# A Systematic Overview of Recent Methods for Non-Contact Chronic Wound Analysis
## 6 3D Wound Reconstruction  
Another aspect of wound analysis is reconstructing the wound into a 3D model. The creation of a precise non-contact wound measurement system often requires the ability to reconstruct the wound as a 3D model which gives essential data of the severity of the wound [71] and provides the means for additional wound analysis. With the development of inexpensive RGB-D cameras, 3D modeling became popular in many fields including telemedicine. Many systems have been developed for wound assessment like DERMA device [96], wound measurement device (WMD) [97], Eykona [71], MAVIS [24], MEDical PHOTogrammetric System (MEDPHOS) [98], etc. The problems with the mentioned systems are high prices and difficulty of use. Recently, a lot of published papers are analyzing images taken by smartphone cameras due to their prevalence and recent high-quality [46; 50; 81]. The use of smartphones worldwide made these methods applicable in rural areas without sufficient medical care. Patients can save images on servers so medical experts can approach the patient data with images in real-time, track the patient's wound changes, advise the patient on healing progress or even change the medical treatment just by analyzing remotely. For better evaluation and robustness of the smartphone-based methods different smartphone (camera) models should be evaluated [84]. [PERSON] et al. [99] reconstructed the 3D model in two phases: scene fusion and mesh generation. Scene fusion is the process where each captured RGB-D frame is integrated into a reconstructed volume. This phase is separated into two subphases (i) registration, based on the iterative closest point (ICP) algorithm; and (ii) integration phase based on truncated signed distance function (TSDF). Finally, with the marching cubes algorithm, the 3D model of a wound and the surrounding skin is constructed. In their further research by [PERSON] et al. [71], performance of the wound 3D reconstruction and measurement were tested with three different RGB-D cameras: Orbbec Astra S, PrimeSense Carmine 1.09, and Microsoft Kinect v2. When comparing the measurement, it was concluded that area measurement precision is better for the Astra and Carmine cameras while volume measurement showed to be highly demanding for all three RGB-D cameras. Overall PrimeSense Carmine 1.09 camera demonstrated the best results in wound analysis because of a better color to depth registration and better response to glossy surfaces. 3D reconstruction was performed in real-time with a handheld camera, hence accuracy rates were not very high. Using an industrial 3D scanner for ground truth acquisition and standard digital camera for the proposed method, [PERSON] et al. [31] obtained the 3D reconstruction from a set of image features that had been matched between two different views. Epipolar geometry is initialized with robust sparse matches, then it is iteratively reinforced, and based on triangulation, the 3D model is reconstructed. The experimental result showed error less than 0.6 mm, i.e., a geometric error of 1.3%. [PERSON] et al. [100] classified wounds from different camera angles and mapped on the 3D wound model with triangulation. Each triangle is labeled according to its tissue's highest score and