# A Systematic Overview of Recent Methods for Non-Contact Chronic Wound Analysis
###### Abstract
However, if multiple views with associated point clouds are available, more detailed and precise reconstruction is possible with point cloud registration, regardless of the wound position and size. Regardless of the camera used, the recording of the wound should be as close as possible with respect to camera specifications in order to achieve the best possible resolution and sampling precision. In order to improve generalization and robustness of the overall system, lighting conditions for recording images should be such that it should not favor a single type of lighting especially when creating data samples used for training various machine learning methods. Furthermore, using diffused type of lighting sources, i.e., not using directional light should somewhat alleviate the influence of shadows from surface geometry and reflections from glossy parts of the wound. Typically, image analysis is tested only on a selected group of images which are often not a representative group, so high diversity in used images for training and testing would result in a more robust method. Another issue is the small number of images of chronic wounds that exist in publicly available databases, especially annotated. The availability of annotated databases would save a lot of time for researches in this field and it would enable easier entry of other researchers into the field and faster application of novel methods. Most commonly used wound image database is provided by  
Figure 1: Tissue types in chronic wounds. Medical Device Technical Consultancy Service (Medetec) [15], which is used by many researchers in this field [4; 6; 11; 16]. The aim of this paper is to provide an overview of most recent methods used for various stages of non-contact wound analysis which could be used for developing an end-to-end solution for fully automated system for wound analysis. This system would incorporate all stages from data acquisition, to segmentation and classification, ending with measurement and healing evaluation. Such a solution could even be integrated into the Clinical Decision Support Systems (CDSS). CDSS helps clinicians with decision making where it is basically a support system that uses knowledge gathered from two or more sources of patient data in order to generate specific advice. The system analyzes available patient data and can even be combined with electronic health records (EHR) [17]. By using the aggregated data, it provides assistance for complex tasks and makes it easier and faster for clinicians to make decisions on important cases. CDSS cannot replace the medical expert and make decisions on its own, but it can advise and help with its knowledge base in order to provide a better solution for the patient. In the study by [18], CDSS showed good results where it improved the performance of medical practitioners. Therefore, integrating an automated wound assessment solution into the CDSS would be beneficial because it would enhance the system with another valuable source of data. The system could then integrate data on wound metrics and tissue prevalence into its decision making process or it could display the 3D wound model for follow-up comparison and further analysis. The main contributions of this paper are as follows: (i) detailed overview of papers which deal with all types of wounds (including burns and melanoma) in the field of wound image analysis (alongside with computation times for conducting the proposed methods if available) as recent as 2020; (ii) overview of the most used preprocessing and post processing methods; (iii) overview of most recent wound image analysis tasks of wound segmentation, tissues classification, 3D reconstruction, wound measurement, and healing prediction presented orderly as in the studied papers. The rest of the paper is divided as follows. In Section 1.1, a brief historical overview is provided, while Section 1.2 describes the methodology used for this paper.