Pectoral Muscle Segmentation on Digital Mammograms by Nonlinear Diffusion Filtering
Bibliographic record
Abstract
The pectoral muscle represents a predominant density region in the most medio-lateral oblique (MLO) views of mammograms. Presence of pectoral muscle in the mammogram may affect the resulting of image processing and could bias the detection procedures. So during analysis, the pectoral muscle should preferably be excluded from processing. We proposed a new method for the identification of the pectoral muscle in MLO mammograms based on nonlinear diffusion algorithm which is an edge preserving smoother. The proposed method is applied to 90 mammograms from Mammography Image Analysis Society (MIAS) database. We compared our results by those recognized by two expert radiologists. To evaluate the accuracy of proposed method, HDM (Hausdorff distance measure) and MAEDM (mean of absolute error distance measure) were used. Then we compared our results by two other pectoral muscle segmentation methods proposed by Karssemeijer and Ferrari. The first is based on Hough-transform and the second is based on Gabor-filters. Our proposed algorithm shows superior results in comparison.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".