Effect of Adaptive-Neighborhood Contrast Enhancement on the Extraction of the Breast Skin-Line in Mammograms
Bibliographic record
Abstract
Extraction of the breast skin-line is crucial in computeraided analysis of mammograms. This paper presents an analysis of the effect of adaptive-neighborhood contrast enhancement (ANCE) [1] on skin-line extraction. ANCE is used to enhance the parenchyma of the breast and suppress the background noise. Suppression of the background noise can improve skin-line extraction. Our skin-line extraction method is based on the work by Ojala et al. [2]. We use the Hausdorff distance [3, 4] for quantitative comparison of the skin-lines. Our work shows that ANCE improves the skin-line extraction due to its ability of suppressing noise while improving the contrast. We have defined an improvement factor based on the Hausdorff distance. The metric allows us to spot automatically the mammograms with significant improvement in the detection of the skin-line because of ANCE. We tested 83 images from the MIAS database [5], with the ground-truth skin-lines hand-drawn by a radiologist [6]. The average Hausdorff distance improvement with ANCE was 11 pixels (2.2 mm).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".