Difference between revisions of "TIBM: Registration"
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* A registration algorithm dedicated to retinal images has also been developed [https://icube-publis.unistra.fr/2-FLP11 2-FLP11]. | * A registration algorithm dedicated to retinal images has also been developed [https://icube-publis.unistra.fr/2-FLP11 2-FLP11]. | ||
* Contributions were made for the non-rigid registration of binary images [https://icube-publis.unistra.fr/2-GNKF12 2-GNKF12] as well as for the warping of binary images under topological constraints [https://icube-publis.unistra.fr/2-FPNC11 2-FPNC11]. | * Contributions were made for the non-rigid registration of binary images [https://icube-publis.unistra.fr/2-GNKF12 2-GNKF12] as well as for the warping of binary images under topological constraints [https://icube-publis.unistra.fr/2-FPNC11 2-FPNC11]. | ||
− | * The problem of warping 4th order tensor fields was also tackled [https://icube-publis.unistra.fr/4-GRNH11 4-GRNH11], the 4th order tensor being a mathematical model used in diffusion MRI to represent fiber | + | * The problem of warping 4th order tensor fields was also tackled [https://icube-publis.unistra.fr/4-GRNH11 4-GRNH11], the 4th order tensor being a mathematical model used in diffusion MRI to represent fiber crossings. |
* Estimating non-rigid registration between two exams of a given subject is an interesting tool for the quantification of cerebral atrophy over time. In this context, a method was proposed for estimating the uncertainty in atrophy quantification using a Bayesian framework [https://icube-publis.unistra.com/2-SRHR13 2-SRHR13]. | * Estimating non-rigid registration between two exams of a given subject is an interesting tool for the quantification of cerebral atrophy over time. In this context, a method was proposed for estimating the uncertainty in atrophy quantification using a Bayesian framework [https://icube-publis.unistra.com/2-SRHR13 2-SRHR13]. | ||
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Revision as of 11:17, 10 October 2016
Registration is a crucial step in medical image processing. Registration may be mono- or multimodal, rigid (intra-patient) or deformable (inter-patient) and may involve two or more images.
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PhD thesis
- S. Sharma, Estimation de l'atrophie cérébrale en IRM : application à la sclérose en plaques, septembre 2011
- A. Charnoz, Recalage d'organes intra-patient à partir de l'étude de leur réseau vasculaire : application au foie, janvier 2007
- V. Noblet, Recalage non rigide d'images cérébrales 3D avec contrainte de conservation de la topologie, mars 2006
- O. Musse, Contribution à la mise en correspondance non rigide d’images médicales : une approche paramétrique hiérarchique sous contraintes topologiques. Application au recalage déformable du cerveau en imagerie IRM, décembre 2000
- C. Nikou, Contribution au recalage d’images médicales multimodales : approches par fonctions de similarité robustes et modèles déformables physiques sous contraintes statistiques, mai 1999.