Optic Disc Localization in Normal and Pathological Retinal Images Using Dictionary-Based Approach
Bibliographic record
Abstract
This paper presents a novel method for optic disc (OD) detection in retinal images using a dictionary-based approach.The proposed method leverages the consistent properties of the OD region within retinal images, such as its circular shape, brightness, and blood vessel convergence at the center.In the training phase, a set of dictionary elements is composed using similar patterns selected from sub-images.The OD region can then be linearly represented in terms of these dictionary elements.The robustness of the detected OD center is ensured by verifying the presence of main blood vessel convergence at its neighborhood.The main blood vessels are obtained by estimating the background information using a large window sized median filter.To reduce the average computation time, OD detection is performed only on bright pixels in the test image, with pixel intensity assumed as the primary component for OD detection.These bright pixels are selected using local thresholding with a 15*15 block and Otsu algorithm.The proposed method is evaluated on three publicly available datasets, DRIVE, STARE, and DIARETDB1, comprising 210 retinal images.The experimental results show a success rate of 100%, 95.06%, and 98.8% for the three datasets, respectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".