Registration and fusion of retinal images-an evaluation study
Why this work is in the frame
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Bibliographic record
Abstract
We present the results of a study on the application of registration and pixel-level fusion techniques to retinal images. The images are of different modalities (color, fluorescein angiogram), different resolutions, and taken at different times (from a few minutes during an angiography examination to several years between two examinations). We propose a new registration method based on global point mapping with blood vessel bifurcations as control points and a search for control point matches that uses local structural information of the retinal network. Three transformation types (similarity, affine, and second-order polynomial) are evaluated on each image pair. Fourteen pixel-level fusion techniques have been tested and classified according to their qualitative and quantitative performance. Four quantitative fusion performance criteria are used to evaluate the gain obtained with the grayscale fusion.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it