Local spectral adaptive multitaper method with bilateral filtering for spectrum analysis of mammographic images
Bibliographic record
Abstract
Estimation of the image power spectrum is fundamental to the development of a figure of merit for image performance analysis. We are investigating a new multitaper approach to determine power spectra, which provides a combination of low variance and high spectral resolution in the frequency range of interest. To further reduce the variance, the spectrum estimated by the proposed Local Spectral Adaptive Multitaper Method (LSAMTM) is subsequently smoothed in the frequency domain by bilateral filtering, while keeping the spectral resolution intact. This tool will be especially valuable in power spectrum estimation of images that deviate significantly from uniform white noise. The performance of this approach was evaluated in terms of spectral stability, variance reduction, bias and frequency precision. It was also compared to the conventional power spectrum method in several typical situations, including the noise power spectra (NPS) measurements of simulated projection images of a uniform phantom and NPS measurement of real detector images of a uniform phantom for two clinical digital mammography systems. Examination of variance reduction versus spectral resolution and bias indicates that the LSAMTM with bilateral filtering technique is superior to the conventional estimation methods in variance reduction, spectral resolution and in the prevention of spectrum leakage. It has the ability to keep both low variance and narrow spectral linewidth in the frequency range of interest. Up to 87% more variance reduction can be achieved with proper filtration and no sacrifice of frequency precision has been observed.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".