Brain Extraction in Multiple T1-weighted Magnetic Resonance Imaging slices using Digital Image Processing techniques
Bibliographic record
Abstract
Brain Imaging has been source of several studies in the literature, mostly due to its importanceboth to predict and to analyze certain diseases or conditions. Extracting the brain from patient images for medical analysis can provide useful diagnostic and prognostic information.To this end, digital image processing algorithms have been applied to medical tasks with a focus on the identification of the brain. This work proposes a brain extraction framework based on three major steps: 1) Dataset and Image Selection; 2) Preprocessing; and 3) Largest Connected Component extraction. Our data are obtained from the OASIS dataset.The preprocessing step is applied in order to enhance contrast and eliminate possible noise from the T1-weighted MRI. Largest Connected Component extraction is performed by initially detecting the largest element in the image (i.e. the brain gray matter) and then by extracting it through mathematical morphology operators. The unsupervised framework extracts the brain in different axial slices without adjustments. The main contribution of this work is a method using only digital image processing for automatically identifying the brain from several different slices, which differs from the literature since is performed without parameter resetting. Five metrics were applied to evaluate our results: Specificity, Recall, Accuracy, F-Measure, and Precision. In our first experiment, two metrics resulted in more than 90% in efficiency (Specificity and Precision), two of them surpassed 80% (F-Measure and Accuracy), and Sensitivity exceeded 70%. Our second experiment compares our results with those produced by related works, having been ranked in the top positions of Sensitivity and Specificity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".