Evaluation of wound healing process based on texture image analysis
Bibliographic record
Abstract
Wound healing rate remains an interesting and important issue, in which modern imaging techniques have not yet given a definitive answer. In order to guide better therapeutic interventions, a better understanding of the fundamental mechanisms driving tissue repair are required. The wound healing rate is primarily quantified by the rate of change of the wound’s surface area. The objective of this work is to establish a standardized and objective technique to assess the progress of wound healing in wounds appearing on patient’s feet, by means of texture image analysis. Image pre-processing, segmentation, texture and geometrical analysis together with visual expert’s evaluation were used to assess the wound healing process. A total of 77 digital images from 11 different subjects with foot wounds were taken every third day, for 21 days, by an inexpensive digital camera under different lighting conditions. The images were intensity normalized, and wounds were automatic segmented using a segmentation system based on snakes. From the segmented wounds, 56 different texture features and 4 different geometrical measures were extracted in order to identify features that quantify the rate of wound healing. Texture features that may indicate the progression of wound healing process were identified. More specifically, certain texture features increase (mean, contrast, roughness and radial sum), while some other texture features decrease (sum of squares variance, sum variance, sum average, entropy, coarseness, EE-laws texture energy measures and the Hurst coefficients for fractal dimension one and two analysis) with the progression of the wound healing process. These features were found to be significantly different at an observed time point during the wound healing process, when compared to previous different time points, and this could be used to indicate the rate of wound healing. No significant differences were found for all geometrical measures extracted from the wounds between different time points. Based on the results of this study, it is suggested that some texture features might be used to monitor the wound healing process, thus reducing the workload of experts, provide standardization, reduce costs, and improve the treatment quality for patients. The simplicity of the method also suggests that it may be a valuable tool in clinical wound evaluation. A larger scale study is needed to establish the application in clinical practice and for computing texture features and geometrical measures that may provide information for better and earlier differentiation of the wound healing process. Future work will incorporate additional texture features and geometrical measures for assessing the wound healing process in order to be used in the real clinical practice.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".