Real‐time removal of impulse noise from MR images for radiosurgery applications
Bibliographic record
Abstract
Summary In the recent years, image processing techniques are used as a tool to improve detection and diagnostic capabilities in the medical applications. Among these techniques, medical image enhancement algorithms play an essential role in the removal of the noise, which can be produced by medical instruments and during image transfer. Impulse noise is a major type of noise, which is produced by medical imaging systems, such as MRI, computed tomography (CT), and angiography instruments. An embeddable hardware module, which can denoise medical images before and during surgical operations, could be very helpful. In this paper, an accurate algorithm is proposed for real‐time removal of impulse noise in medical images. Our algorithm categorizes all image blocks into three types of edge, smooth, and disordered areas. A different reconstruction method is applied to each category of blocks for noise removal. The proposed method is tested on MR images. Simulation results show acceptable denoising accuracy for various levels of noise. Also, an field programmable gate array (FPGA) implementation of our denoising algorithm shows acceptable hardware resource utilization. Hence, the algorithm is suitable for embedding in medical hardware instruments such as radiosurgery devices.
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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 | 0.002 | 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 | 0.000 |
| 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".