Robust Volumetric Texture Classification of Magnetic Resonance Images of the Brain Using Local Frequency Descriptor
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a method for robust volumetric texture classification. It also proposes 2D and 3D gradient calculation methods designed to be robust to imaging effects and artifacts. Using the proposed 2D method, the gradient information is extracted on the XYZ orthogonal planes at each voxel and used to form a local coordinate system. The local coordinate system and the local 3D gradient computed by the proposed 3D gradient calculator are then used to define volumetric texture features. It is shown that the presented gradient calculation methods can be efficiently implemented by convolving with 2D and 3D kernels. The experimental results demonstrate that the proposed gradient operators and the texture features are robust to imaging effects and artifacts, such as blurriness and noise in 2D and 3D images. The proposed method is compared with three state-of- the-art volumetric texture classification methods the 3D gray level cooccurance matrix, 3D local binary patterns, and second orientation pyramid on magnetic resonance imaging data of the brain. The experimental results show the superiority of the proposed method in accuracy, robustness, and speed.
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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 | 0.002 |
| 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 it