Unsupervised model based image segmentation using domain knowledge based fuzzy logic and edge enhancement
Bibliographic record
Abstract
In this paper, we present an automatic model based image segmentation system, which combines a multi-resolution discrete dynamic contour (DDC) model refinement procedure and the domain knowledge of the image class. The segmentation begins on a low-resolution image by defining an open DDC model, followed by a contour growing process generates the closed DDC model, which deforms progressively towards higher resolution images. A combination of knowledge based fuzzy inference system (FIS) and a set of adaptive region based operators is used to enhance the edges of interest and to govern the DDC model deformation. With the above process we were able to greatly reduce the sensitivity to the initial model, thus paving the way for automatic segmentation on noisy images. Domain knowledge of a particular class of images is encapsulated within the FIS such that it can be easily changed for different image classes. We applied this algorithm successfully to detect the organ boundary in ultra-sound images of prostates and examples are shown in order to illustrate the advantages of the proposed method.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".