Deformable Image Registration with Automatic Non-Correspondence Detection

Kanglin Chen, Alexander Derksen*, Stefan Heldmann, Marc Hallmann, Benjamin Berkels

*Corresponding author for this work


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.

Original languageEnglish
Title of host publicationInternational Conference on Scale Space and Variational Methods in Computer Vision : SSVM 2015: Scale Space and Variational Methods in Computer Vision
PublisherSpringer Verlag
Publication date01.01.2015
Publication statusPublished - 01.01.2015


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