No‐reference quality measure in brain MRI images using binary operations, texture and set analysis
Bibliographic record
Abstract
The authors propose a new application‐specific, post‐acquisition quality evaluation method for brain magnetic resonance imaging (MRI) images. The domain of a MRI slice is regarded as universal set. Four feature images; greyscale, local entropy, local contrast and local standard deviation are extracted from the slice and transformed into the binary domain. Each feature image is regarded as a set enclosed by the universal set. Four qualities attribute; lightness, contrast, sharpness and texture details are described by four different combinations of feature sets. In an ideal MRI slice, the four feature sets are identically equal. Degree of distortion in real MRI slice is quantified by fidelity between the sets that describe a quality attribute. Noise is the fifth quality attribute and is described by the slice Euler number region property. Total quality score is the weighted sum of the five quality scores. The authors' proposed method addresses current challenges in image quality evaluation. It is simple, easy‐to‐use and easy‐to‐understand. Incorporation of binary transformation in the proposed method reduces computational and operational complexity of the algorithm. They provide experimental results that demonstrate efficacy of their proposed method on good quality images and on common distortions in MRI images of the brain.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".