Accurate seed points classification using invariant moments & neural network
Bibliographic record
Abstract
Segmentation is a key topic in computer vision and medical image processing. Furthermore, it is used in many medical applications and techniques such as registration. Currently, an accurate segmentation is still a challenging task. In this study, the segmentation process starts by selecting seed points within the region of interest. Manual seed points selection can be time consuming and requires an expert to complete the selection. In this paper, we propose a novel method for automatic classification of the seed points in liver Magnetic Resonance Imaging (MRI) belonging to the same patient each time the segmentation is performed. The proposed method uses Geometric Moment Invariants as a feature vector to identify the locations of seed points. Artificial Neural Network (ANN) model is trained using the feature vector of each of the seed points to classify which region of the liver a testing point belongs. We have demonstrated the effectiveness of our technique in classifying three seed points. These seed points represent the left hepatic vein, central hepatic vein, and right hepatic vein of the liver. The proposed method shows high accuracy in classifying the input seed points.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".