# A Systematic Overview of Recent Methods for Non-Contact Chronic Wound Analysis
###### Abstract
Wound imaging methods can be separated into digital camera imaging, hyperspectral imaging, thermal imaging, laser doppler imaging, confocal microscopy, optical coherence tomography (OCT), near-infrared (NIR) reflectance spectroscopy imaging, ultrasound imaging, and other novel imaging techniques [6]. [PERSON] et al. [10] made a review of image-based pressure wounds analysis of 82 scientific papers and surmised four main wound imaging tasks: wound segmentation, wound measurement, tissue classification, and healing evaluation. 3D reconstruction as an important part of the wound imaging was not evaluated. Furthermore, the paper widely explains the types of neural networks, their definition and usage, and their contribution to skin wound image analysis. All analyzed papers were published before 2018. Wound segmentation separates the wound from surrounding skin and background, wound measurement determines wound dimensions, tissue classification outlines different wound tissues and healing evaluation predicts the time necessary for wound healing. Most of the research papers in image wound assessment are about wound segmentation and/or classification of wound tissues. The majority of papers divide wound tissues into three types: granulation, fibrin (slough), and necrosis (eschar) as can be seen in Figure 1[11]. Additionally, some authors expand the number of tissue types for the purpose of increased accuracy. In [PERSON] et al. [12], the authors added epithelial healing tissue, while [PERSON] and [PERSON] [13] expand to eight types of tissues, adding fascia, bone, muscle tissue, and tendon. Overall, granulation is red tissue that is healing properly, fibrin (slough) is yellow wet tissue that accumulates on the wound surface, and necrosis is black colored tissue that has died. In order to increase the objectivity of the results, computer-aided wound analysis should be automated as much as possible, without user interference, camera adjustment, placing rulers, markers, or manual cropping of images. Similarly, test images should be acquired in different lighting conditions, since this is one of the significant problems affecting the robustness of a method. Unfortunately, in order to achieve such objectivity and robustness there are many challenges which need to be overcome. Different wound types such as burns or pressure, venous, and diabetic ulcers all have their specific locations where they appear, whether they are on a flat or curved part of the body and the respective size they can achieve. Furthermore, wound surfaces can be very complex with multiple types of tissues intertwined together which make them very hard to separate. Surrounding wound tissue can also interfere with separation with its various skin pigments and especially if it has only recently been healed, its color can still resemble some parts of the wound. Depending on the camera used, wounds can be recorded in one or multiple shots. If a standard 2D camera is used and the wound is on a curved surface, the potential measurement would not be very precise if only one image is taken. It is generally accepted that image-based methods have a maximum error of area measurement of about 10% depending on the wound location and the camera angle. This fact has been experimentally proven in several studies including [14] where the authors also conclude that image-based measurement is a viable alternative to contact-based methods such as VisiTrak. In order to resolve the issue with curved surfaces, multiple images of the same wound from different angles must be taken and then multiple view geometry methods used in order to reconstruct the surface of the wound and precisely measure it. Multiple view geometry approaches have some limitations which could result in arbitrary surfaces not being able to be reconstructed or creation of noisy 3D surfaces, in which case it would be better to use a 3D camera. 3D cameras provide 3D point clouds which means that precise measurement of curved surfaces is possible even from a single view.