A novel diffeomorphic model for image registration and its algorithm
Zhang, Daoping and Chen, Ke (2018) A novel diffeomorphic model for image registration and its algorithm. Journal of Mathematical Imaging and Vision, 60 (8). pp. 1261-1283. ISSN 0924-9907 (https://doi.org/10.1007/s10851-018-0811-3)
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Abstract
In this work, we investigate image registration by mapping one image to another in a variational framework and focus on both model robustness and solver efficiency. We first propose a new variational model with a special regularizer, based on the quasi-conformal theory, which can guarantee that the registration map is diffeomorphic. It is well known that when the deformation is large, many variational models including the popular diffusion model cannot ensure diffeomorphism. One common observation is that the fidelity error appears small while the obtained transform is incorrect by way of mesh folding. However, direct reformulation from the Beltrami framework does not lead to effective models; our new regularizer is constructed based on this framework and added to the diffusion model to get a new model, which can achieve diffeomorphism. However, the idea is applicable to a wide class of models. We then propose an iterative method to solve the resulting nonlinear optimization problem and prove the convergence of the method. Numerical experiments can demonstrate that the new model can not only get a diffeomorphic registration even when the deformation is large, but also possess the accuracy in comparing with the currently best models.
ORCID iDs
Zhang, Daoping and Chen, Ke ORCID: https://orcid.org/0000-0002-6093-6623;-
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Item type: Article ID code: 87416 Dates: DateEvent31 October 2018Published10 April 2018Published Online29 March 2018Accepted22 March 2017SubmittedSubjects: Science > Mathematics Department: Faculty of Science > Mathematics and Statistics Depositing user: Pure Administrator Date deposited: 22 Nov 2023 14:12 Last modified: 25 Sep 2024 05:22 URI: https://strathprints.strath.ac.uk/id/eprint/87416