Retinal blood vessel segmentation using the elite‐guided multi‐objective artificial bee colony algorithm
Bibliographic record
Abstract
Retinal vessel segmentation constitutes an essential part of computer‐assisted tools for the diagnosis of ocular diseases. In this study, the authors propose an unsupervised retinal blood vessels segmentation approach based on the elite‐guided multi‐objective artificial bee colony (EMOABC) algorithm. The proposed method exploits several criteria simultaneously to improve the accuracy of the segmentation results. An energy curve function is used to calculate the values of the thresholding criteria, in order to reduce the noise response from lesions and select the optimal thresholds that separate the blood vessels from the background. In order to achieve computational speed up, a stopping criterion method is used to adjust the parameters of the EMOABC algorithm. The proposed method is computationally simple and faster than most of the available unsupervised algorithms, demonstrating fast convergence to the final segmentation. Additionally, the proposed vessel segmentation method outperforms the metaheuristics vessels segmentation algorithms reported in the literature. The achieved mean discrepancy metrics for the proposed approach are 94.5% accuracy, 97.4% specificity and 73.9% sensitivity for DRIVE database, and 94% accuracy, 96.2% specificity and 73.7% sensitivity for STARE database.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".