Medical Image Conversion with DICOM
Bibliographic record
Abstract
As the standard especially for the storage and transmission of medical images, the standard of Digital Imaging and Communications in Medicine (DICOM) is popular to the people of the world. And as a result, almost all the outputs of the computerized tomography (CT), magnetic resonance (MR), digital subtraction angiography (DSA) and ultrasonography (US) are saved as DICOM format. However, the DICOM format files can be opened by the original programs of windows OS, which is not convenient with the further research in image processing. The paper mainly does some research in the conversion from DICOM format files into general image/ media files. The procedure is to convert DICOM format files into bitmaps and then convert the bitmaps into other general image/ media files. The images then can be viewed directly by the original programs of Windows OS, which will facilitate the further researches on image process. The DICOM format files can be displayed while being converted; the images displayed can also be done with some further operations, such as the process of lightness and contrast gradient of image, image filtering and segmentation.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.068 | 0.037 |
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".