# A Systematic Overview of Recent Methods for Non-Contact Chronic Wound Analysis
## 7 Wound Measurement and Healing Prediction  
Accurate measurements of the wounds are essential in wound analysis as it depicts the severity of the wound and its lengthy healing process. According to [PERSON] and [PERSON] [101], there are different techniques in wound measurement like manual metric measurement, mathematical models, manual and digital planimetry, stereophotogrammetry, and digital imaging methods. From changes of the wound metrics over some period of time the numerical factor of healing prediction can be calculated. [PERSON] et al. [37] placed a white label card with known dimensions beside the wound which was used as a reference to compute the actual pixel size, i.e., cm per pixel unit in the image. Detection of the white card on the image was done from the RYKW probability map. The proposed algorithm outputs three measurements: area, length (major diameter), and width (minor diameter) of the segmented wound with an accuracy of 75.0%, 87.0%, and 85.0%, respectively. [PERSON] et al. [102] showed that depth information is vital for wound measurement and its healing evaluation. First, the wound was segmented with Sobel operator and morphological operations. Measurements were calculated with the help of the ruler from which the pixel length is converted in metric measure. Measurements are performed on wound models with an accuracy of 70%, 80%, and 85% for depth, area, and volume, respectively, but not on the real wound images because of the inconvenience of actual wound measurement on the patients. Other papers like [PERSON] et al. [64] positioned ruler on pictures to calculate the wound measures or gauge pin as by [PERSON] et al. [31]. Authors like [PERSON] et al. [100] developed more advanced techniques and calculated the metrics from the labeled 3D model, which is a mesh of triangles, with the help of a checkerboard placed near the wound. Without any physical tool and using the reconstructed 3D model [PERSON] et al. [99] calculated the perimeter, area, and wound volume. The perimeter was measured as the length of connected points of the wound boundary, while virtual skin over the wound was used for area and volume measurement. The area is calculated as the sum of areas of individual triangles in virtual skin mesh while volume calculation is performed with the maximum unit normal component (MUNC) method and divergence theorem (DTA). [PERSON] et al. [55] developed an algorithm to calculate the healing score based on raw wound assessment results such as red, yellow, or black tissue areas. The healing score was compared with the opinion of three clinicians based on 19 pictures. The agreement between the algorithm healing score and the clinician healing score is measured with Krippendorff's alpha coefficients (KAC) and showed similar healing trends between them. Table 3 presents research papers dealing with 3D wound reconstruction, wound measurement, or prediction of wound healing. To perform the aforementioned tasks, wound segmentation and/or tissue classification must be done prior. Values presented as accuracy rates are average or the highest one for specific methods and values for computational times are average. Not all performance metrics are the same in all the papers and not all values of computational times are presented in researches. \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Paper** & \begin{tabular}{c} **Preprocessing/** \\ **Segmentation/** **Classification** \\ \end{tabular} & \begin{tabular}{c} **3D Reconstruction/Healing** **Prediction/Measurement** \\ \end{tabular} & **Image Database** & \begin{tabular}{c} **Performance** **Metrics** \\ \end{tabular} & **Avg. Time (s)** \\ \hline [PERSON] et al., [103] 2020. & -/Watershed algorithm/- & \
\begin{tabular}{c} Structure from motion, nearest \\ neighbor fed (NNP)/-/ Marker with \\ math. model \\ \end{tabular} & 90 images &
\begin{tabular}{c} 3D measurements \\ average error \\ images 3.8\% \\ \end{tabular} & 205 \\ \hline \hline \end{tabular}
\end{table}