AN AUTOMATIC BI-CHANNEL COMPRESSION TECHNIQUE FOR MEDICAL IMAGES
Bibliographic record
Abstract
This paper introduces an automatic bi-channel compression technique for ROI segmentation and medical image (MI) compression. A novel ROI segmentation technique is presented. This technique uses an introduced artificial neural network (ANN) and an introduced difference fuzzy model (IDFM), obtaining irregular spider hexagon ROI contours. The whole medical image is to be transmitted progressively using the fast algorithm for embedded zerotree wavelet (FEZW) [1]. Different refinement levels are applied to different MI regions. High compression ratios are obtained outside ROI, and a compromise between compression ratio and image quality is to be maintained by choosing a suitable threshold level inside the ROI. The proposed work reduces complexity and storage space, saves time, and has the advantage over previous works that it is fully automatic. Several brain magnetic resonance imaging (MRI) and fluorescene ophthalmic images are analysed; results are compared with other techniques to validate the proposed work.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".