2015 | OriginalPaper | Buchkapitel
Deformable Image Registration with Automatic Non-Correspondence Detection
verfasst von : Kanglin Chen, Alexander Derksen, Stefan Heldmann, Marc Hallmann, Benjamin Berkels
Erschienen in: Scale Space and Variational Methods in Computer Vision
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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.