Detection of vertebral plateaus in lateral lumbar spinal X-ray images with Gabor filters
Bibliographic record
Abstract
A few recent studies have proposed computed-aided methods for the detection and analysis of vertebral bodies in radiographic images. This paper presents a method based on Gabor filters. Forty-one lateral lumbar spinal X-ray images from different patients were included in the study. For each image, a radiologist manually delineated the vertebral plateaus of L1, L2, L3, and L4 using a software tool for image display and mark-up. Each original image was filtered with a bank of 180 Gabor filters. The angle of the Gabor filter with the highest response at each pixel was used to derive a measure of the strength of orientation or alignment. In order to limit the spatial extent of the image data and the derived features in further analysis, a semi-automated procedure was applied to the original image. A neural network utilizing the logistic sigmoid function was trained with pixel intensity from the original image, the result of manual delineation of the plateaus, the Gabor magnitude response, and the alignment image. The average overlap between the results of detection by image processing and manual delineation of the plateaus of L1-L4 in the 41 images tested was 0.917. The results are expected to be useful in the analysis of vertebral deformities and fractures.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".