Polygonal approximation of contours based on the turning angle function
Bibliographic record
Abstract
The turning angle function has been used as a signature to represent the shape of a given contour with the aim of analysis of shape and content-based image retrieval. We propose a method that uses the turning angle function to derive a polygonal model of the given contour in such a manner as to preserve the important details in the contour. The preservation of diagnostically significant features present in the contours of breast masses in mammograms are important to discriminate between benign masses and malignant tumors. To evaluate the practical utility of the proposed polygonal modeling method in terms of the efficiency in the classification of breast masses, we derive an index of spiculation SIPMTF and a measure of fractional concavity Fcc from the models obtained and compare the results with those provided by two methods proposed in previous related works. The features SIPMTF and Fcc were tested with a set of 111 contours, of which 65 are related to benign masses and 46 are related to malignant tumors. High classification accuracies of 0.93 with SIPMTF and 0.91 with Fcc were obtained, in terms of the area under the receiver operating characteristics curve, with a data compression of 0.067 on the average.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".