# A Systematic Overview of Recent Methods for Non-Contact Chronic Wound Analysis
## 5 Tissue Classification  
In the wound image analysis process, tissue classification is usually performed after the wound segmentation process in order to analyze only the tissues on the wound area itself. Therefore, the wound is the only region of interest to be analyzed in this phase. Tissue separation is required for further wound analysis, e.g., healing evaluation. Some research like [PERSON] et al. (2019) do not perform the segmentation process separately, instead, they conduct it with tissue classification at the same time. Some other authors like [PERSON] et al. (2019) and [PERSON] et al. (2019) assume that the boundary of the wound is known so they only focus on the tissue classification task. Calculation of probability map of a pixel belonging to one of the color categories in RYKW color space based on distance in modified HSV space is the foundation for the separation of tissues by [PERSON] et al. (2019). In the used region growing algorithm, the user must select the initial wound image pixel for the algorithm to grow the region based on that initial properties of the pixel. Basically, if the distance between some considered pixel's probability map and the mean probability map of the region is less than a certain threshold value, that pixel neighbor is added to the growing region. In their subsequent research, [PERSON] et al. (2019) and [PERSON] et al. (2019), alongside with region growing algorithm introduced an optimal thresholding algorithm where the wound was segmented by thresholding the difference matrix of the probability map while taking into account the pixel's tissue type and strength of its probability. Calculation of ground truth agreement between 3 clinicians was taken into account and the overall accuracy of 75.1% was observed on 80 wound images. [PERSON] et al. (2019) focused on the tissue classification task assuming the wound boundary is known. With the fuzzy c-means clustering algorithm, data points are assigned membership value for  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
each cluster and with further analysis, the correspondence to specific class, i.e., tissue is evaluated. An analysis is divided into (i) unsure regions, (ii) sure regions, and (iii) epithelial and granulation tissue. The method is tested on 600 wound images with accuracy rates of images containing all four tissue types of 87.6%, 82.4%, 89.3%, and 82.0% for granulation, slough, eschar, epithelial tissues, respectively. Authors in [PERSON] et al. [9] also focused only on tissue classification task without segmentation of the wound. Training of deep neural network was performed on 105 features based on color, texture, statistics entropy, etc. The neural network performed classification on 250 images plus images from the Medetec database with a very high accuracy rate of 99%. Such accuracy can be explained due to consideration of only the manually delineated wound region of interest, i.e., no process of wound segmentation from the surrounding skin and background. [PERSON] et al. [3] used HSI color space and its S color channel which showed high contrast at the boundary of the wound and skin. With a fuzzy divergence-based thresholding wound boundary was delineated. Five color and ten texture features were extracted from 45 color channels resulting in a total of 675 extracted features from which 50 of them showed to be statistically significant. Supervised Bayesian classifier and SVM classifier were used to learn and classify the three different tissue types. The overall accuracy of the Bayesian classifier was 81.15%, whereas the SVM classifier achieved better accuracy of 86.13%. Methods were tested on 74 wound images from the Medetec database. [PERSON] et al. [88] established a framework called Tele-Wound Technology Network (TWTN) for wound analysis and data storage which focused on capturing the wound images with a smartphone camera to help patients at remote locations to get better treatment promptly.