De-noising of SPECT images via optimal thresholding by wavelets
Bibliographic record
Abstract
Single photon emission computed tomography (SPECT) imaging provides functional information and precise physiological uptake of radioactivity in a patient's body. Although SPECT imaging is considered to be highly useful in oncology, but the low signal to noise ratio (SNR) caused by photon noise, introduces considerable compromise in image quality and reduction of diagnostic accuracy. It is necessary to apply appropriate noise reduction algorithm to improve the quality of acquired images. In this paper we have used wavelet based denoising in which PSNAR criteria were utilized to arrive at an optimum thresholding of the coefficient at wavelet domain. We have used SIMIND software for simulation of SPECT images and generation of images using cylindrical jaszak phantom. The images were acquired using one million counts of 64x64 matrix size. In this research, simulated images were utilized to construct data dependent optimum threshold level of wavelet coefficients We have compared the results of our thresholding scheme with those obtained by some of commonly used standard denoising schemes in which we show the use of commonly used wavelet-based denoising leads to an inferior noise reduction results as compared with our optimally searched and derived thresholding.
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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".