# A Systematic Overview of Recent Methods for Non-Contact Chronic Wound Analysis
### Brief Historical Overview  
Chronic wounds as one of the medical weak spots need precise and fast healing treatment. Improvements in technology and computing in the past contributed to development in the field of automation and computer vision. Due to obvious shortcomings of contact based methods, researchers are focused on the development of automated non-contact methods for wound assessment. Objective and precise wound healing assessment would help clinicians in determining most appropriate wound treatment. Figure 2 presents typical methods used for wound analysis in specific decades. It can be seen that as technology advanced, researchers moved from simple features and hardcoded thresholds to soft computing methods and machine learning. First researchers in this field analyzed the wounds in 2D images using linear measurement, planimetry or stereophotogrammetry which had fair accuracy but it was not suitable for medical centers due to bulky equipment and slow performance [19]. Furthermore, typically a ruler, or some scaling device was placed near the wound where the image recording was taken [20]. While conducting wound measurement on video images, research by [21] concluded that it is better than using simple photography. First wound volume measurement was performed with injection of gels inside the wound [22] or with more noninvasive methods like 3D laser imaging system with notable accuracy [23]. Moreover, Measurement of Area and Volume Instrument System (MAVIS) was introduced which utilized structured light principle and generated the 3D model of the wound. The method was noninvasive and showed good performance results, but the system was large and expensive [24]. Wound tissue analysis had been primarily based on digital images with methods like red-yellow-black model [25] or color histograms, which were used to classify the wound tissues in order to assess the wound healing process [26]. Furthermore, with the development of more advanced algorithms such as support vector machines (SVM), researchers in the field of wound analysis started using these methods for image segmentation task [27]. Advances also happened in wound measurement where Visitrak system, similar to digital planimetry, was used with very high accuracy [28], but more researches focused on avoiding the contact with the wound [29]. Improved MAVIS II performed 3D reconstruction from handheld device based on stereo vision with volume error of maximum 6%, but the edge of the wound needed to be traced manually [30]. More recent methods started to implement standard digital camera for stereo vision where 3D reconstruction is conducted from two images [31], or from a handheld RGB-D camera [32]. Methods were faster, more robust and accuracy rates increased. The most recent novelty in wound analysis is the use of deep learning as it showed promising results in certain tasks [33]. With the development of widespread mobile phones, some papers performed image analysis based on pictures taken by phone with some accuracy [34]. The biggest advantage of those systems is that the user can take a picture of the wound itself, send the image to a data-center, so that a clinician can inspect the wound just by accessing the wound image remotely, and even advise the patient in healing treatment. Although all the aforementioned systems perform some tasks of wound assessment fairly, from a medical aspect, an end to end system that would perform all tasks of wound analysis including wound detection, segmentation, classification, 3D reconstruction, measurement and healing evaluation is still unknown. In order for such a system to be implemented in a medical center, it must be accurate, fast, safe for patients, easy to use, not expensive and robust in a way that can work under different image capturing conditions, types of skin, and different types of the wounds. Moreover, the system  
Figure 2: Brief timeline of typical methods used by period. should be as automatic as possible in order to reduce subjective input and raise measurement objectivity.