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Longitudinal image analysis plays an important role in depicting the development of the brain structure, where image regression and interpolation are two commonly used techniques. In this paper, we develop an efficient model and approach based on a path regression on the image manifold instead of the geodesic one to avoid the complexity of the geodesic computing. Concretely, first we model the deformation by diffeomorphism, and then a large deformation is represented by a path on the orbit of the diffeomorphism group action. This path is obtained by compositing several small deformations, which can be well approximated by its linearization. Second, we introduce some intermediate images as constraints to the model, which guide to form the best fitting path. Thirdly, we propose an approximated quadratic model by local linearization method, where a closed form is deduced for the solution. It actually speeds up the algorithm. Finally, we evaluate the proposed model and algorithm on a synthetic data and a really longitudinal MRI data and the results show that our proposed method outperforms several state-of-the-art method.
This presentation is part of Minisymposium “MS22 - Mapping problems in imaging, graphics and vision (3 parts)”
organized by: Ronald Lui (Chinese University of Hong Kong) , Ke Chen (University of Liverpool) .