ArticleInternational journal of biomedical imaging2012
Landmark optimization using local curvature for point-based nonlinear rodent brain image registration.
Article in International journal of biomedical imaging, 2012. 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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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.
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Who cites it
4 citing papers in PubMed.
- Bayesian Landmark-based Shape Analysis of Tumor Pathology Images.Journal of the American Statistical Association · 2024Article
- Deformable image registration between pathological images and MR image via an optical macro image.Pathology, research and practice · 2016Article
- A Learning Based Fiducial-driven Registration Scheme for Evaluating Laser Ablation Changes in Neurological Disorders.Neurocomputing · 2014Article
- Optimal setting of image bounding box can improve registration accuracy of diffusion tensor tractography.International journal of computer assisted radiology and surgery · 2013Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Purpose. To develop a technique to automate landmark selection for point-based interpolating transformations for nonlinear medical image registration. Materials and Methods. Interpolating transformations were calculated from homologous point landmarks on the source (image to be transformed) and target (reference image). Point landmarks are placed at regular intervals on contours of anatomical features, and their positions are optimized along the contour surface by a function composed of curvature similarity and displacements of the homologous landmarks. The method was evaluated in two cases (n = 5 each). In one, MRI was registered to histological sections; in the second, geometric distortions in EPI MRI were corrected. Normalized mutual information and target registration error were calculated to compare the registration accuracy of the automatically and manually generated landmarks. Results. Statistical analyses demonstrated significant improvement (P < 0.05) in registration accuracy by landmark optimization in most data sets and trends towards improvement (P < 0.1) in others as compared to manual landmark selection.
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Registered trials
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