. 710-717 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4191 LNCS - II).

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Urschler, M, Zach, C, Ditt, H & Bischof, H 2006, Automatic point landmark matching for regularizing nonlinear intensity registration: Application to thoracic CT images. in R Larsen, M Nielsen & J Sporring (eds), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006: 9th International Conference, Copenhagen, Denmark, October 1-6, 2006. Proceedings, Part II. vol. 4191, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4191 LNCS - II, Springer, Berlin Heidelberg, pp. 710-717, 9th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006, Copenhagen, Denmark, 1/10/06. https://doi.org/10.1007/11866763_87
Urschler M, Zach C, Ditt H, Bischof H. Automatic point landmark matching for regularizing nonlinear intensity registration: Application to thoracic CT images. In Larsen R, Nielsen M, Sporring J, editors, Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006: 9th International Conference, Copenhagen, Denmark, October 1-6, 2006. Proceedings, Part II. Vol. 4191. Berlin Heidelberg: Springer. 2006. p. 710-717. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/11866763_87
Urschler, Martin ; Zach, Christopher ; Ditt, Hendrik ; Bischof, Horst. / Automatic point landmark matching for regularizing nonlinear intensity registration : Application to thoracic CT images. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006: 9th International Conference, Copenhagen, Denmark, October 1-6, 2006. Proceedings, Part II. editor / Rasmus Larsen ; Mads Nielsen ; Jon Sporring. Vol. 4191 Berlin Heidelberg : Springer, 2006. pp. 710-717 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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