# A Systematic Overview of Recent Methods for Non-Contact Chronic Wound Analysis
###### Abstract  
Chronic wounds or wounds that are not healing properly are a worldwide health problem that affect the global economy and population. Alongside with aging of the population, increasing obesity and diabetes patients, we can assume that costs of chronic wound healing will be even higher. Wound assessment should be fast and accurate in order to reduce the possible complications, and therefore shorten the wound healing process. Contact methods often used by medical experts have drawbacks that are easily overcome by non-contact methods like image analysis, where wound analysis is fully or partially automated. Two major tasks in wound analysis on images are segmentation of the wound from the healthy skin and background, and classification of the most important wound tissues like granulation, fibrin, and necrosis. These tasks are necessary for further assessment like wound measurement or healing evaluation based on tissue representation. Researchers use various methods and algorithms for image wound analysis with the aim to outperform accuracy rates and show the robustness of the proposed methods. Recently, neural networks and deep learning algorithms have driven considerable performance improvement across various fields, which has a led to a significant rise of research papers in the field of wound analysis as well. The aim of this paper is to provide an overview of recent methods for non-contact wound analysis which could be used for developing an end-to-end solution for a fully automated wound analysis system which would incorporate all stages from data acquisition, to segmentation and classification, ending with measurement and healing evaluation. chronic wounds; imaging; wound segmentation; tissue classification; 3D reconstruction; wound measurement; healing evaluation +
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acute and chronic. Acute wounds heal gradually, appropriate for size and type of the wound, usually a short period of time [5]. Chronic wounds can be divided depending on its cause, e.g., venous leg ulcer, diabetic foot ulcer, pressure ulcer, and burns [6]. In [PERSON] [7], CW are similarly grouped, but instead of burns, the authors introduced arterial ulcers. Every type of CW has special circumstances and should therefore be treated and healed accordingly. Chronic wounds affect population and economies worldwide. The aging of the population together with an increase in obesity and diabetes contributes to an increasing number of chronic wounds. According to [5], expenses for USA per year for the overall treatment of chronic wounds were estimated to be $25 billion affecting 6.5 million people. According to the same source, the prevalence rate of chronic wounds in the UK population is around 5.9% which results in \(E\)2.3-_E_3.1 billion for healthcare costs per year. Germany and India have prevalence rates of 5.9% and 4.5%, respectively [8]. Worldwide, in 2012, there were 24 million people affected by chronic wounds [7]. Overall cost data for other parts of the world like Africa or India is not presented but we can assume numerous chronic wound problems due to shortages of medical staff and equipment especially in big rural areas [6]. Manual wound analysis is a problematic task because it is susceptible to the opinion of an individual; hence, wound imaging has a great potential to offer much more information than analysis from one point of view. Chronic wound analysis methods can be divided into contact and non-contact methods. Contact methods such as manual planimetry using rulers, transparency tracing, color dye injection, and alginate molds, are considered traditional and have been used mostly in the past [9]. They are usually very painful for patients as well as unpractical for medical staff, and because of the irregularity of wound shape, they often lack accuracy and precision. The application of non-contact wound analysis has improved with the increase in computational power. Moreover, advances in data analysis have led to the fast-growing use of the digital imaging approach in wound assessment.