Technical Note: Determining regions of interest for CCD camera‐based fiber optic luminescence dosimetry by examining signal‐to‐noise ratio
Bibliographic record
Abstract
PURPOSE: The goal of this work was to develop a method for determining regions of interest (ROIs) based on signal-to-noise ratio (SNR) for the analysis of charge-coupled device (CCD) images used in luminescence-based radiation dosimetry. METHODS: The ROI determination method was developed using images containing high-and low-intensity signals taken with a CCD-based, fiber optic plastic scintillation detector system. A series of threshold intensity values was defined for each signal, and ROIs were fitted around the pixels that exceeded each threshold. The SNR for each ROI was calculated and the relationship between SNR and ROI area was examined. RESULTS: The SNR was found to increase rapidly over small ROIs for both signal levels. After reaching a maximum, the SNR of the low-intensity signal decreased steadily over larger ROIs, but the high-intensity SNR did not decrease appreciably over the ROI sizes studied. The spatial extent of the normalized images showed intensity independence, suggesting that a fixed ROI is useful for varying signal levels. CONCLUSIONS: The method described here constitutes a simple yet effective method for defining ROIs based on SNR that could enhance the low-level detection capabilities of CCD-based luminescence dosimetry systems.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".