Osteoporosis Detection in Lumbar Spine L1-L4 based on Trabecular Bone Texture Features
Bibliographic record
Abstract
The proposed work can be divided into two parts: first, remove the noise from the trabecular lumbar spine L1-L4 of the X-ray images using two-stage principal component analysis with neighbourhood pixel grouping, followed by a hybrid median filter, and the texture features are enhanced with sharpening combined with range filters. Second, detect osteoporosis using texture features. This can be done by one-dimensional discrete wavelet transform, followed by a two-dimensional edge detection filter. Finally, classify the normal or osteoporotic images according to conventional classifications. Testing is conducted using X-ray images and dual-energy X-ray absorptiometry (DXA) reports from the same person. The DXA report describes a statistical analysis of normal or osteoporotic, but the proposed work is classified as osteoporotic or normal according to the texture features. Classified results are validated with the DXA and provide an average accuracy of 99.18%. The proposed method has better diagnostic accuracy than the existing method using DXA with X-ray.
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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".