An Optic Disc Segmentation Method Based on Active Contour Tracking
Bibliographic record
Abstract
This paper attempts to find a way to complete optic disc segmentation in retinal images both accurately and efficiently.For this purpose, an optic disc segmentation method was designed based on active contour tracking.Firstly, the original retinal image was denoised, and its contrast was enhanced.Then, the center of optic disc was preliminarily identified by least squares method.Next, the region of interest (ROI) was determined based on the features and center of optic disc.Finally, the actual boundaries of optic disc were obtained by active contour tracking.Our method was tested on 522 retinal images from four representative public databases on retinal images, namely, Digital Retinal Images for Vessel Extraction (DRIVE), Structured Analysis of the Retina (STARE), Drishti-GS1 and Messidor.The experimental results show that our method achieved a segmentation accuracy of 100%, 92.6%, 99%, 95.7%, respectively, on the four databases, and exhibited better robustness and speed than the two contrastive methods.Our method was especially effective in retinal images with mild diseases or poor contrast.The research findings lay the basis for prediction and computer-aided diagnosis of fundus diseases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".