A novel automated approach for segmenting lateral ventricle in MR images of the brain using sparse representation classification and dictionary learning
Bibliographic record
Abstract
Segmenting lateral ventricle in medical images plays an important role in medical diagnosis. The volume of lateral ventricle increases with age and it is an important indicator of Alzheimer's, schizophrenia, and depressive disorders. In this article a new approach based on sparse representation and dictionary learning as a pre process of the existing active contour models for segmentation is introduced to automatically segment this area. In recent years utilizing methods for sparsely representing a signal over a given dictionary has gained considerable attention by scholars. Applications of signal sparse representation varies from compression to denoising, classification and many more. This article expands this growing area of research into a new level, by introducing a new approach for segmenting MRI images utilizing sparse representation solutions. The method takes advantage of K-SVD dictionary learning algorithm to create two distinct over complete dictionaries for each class and it uses sparse representation classification (SRC) algorithm to sparsely represent the image as well as discriminating the two different classes in the image.
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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 | 0.000 |
| 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".