Removing radiopaque artifacts from mammograms using area morphology
Bibliographic record
Abstract
A mammogram contains two distinctive regions: the exposed breast region and the unexposed air-background region. The background region often contains radiopaque artifacts in the form of identification labels, radiopaque markers, and wedges. The primary motivation for removing such artifacts from mammograms is too lessen their effect on subsequent processing algorithms. For example, accurate segmentation of the breast region is an important pre-processing step in the computerized analysis of mammograms. It allows the search for abnormalities to be limited to the breast region of the mammogram without undue influence from the background. One of the problems with precise segmentation of the breast region is that high-intensity radiopaque artifacts can result in a non-uniform background region, and interfere with deriving an accurate representation of the breast contour. This paper proposes a new approach for removing radiopaque artifacts from the background region of mammograms based on the concept of area morphology. Area morphology uses attributes of structures rather than a fixed shape structuring element as used in classical morphology. This allows radiopaque artifacts to be removed, irrespective of shape.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".