Effects of image resolution and noise on estimating the fractal dimension of tissue specimens.
Bibliographic record
Abstract
OBJECTIVE: To investigate the effects of imaging system noise and resolution on the ability to estimate and distinguish relative differences in the fractal dimension of tissue specimens. STUDY DESIGN: Mathematically derived test images of known fractal dimension mimicking the complexity of epithelial morphology were created. The box-counting method was used to compute fractal dimension. To study the effects of resolution on fractal dimension, the test images were convolved with Gaussian point spread functions (PSF), and effects of noise were studied by adding Poisson and Gaussian noise. Application of these findings was illustrated by measuring the resolution and noise for a typical optical microscope and digital camera (OMDC) system. RESULTS: Poor spatial resolution reduces the fractal dimension and has an increased adverse effect on higher dimensions. Fractal dimension can be estimated within 7% of the true dimension, and relative differences of 0.1 between dimensions are distinguishable provided the PSF of an imaging system has a full-width-at-half-maximum < or = 4 pixels and the contrast-to-noise ratio > 15. These conditions were satisfied by our OMDC. CONCLUSION: Effects of noise and resolution from a typical OMDC system do not significantly inhibit the ability to estimate and distinguish relative differences in the fractal dimension of tissue specimens.
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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".