Effect of Neighbourhood Size in Entropy Mapping of Ultrasound Images
Bibliographic record
Abstract
Image filtering is a technique that can create additional visual representations of the original image. Entropy filtering is a specific application that can be used to highlight randomness of pixel grayscale intensities within an image. These image map created from filtering are based on the number of surrounding neighbourhood of pixels considered. However, there is no standard procedure for determining the correct "neighbourhood size" to use. We investigated the effects of neighbourhood size on the entropy calculation and provide a standardized approach for determining an appropriate neighbourhood size in entropy filtering in a musculoskeletal application. Ten healthy subjects showing no symptoms related to neuromuscular disease were recruited and ultrasound images of their trapezius muscle were acquired. The muscle regions in the images were manually isolated and regions of interest with varying neighbourhood sizes (increasing by 2 pixels) from 3x3 to 61X61 pixels were extracted. The entropy, relative signal entropy over noise entropy, statistical effect size as well as the percentage change of the effect size and instantaneous slope of the effect size was examined. The analysis showed that a neighbourhood size within the range of 21-25 pixels provides the maximum amount of information gained and coincides with a percentage change of the effect size of less than 5% and instantaneous slopes < 0.05.
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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 | 0.005 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".