ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2013
Particle-guided image registration.
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Entropy-based particle correspondence for shape populations.International journal of computer assisted radiology and surgery · 2016Review
- Multi-Object Model-based Multi-Atlas Segmentation for Rodent Brains using Dense Discrete Correspondences.Proceedings of SPIE--the International Society for Optical Engineering · 2016Article
- Robust estimation of group-wise cortical correspondence with an application to macaque and human neuroimaging studies.Frontiers in neuroscience · 2015Article
- Multi-atlas segmentation with particle-based group-wise image registration.Proceedings of SPIE--the International Society for Optical Engineering · 2014Article
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Authors and funding
4 authors.
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Abstract
We present a novel image registration method based on B-spline free-form deformation that simultaneously optimizes particle correspondence and image similarity metrics. Different from previous B-spline based registration methods optimized w.r.t. the control points, the deformation in our method is estimated from a set of dense unstructured pair of points, which we refer as corresponding particles. As intensity values are matched on the corresponding location, the registration performance is iteratively improved. Moreover, the use of corresponding particles naturally extends our method to a group-wise registration by computing a mean of particles. Motivated by a surface-based group-wise particle correspondence method, we developed a novel system that takes such particles to the image domain, while keeping the spirit of the method similar. The core algorithm both minimizes an entropy based group-wise correspondence metric as well as maximizes the space sampling of the particles. We demonstrate the results of our method in an application of rodent brain structure segmentation and show that our method provides better accuracy in two structures compared to other registration methods.
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