A novel marker enhancement filter (MEF) for fluoroscopic images
Bibliographic record
Abstract
To enhance the measurements of radio-opaque cylindrical fiducial markers in low contrast x-ray and fluoroscopic images, a novel nonlinear marker enhancement filter (MEF) has been designed. It was primarily developed to assist in automatic initialization of a tracking procedure for intra-fraction organ motion analysis in fluoroscopic sequences. Conventional procedures were not able to provide sufficient improvement due to the complications of noise, small marker size, cylindrical shape and multiple orientations, intensity variations of the background, and the presence of overlaying anatomical measurements in this application. The proposed MEF design is based on the principles of linear scale space. It includes measures that assess the probability of each pixel to belong to a marker measurement, morphological operations, and a novel contrast enhancement function for standardization of the filter output. The MEF was tested on fluoroscopic images of two phantoms and three prostate patients, and was shown to perform better or comparable to the existing filters in terms of marker enhancement and background suppression, while performing significantly better in marker shape preservation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".