Robust Segmentation-Free Algorithm for Homogeneity Quantification in Images
Bibliographic record
Abstract
OBJECTIVE: Homogeneity is a notion used to describe images in various fields and is often linked to critical aspects of those fields. However, this term is rarely defined in the literature and no gold standard exists for its quantification. A few quantification algorithms have been proposed, but they lack both simplicity and robustness. As a result, the scientific community uses the notion of homogeneity in subjective analysis, preventing objective comparison of a large number of data or of different studies. The main objectives of this manuscript are to propose a definition of homogeneity and an algorithm for its quantification. METHOD: This algorithm, called MASQH, rely on a multi-scale, statistical and segmentation-free approach and outputs a single homogeneity index, which makes it robust and easy to use. RESULTS: The performance and reliability of the method are demonstrated through three case studies: Firstly, on synthetic images to study the behavior and assess the relevance of the algorithm in diverse situations and hence, in various potential fields. Secondly, on histological images derived from experimental chitosan-platelet-rich-plasma hybrid biomaterial, where the quantitative results are compared to a qualitative classification provided by an expert in the field. Thirdly, on experimental nanocomposites images for which results are compared to two other homogeneity quantification algorithms from the field of nanocomposites. CONCLUSION AND SIGNIFICANCE: By quantifying homogeneity, the MASQH method may help to compare disparate studies in the literature and quantitatively demonstrate the impact of homogeneity in various fields. The MASQH method is freely available online.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".