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Deformable image registration with automatic non-correspondence detection

: Chen, K.; Derksen, A.; Heldmann, S.; Hallmann, M.; Berkels, B.


Aujol, J.-F.:
Scale space and variational methods in computer vision. 5th international conference, SSVM 2015 : Lège-Cap Ferret, France, May 31-June 4, 2015; Proceedings
Cham: Springer International Publishing, 2015 (Lecture Notes in Computer Science 9087)
ISBN: 978-3-319-18460-9 (Print)
ISBN: 978-3-319-18461-6 (Online)
International Conference on Scale Space and Variational Methods in Computer Vision (SSVM) <5, 2015, Lège-Cap Ferret>
Fraunhofer MEVIS ()

Image registration aims at establishing pointwise correspondences between given images. However, in many practical applications, no correspondences can be established in certain parts of the images. A typical example is the tumor resection area in pre- and post-operative medical images. In this paper, we introduce a novel variational framework that combines registration with an automatic detection of non-correspondence regions. The formulation of the proposed approach is simple but efficient, and compatible with a large class of image registration similarity measures and regularizers. The resulting minimization problem is solved numerically with a non-alternating gradient flow scheme. Furthermore, the method is validated on synthetic data as well as axial slices of pre-, post- and intra-operative MR T1 head scans.