An Investigation of Bone Image Texture Analysis for Predicting Fracture Risk
Bibliographic record
Abstract
Osteoporosis is caused by loss of bone mineral content, which leads to bone fractures or structural deformations of bone. Osteoporosis usually occurs when people get older, after menopause in women, or it can be caused by a lack in the intake of a sufficient amount of calcium and vitamin D. Until recently, osteoporosis was considered to be an unavoidable part of aging, but today, approved and effective treatments can be used to deal with the consequences. At present, determination of risk of bone abnormalities is done by measuring the density of bone (largely determined by calcium content). Dual energy X-ray Absorptiometry (DXA) is the gold standard technique for measuring bone mineral density (BMD). Even though BMD is one of the principal determinants of bone strength, BMD measurements do not give information about variation of trabecular structure of bone. That's why DXA alone has limited ability to predict who will sustain an osteoporotic fracture. To predict fracture risk of patients, the texture analysis of the DXA images is of interest as a measure to predict fracture in addition to BMD. This thesis focuses on the application of texture analysis to digital images of bone scans of patients at risk of fracture and osteoporosis. Texture analysis was performed by analyzing the variation of grey level patterns of pixels of DXA images. Texture analysis of such images will give an idea of the variation of grey scale patterns of pixels between normal and osteoporotic DXA images of bone. Existing texture analysis measures such as contrast measures of co-occurrence matrices and mean slope value of fractal dimension based measure are used to analyze the texture of DXA images. An alternative partitioning technique is proposed as a measure of the texture analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".