# A Systematic Overview of Recent Methods for Non-Contact Chronic Wound Analysis
## 4 Wound Segmentation  
Wound image segmentation where the wound is separated from the background and the other parts of the body is a necessary task in order to perform additional wound analysis such as tissue classification, wound measurement, 3D reconstruction, or healing evaluation. The goal of segmentation is to separate the image in two classes, namely, wound, and non-wound [48]. Some researchers like [PERSON] et al. [58] first segment the part of the body with the wound, and then the wound itself from the surrounding skin. The performance of image segmentation depends on the input image that is fed into the algorithm because if the wound on the input image is a too small, or very dark, the probability of accurate segmentation is uncertain [37]. As mentioned before, authors in [58] separated wound segmentation into two stages. The first stage was removing the background of the image which resulted in an image containing only the wound and surrounding skin. The result is then fed to the second stage classifier to delineate the wound. In each stage, the SVM classifier is used and trained on a combination of texture and color features with only two classes, first stage, background, and skin class (wound and surrounding skin together), and a second stage wound and surrounding skin class. Wound area segmentation is fully automated with an accuracy of 71.98% tested on 10 images. On the most recent work by [PERSON] et al. [5], authors continued to work with superpixel image regions but with a different algorithm approach. Based on a region growing algorithm, as in previous work by [PERSON] et al. [68], the authors combined 4D probability map generation with a superpixel region growing algorithm and achieved an overall accuracy of 79.2% on 30 images. [PERSON] et al. [48] introduced the spectral clustering method for wound image segmentation. Based on the S channel in HSV color space, the image was converted into data points from which the similarity graph was calculated with the Gaussian similarity function. Calculating the similarity matrix and finally, with the aid of the k-means clustering method, an encouraging accuracy of 86.73% on 105 images was obtained. In their new research [8], authors continued with fuzzy spectral clustering. Gray based fuzzy similarity measure was used to construct the similarity matrix and k-means to cluster the points. Wound segmentation based on 70 images obtained an accuracy of 91.5%. [PERSON] et al. [52] used fuzzy c-means and k-means for wound segmentation of five different types of chronic wounds on 77 images from the Medetec image database. Analysis of contrast in fifteen color spaces and their 45 color channels provided the highest contrast in Db and Dr channels in YDbDr color space. The proposed method gave average accuracy results in the Db channel 75.23% and Dr channel 72.54%. [PERSON] et al. [51] proposed a deep neural network based on modified MobileNet architecture together with traditional methods to remove the backgrounds of the images and semantically correct the segmentation results. The precision of 94.69% was achieved on 950 images obtained from the internet and hospital. The proposed WoundSeg neural network in X. [PERSON] et al. [69] also adapted MobileNet architecture with different numbers of channels alongside with VGG16 architecture. According to the same authors, networks with MobileNet architecture are more efficient compared with those using VGG16, although with a little lower accuracy of 98.18%. MobileNet is also the backbone of CNN in the most recent work by [PERSON] et al. [70]. Convolutional kernel enchanted with location information is added at the first convolution layer of the network which improves the segmentation accuracy. On 950 images precision was 95.02%. [PERSON] et al. [32] considered also wound detection as an important stage of wound analysis. All other papers consider the wound to be detected by the user who is capturing the image. In their further research [PERSON] et al.