Medical image compression based on region of interest using better portable graphics (BPG)
Bibliographic record
Abstract
Everyday, an enormous number of medical images are produced by hospitals and medical imaging center for research, surgical and disease diagnostics. Therefore, compression is necessary for storing, managing and transferring these data to make storage manageable. Medical images have some parts which are more important called region of interest (ROI) with useful information for the diagnostic purpose that should be reconstructed with high quality during the image decompression process. In this paper, a state-of-the-art image compression format known as Better Portable Graphics (BPG), which is based on the High Efficiency Video Coding (HEVC), is used for medical image compression. In the proposed compression method, first the medical image is segmented into two parts: ROI and non-ROI regions. In the next step, lossless BPG compression algorithm is applied to the ROI areas, and lossy BPG is utilized for non-ROI regions. In the end, the resulting reconstructed images are combined to create a complete compressed image. The MRI scan dataset hosted by the University of Cyprus is used to evaluate the performance of the proposed compression method to demonstrate improvement between 10-25% in the compression rate compared to traditional image compression techniques used in the medical industry.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".