# A Systematic Overview of Recent Methods for Non-Contact Chronic Wound Analysis
## 8 Discussion
In that case, segmentation is driven exclusively by visual features available, color and texture, where color is used more predominantly then texture because intrinsic property of the wound surface often results in glossy outlook which somewhat reduces the expressiveness of surface texture properties. By application of previously mentioned presegmentation algorithms, the main segmentation phase is facilitated by the availability of desired number of regions which have distinct visual properties. The segmentation procedure is then reduced to choosing suitable and generating appropriate visual features and descriptors as well as conducting wound/non-wound classification with some of the numerous machine learning methods available [40]. The exception to this type of pipeline include applications of deep neural networks which are typically closed form solutions that provide segmented images at the end of its operation [36]. As with other fields of computer vision, deep neural networks are slowly becoming the predominant choice for machine learning method in this field. This is evident both in terms of the number of recent research and in performance [9]. Nevertheless, the main problem for further application of deep neural networks in this field is the lack of publicly available annotated database. The lack of such a database is somewhat understandable since the actual annotation should be conducted by medical experts and wound images could be very complex with multiple tissue types that are intertwined together, resulting in significant amount of time required for annotating just a single image. Development of robust deep neural network for general application requires tens of thousands of precisely annotated images which makes creation of annotated image database in this field a gargantuan task indeed. However, certain techniques such as data augmentation and transfer learning [44] helps development of useful deep neural networks even with much smaller databases. When 3D wound data in the form of a mesh or a point cloud is available, surface geometry information beside visual information could also be used. By adding that third spatial dimension, segmentation could be facilitated and made more precise and driven by the local visual and geometric information at the same time. For now, deep neural network architectures applied in this field does not use spatial 3D geometry information, but in the future we can be sure that deep neural networks designed for 3D point clouds such as PointNet++ [110] would be adapted and used in this field. Vast majority of research in the field of wound analysis is based on 2D images, which are acceptable if the considered wound is small and located on a locally flat area. On the other hand, because of the inherent error of measurement based on 2D images [14], and the propagation of inexpensive 3D cameras which are now being included in portable devices such as smartphones, it is plausible that in the future the majority of research will shift into 3D data analysis. Older research used expensive laser line scanners [96], or multiple view geometry [100] to reconstruct the surface of the wound. Such applications were often too expensive and impractical to use or were limited by the requirements or potential noisiness of multiple view geometry approach. Modern 3D cameras are inexpensive and have reliable measurements which could be used for precise 3D wound reconstruction [71]. Wound tissue classification is extremely important for determining wound condition and therefore proposing wound therapy. Consequently, there is a lot of research on this topic. The problem by itself is rather simple, a classification into typically three classes of wound tissues: granulation, fibrin (or slough), and necrosis. It is generally accepted that the tissue classes roughly correspond to the colors, red, yellow, and black, but what makes the problem difficult is the imperfections and inconsistency of recordings, image encoding artefacts, surface reflections etc.