Assessment of trabecular bone structure using fuzzy distance transform based on Min-Max operations
Bibliographic record
Abstract
Trabecular bone consists of a network of tiny strands and plates. Micro-structural features of trabecular bone include thickness, relative volume, spacing and connectivity. The accurate evaluation of these features is of significant interest in the assessment of the mechanical and transport properties of bone. Extracting these features from μCT and μMRI images is difficult and measurements vary substantially according to image processing technique used. In this paper, we propose a fuzzy distance transform (FDT) method to measure trabecular bone thickness based on Min-Max operations and using an additive weighting term. Due to the employed fuzzy Min-Max operations along with the additive weighing term, the FDT method is performed in integer number space to consequently produce a computationally fast, robust and efficient use of memory method with taking into account the gray level of pixels. This method has been used to measure the trabecular thickness of bone samples with different bone volume fractions (BVF). Additionally, its performance was studied considering parameters such as image resolution, object rotation, noise and running time. The algorithm has proven to be very robust, precise and faster.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".