Using the local mode for edge detection in ultrasound images
Bibliographic record
Abstract
We are investigating quantitative ultrasound texture measures as an additional source of diagnostic information for the detection of white matter damage in very preterm infants. White matter damage is a form of brain damage which leads to cerebral palsy. Ultrasound speckle properties have been shown to correlate to scatterer properties in phantoms. The disease process alters the scatterer type and density in white matter. We are enhancing speckle edges in cranial ultrasound images to determine if local speckle gradients and speckle edge densities correlate to patient outcome. Speckle edges are very diffuse and traditional edge enhancing schemes, such as Sobel, do not perform well. To capture the speckle edge detail, we use film images scanned at a very high resolution. The digitization introduces a significant noise component and does not suppress the ultrasound film grain. We present a non-linear filter to enhance the speckle edge information. The filter (DM) exploits the changes to speckle edges that result from applying local mode filtering. This technique has the advantages of maintaining edge center localization with large window sizes and performing better than Sobel for diffuse edges. In this work, we discuss the filter, its parameters, and their selection for a given application
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".