## 3 Results
### Surface Segmentation and Parameter Evaluation  
The identification of the region of interest began with an essential preprocessing step involving the cleaning of the point cloud. This process was aimed at removing noise, outliers, and irrelevant data points that could otherwise interfere with the accuracy of the subsequent 3D reconstruction. By refining the point cloud, the quality and precision of the generated mesh were significantly improved, ensuring that the reconstructed surface accurately represented the ulcer region. This step was critical for maintaining the integrity of the data and for enabling a reliable analysis in later stages, such as the extraction of quantitative parameters or the visualization of the 3D model.  
Figure 9a shows the initial point cloud corresponding to the analyzed ulcer. The preliminary preprocessing method implemented resulted in a mostly accurate alignment of the FOV with respect to the RGB camera, as depicted in Figure 9b.  
Figure 8: (**a**) Network trained on Dataset A. (**b**) Network trained on Dataset B. This figure shows YOLO segmentation performance (IoU) for each image. The median value is drawn in black, while the mean value is drawn in orange  
Surface reconstruction of the cleaned depth 3D image yielded the result shown in Figure 10a. Furthermore, internal ulcer points were leveraged to isolate and reconstruct the ulcer surface shown in Figure 10b.  
The quantitative parameters of the perimeter and area were then extracted as support for the clinical evaluation of the wound. The results are shown in Table 3 which summarizes the considered ulcer parameters.  
The 3D perimeter resulted in a value of 66.8 cm, which would have been strongly underestimated if measured in the 2D domain, yielding 39.5 cm. The 3D area computation followed a similar trend, showing almost a doubled value compared to the 2D measurements. The importance of 3D information lies in its ability to capture the true geometry and spatial characteristics of complex surfaces, which are often oversimplified in 2D representations. In the case of the ulcer measurements, the 3D perimeter and area significantly exceeded their 2D counterparts, illustrating how flat, two-dimensional approximations can underestimate the true extent of irregular or curved regions. By incorporating 3D data, it becomes possible to account for variations in surface topography and depth, providing  
Figure 10: (**a**) Total surface reconstruction of the cleaned depth 3D image corresponding to the 2D image. (**b**) Skin ulcer 3D surface reconstruction obtained by isolating internal points of the ulcer contour.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Area [cm\({}^{2}\)]** & **Perimeter [cm]** \\  & _Delaunay_ & **Perimeter [cm]** \\ \hline
**2D** & 67.9 & 39.5 \\ \hline
**3D** & 123.1 & 66.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Extracted parameters for the example RGB image.  
Figure 9: (**a**) Raw Point cloud. (**b**) Cleaned point cloud. The cleaning step on the points of no interest is shown, focusing solely on the area aligned with the corresponding 2D image highlighted with the thermal color map.  
a more accurate and comprehensive assessment of the ulcer's dimensions. This level of precision is crucial for clinical applications, where reliable measurements are necessary for diagnosis, treatment planning, and monitoring the progression or healing of the condition. Without 3D information, critical details may be missed, leading to suboptimal clinical outcomes.