A framework for shape matching in deformable image registration.
Many existing image registration methods have difficulties in accurately describing significant rotation and bending of entities (e.g. organs) between two datasets. A common problem in this case is to ensure that the resulting registration is physically plausible, i.e. that the registration describes the actual bending/rotation occurring rather than just introducing expansion in some areas and shrinkage in others. In this work we developed a general framework for deformable image registration of two 3D datasets that alleviates this problem. To ensure that only physically feasible and plausible solutions to the registration problem are found, a soft tissue deformable model is used to constrain the search space for the desired correspondence map while minimizing a similarity metric between the source and reference datasets. Results from a deformable phantom experiment were used to verify and evaluate the framework.
Noe
Karsten Østergaard
KØ
Department of Computer Science, University of Aarhus, Denmark. kn@daimi.au.dk
Mosegaard
Jesper
J
Tanderup
Kari
K
Sørensen
Thomas Sangild
TS
D000465:Algorithms; D003238:Connective Tissue / Q000502:physiology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008953:Models, Anatomic; D013314:Stress, Mechanical; D014057:Tomography, X-Ray Computed