Evaluation of wound healing process based on texture image analysis
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».