Jan Modersitzki
Research Subjects
Research Interests
Jan Modersitzki has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Oliver Schmitt
MSH Medical School Hamburg – University of Applied Sciences and Medical University, Hamburg, Germany
3 shared publications - Bernd Fischer
University of Lübeck
1 shared publication - Bernd Fischer
Medical University of Lübeck
1 shared publication G. Lustig
Medical University of Lübeck
1 shared publication- Wolfgang Obelöer
Medical University of Lübeck
1 shared publication
Latest Publications
- 1 January 2004
- Published by Georg Thieme Verlag KG in Methods of Information in Medicine
- Vol. 43 (04) , 327-330
Intensity-based Image Registration with a Guaranteed One-to-one Point Match
Objectives: In this paper, we propose a novel registration technique, which combines the concepts of landmark and automatic, non-rigid intensity-based approaches. A general framework, which might be used for many different registration problems is presented. The novel approach enables the incorporation of different distance measures as well as different smoothers. Methods: The proposed scheme minimizes a regular-ized distance measure subject to some interpolation constraints. The desired deformation is computed iteratively using an Euler-scheme for the first variation of the chosen objective functional. Results: A fast and robust numerical scheme for the computation of the wanted minimizer is developed, implemented, and applied to various registration tasks. This includes the registration of pre- and post-intervention images of the human eye. Conclusions: A novel framework for a parameter-free, non-rigid registration scheme which allows for the additional incorporation of user-defined landmarks is proposed. It enhances the reliability of conventional approaches considerably and thereby their acceptability by practitioners in a clinical environment.
- 1 January 2004
- Published by Georg Thieme Verlag KG in Methods of Information in Medicine
- Vol. 43 (04) , 327-330
Intensity-based Image Registration with a Guaranteed One-to-one Point Match
Objectives: In this paper, we propose a novel registration technique, which combines the concepts of landmark and automatic, non-rigid intensity-based approaches. A general framework, which might be used for many different registration problems is presented. The novel approach enables the incorporation of different distance measures as well as different smoothers. Methods: The proposed scheme minimizes a regular-ized distance measure subject to some interpolation constraints. The desired deformation is computed iteratively using an Euler-scheme for the first variation of the chosen objective functional. Results: A fast and robust numerical scheme for the computation of the wanted minimizer is developed, implemented, and applied to various registration tasks. This includes the registration of pre- and post-intervention images of the human eye. Conclusions: A novel framework for a parameter-free, non-rigid registration scheme which allows for the additional incorporation of user-defined landmarks is proposed. It enhances the reliability of conventional approaches considerably and thereby their acceptability by practitioners in a clinical environment.
- 1 January 2001
- Published by Springer Nature
- p. 179-183
Effiziente, nicht-lineare Registrierung eines histologischen Serienschnittes durch das menschliche Gehirn
- 1 January 2001
- Published by Springer Nature
- p. 179-183
Effiziente, nicht-lineare Registrierung eines histologischen Serienschnittes durch das menschliche Gehirn
- 1 January 2001
- Published by Springer Nature
- p. 174-178
Registrierung einer hochaufgelösten histologischen Schnittserie eines Rattenhirns
- 1 January 2001
- Published by Springer Nature
- p. 174-178
Registrierung einer hochaufgelösten histologischen Schnittserie eines Rattenhirns
- 1 January 1999
- Published by Springer Nature
- p. 141-149
Elastic matching of very large digital images on high performance clusters
- 1 January 1999
- Published by Springer Nature
- p. 141-149
Elastic matching of very large digital images on high performance clusters