ArticleProceedings. IEEE International Symposium on Biomedical Imaging2010
SERIAL NONRIGID VASCULAR REGISTRATION USING WEIGHTED NORMALIZED MUTUAL INFORMATION.
Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Weighted mutual information analysis substantially improves domain-based functional network models.Bioinformatics (Oxford, England) · 2016Article
- Low-Dimensional Non-Rigid Image Registration Using Statistical Deformation Models From Semi-Supervised Training Data.IEEE transactions on medical imaging · 2015Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Vascular registration is a challenging problem with many potential applications. However, registering vessels accurately is difficult as they often occupy a small portion of the image and their relative motion/deformation is swamped by the displacements seen in large organs such as the heart and the liver. Our registration method uses a vessel detection algorithm to generate a vesselness image (probability of having a vessel at any given voxel) which is used to construct a weighting factor that is used to modify the intensity metric to give preference to vascular structures while maintaining the larger context. Therefore, our proposing method uses fully data-driven calculated weights and needs no prior knowledge for the weight calculation. We applied our method to the registration of serial MRI lamb images obtained from studies on tissue engineered vascular grafts and demonstrate encouraging performance as compared to non-weighted registration methods.
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Registered trials
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