# A Systematic Overview of Recent Methods for Non-Contact Chronic Wound Analysis
## 8 Discussion  
As previously mentioned, the topic of this paper is to review recent methods for wound analysis that could lead to development of an end-to-end solution for automatic wound analysis. Such a solution would be a rather complex system with requirements to automatically detect and segment the wounds from the background and surrounding healthy tissue. Furthermore, the system should be able to classify the assortment of tissue types found on the wound surface and in the end produce measurements, such as wound dimensions, percentage of detected tissues which leads to healing evaluation, and therapy suggestions. Additionally, the system analysis could also result with the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Paper**} & \multicolumn{2}{c}{**Preprocessing/S Segmentation/Prediction/Healing Prediction/Measurement**} & \multirow{2}{*}{**Image Database**} & \multicolumn{2}{c}{**Performance**} & \multirow{2}{*}{**Avg. Time (t)**} \\  & **Classification** & & & & \\ \hline [PERSON] et al., [104] 2019. & CIELab color space/Color thresholding and clustering/ & Structure from motion/Marker with math. model & 3 image sequences & Error length 4.25\%, area 15.3\%, value 17.46\% & N/A \\ \hline [PERSON] et al., [105] & Modified HSV color space/Manually computed/Histogram thresholding & \(\cdot\)/Wound healing index & 119 images of one wound during 90 days & N/A & N/A \\ \hline [PERSON] et al., [106] 2019. & CLAHE/Gradient boosted trees (GBT) classifier & \(\cdot\)/Healing assessment index based on differences between tissue type in consecutive images/- & 377 images (36 patches)  
detailed 3D reconstruction of the wound which could be used for further analysis and follow-up comparisons during examinations. In order to produce good results in wound segmentation and tissue classification stages, most considered research have some form of input data preprocessing. Input data in this field of study is usually visual data in the form of wound photographs which are sometimes accompanied with corresponding depth images as well. Preprocessing usually involves noise removal and sometimes blurring in order to remove image encoding artefacts, reflections, etc., and therefore improve segmentation or wound detection phases. Preprocessing also typically involves choosing of the most appropriate color space. HSV is the traditional choice whose performance has been proven in numerous fields including wound analysis, with CIE Lab being the second most used. Recently, some other color spaces such as YCbCr and YDbDr that originate from video transmission encoding have also been successfully used for wound analysis, and researchers have shown that these color spaces are particularly suitable for distinguishing between wound and non-wound image areas [8; 11]. In case deep neural networks are used, preprocessing usually involves image cropping or image resizing in order to reduce the image to the size appropriate for the particular deep neural network architecture [44; 65]. Some wound analysis systems also use presegmentation as a preprocessing step where the input image is divided into a form of superpixels which can be regularized into a grid or arbitrary shapes by using an algorithm such as a SLIC, MeanShift, QuickShft, etc. By using the aforementioned algorithms researchers gain a desired number of regions with uniform properties which they can rapidly analyze and determine their purpose w.r.t wound analysis process. Separating the wound surface from the background, specifically from the surrounding healthy tissue can be challenging task especially if the surrounding tissue has recently healed and its surface color can often resemble that of the wound. If the wound segmentation is conducted on 2D images, then underlining surface geometry is not available.