Contrast Ratio during Visualization of Subsurface Optical Inhomogeneities in Turbid Tissues: Perturbation Analysis
Bibliographic record
Abstract
Visualization and monitoring of the capillary loops and microvasculature patterns in dermis and mucosa are of interest for various clinical applications, including early cancer and shock detection. We developed an approach for the assessment of the contrast ratio during the visualization of subsurface optical heterogeneities. Using the diffuse approximation and perturbation analysis, we considered light absorption heterogeneities as negative light sources. We estimated the contrast ratio as a function of the surface layer's optical properties for diffuse and collimated wide beam illumination. Based on findings, we formulated several practical suggestions: a) proper selection of camera (with maximum dynamic range) is of paramount importance, b) narrow-band illumination is more efficient than white light illumination, and c) use of collimated light provides up to 60% improvement in contrast vs. diffuse illumination. Obtained results can be used for the optimization of imaging techniques
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".