Speckle reduction and lesion segmentation of OCT tooth images for early caries detection
Bibliographic record
Abstract
The significance of identifying early non-cavitated carious lesions and monitoring the lesion extent has led to increasing prospects for prevention, early diagnosis, and implementation of conservative treatments. This paper emphasizes the importance of speckle reduction and possible lesion segmentation options of optical coherence tomography (OCT) images prior to caries detection. First, a comparison of popular speckle reduction filters is presented. These filtering algorithms were evaluated to measure the ability of different methods for reducing background noise from raw images. Both qualitative and quantitative results (signal-to-noise ratio, contrast-to-noise ratio) are reported. Image segmentation is then applied to multiple tooth images. With proper thresholding, high intensity response regions are outlined with the possibility of assessing caries and monitoring its regression. Our results show that a rotating kernel transformation (RKT) filter with 9x9 kernel size provides a good compromise between noise reduction yet preserving the pathological features of interest as required for subsequent feature segmentation analyses.
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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 | 0.000 |
| 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) | 0.000 | 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".