Automated Teeth Extraction from Dental Panoramic X-Ray Images using Genetic Algorithm
Bibliographic record
Abstract
Dental x-ray imaging helps dentists and radiologists to diagnose dental diseases and to provide patients with treatment plannings. In many cases, dental diseases are hard to detect by relying only on visual inspection. Therefore, automating the diagnosis process has been a topic of interest for dental problems. Teeth extraction is the basic task needed for nearly all dentistry decision support systems relying on radiographic images as the inputs. The most challenging type of image to perform extraction on is the panoramic image since it includes other parts of the patient's mouth, and structures lack explicit boundaries. The proposed method in this paper is the first automated teeth extraction system from dental panoramic images using evolutionary algorithms. First, the jaw is extracted from the main image. Then, upper and lower jaws are separated, followed by a genetic algorithm to detect teeth gap valleys. The method is assessed applying to 42 images, where the perceived accuracy is 81.14% for upper jaws and 73.63% for lower jaws, which is comparable with previous methods used on more straightforward image types.
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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 | 0.001 |
| 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".