Tissue segmentation by fuzzy clustering technique: case study on Alzheimer's disease
Bibliographic record
Abstract
Segmentation of brain images especially into three main tissue types: Gray Matter (GM), Cerebrospinal Fluid (CSF), and White Matter (WM) has important role in computer aided neurosurgery and diagnosis. In imaging, physical phenomena and the acquisition system are responsible for noise and the Partial Volume Effect (PVE) respectively, which affect the uncertainty and the imprecision. To reduce the effect of these different imperfections, we propose a clustering approach that is based on a fuzzy- possibilistic segmentation process for the assessment of WM, GM and CSF volumes from Alzheimer’s brain images. The brain segmentation scheme which is illustrated in the study of Alzheimer’s disease using Alzheimer’s disease Neuroimaging Initiative (ADNI) and real images take in consideration the PVE and it is less sensitive to noise.
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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 | 0.000 | 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 | 0.000 | 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".