Speckle detection in ultrasonic images using unsupervised clustering techniques
Bibliographic record
Abstract
In ultrasonic images, identification of speckled regions helps to estimate probe movement as well as improve performance of algorithms for adaptive speckle suppression and the elevational separation of B-scans by speckle decorrelation. By tracking FDS patch displacements over time we can calculate strain and detect tumor location. Previous studies for speckle detection were based on classification techniques which estimated parameters of the statistical distribution which were based on observation data and ultrasound echo envelope signal. However, in this study, we proposed a new combination of statistical features which were extracted from the ultrasound images and explored their properties for the speckle detection. These features were used as inputs to the unsupervised clustering algorithms for the speckle classification. We used five different types of unsupervised techniques and compared their performance by feeding different combinations of the statistical features. In order to quantitatively compare statistical features and classification methods, as ground truth, we used simulations of cyst and fetus ultrasound images which were generated using Field II ultrasound simulation program[1]. Initial results showed that by combining two statistical models (K and Rayleigh distributions) we can get best speck detection signatures to feed unsupervised classifiers and maximize speckle detection performance.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".