Compensation of the non-uniformity of back-lighting sources when digitising X-ray films for video-densitometric measures
Bibliographic record
Abstract
Background: computerised image analysis and the building of electronic archives require the digitisation of x-ray films performed in the past, when direct digitisation was not yet available. Different techniques can be used, based on laser scanning or camera acquisition; both can be heavily impaired by the non-uniformity of the backlighting source. This work wants to develop a methodology, able to reduce this noise source. Methods: The introduced algorithm is a step beyond the common compensation of backlighting non-uniformity through subtraction; in fact, each grey level is corrected taking into account not only its position on the back-lighting system and its raw value, but also the interaction between these two parameters. Results: Given a certain acquisition system, the introduced algorithm has allowed to reduce the maximum grey level standard deviation, read on uniform grey level films, from 9.15 to 2.00 (on a scale from 0 to 255) against traditional subtraction techniques that achieved a minimum standard deviation equal to 5.39. Conclusions: The employed algorithm has proved to be effective in order to obtain same measured grey levels, almost independent from the position of the radiograph on the back-lighting system, and therefore makes x ray image digitisation more accurate. Only 26.4% had FADS prior to 20 weeks. Sonogram performed anytime during the pregnancy could detect 55.9% (62 of 121)) of congenital malformations suggesting that it was missed in 44.1% cases. Those who submitted for FADS between 14 – 24 weeks of pregnancy, the malformations were detected in 53.2% of 66 pregnancies. The majority (45.2%) of anomalies diagnosed by ultrasonography were neural tube defects.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".