Evaluation of hierarchical elastic medical image registration method
Bibliographic record
Abstract
The paper investigates the hierarchical approach to elastic medical image registration based on mutual information (MI) (Likar, B. and Pernus, F., Image and Vision Computing, vol.19, p.33-44, 2001), in which images are progressively subdivided, locally registered, and elastically interpolated using a thin-plate spline. The technique has been shown to be efficient and robust with small local transformations. However, problems do exist with this technique. First, MI is a statistical property of the two images, so the reduction in the number of samples due to the partitioning of the images into smaller sub-images reduces the statistical quality of the joint intensity histogram. Also, the partitioning scheme may lose some important information, such as edges, which lie exactly on the partition. The statistical problem of MI is resolved by resampling and combining with global MI. An overlapping scheme is implemented in which the image is subdivided into sub-images which overlap their neighbours. This helps to overcome the edge problems. Experiments show that these two methods can improve the registration results to some limited extent (PSNR) and the visual result is much better, especially for the overlapping window scheme.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".