Alignment of Confocal Scanning Laser Ophthalmoscopy Photoreceptor Images at Different Polarizations Using Complex Phase Relationships
Bibliographic record
Abstract
A polarimetric technique for enhancing fundus images was recently introduced , where confocal scanning laser ophthalmoscopy (CSLO) images are acquired under differing incoming polarization states, and spatially resolved Mueller images are constructed based on the images. An important stage in this technique is the alignment of CSLO images acquired under differing polarization states. This has proven to be particularly difficult when dealing with photoreceptor images, which are characterized by poor SNRs and intensity inhomogeneities due to polarization properties. In this paper, an automated approach to aligning CSLO photoreceptor images acquired under differing polarization states is presented. A novel energy functional based on complex phase relationships is introduced that is invariant to polarization and scale, as well as robust to noise and highly sensitive to photoreceptor structural characteristics. A sequential quadratic programming approach is employed to determine the optimal alignment between the photoreceptor images by minimizing the proposed energy functional. The method has been tested on CSLO fish photoreceptor images acquired under differing polarization states and evaluated based on alignment accuracy. The results demonstrate that the proposed method outperforms existing techniques used for aligning CSLO images, with lower mean alignment error for all test cases.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".