# A Systematic Overview of Recent Methods for Non-Contact Chronic Wound Analysis
## 2 Performance Metrics and Algorithms Overview  
To calculate the accuracy of a particular method, the results of the method are compared with image ground truth, which is the original image typically labeled by medical experts. True positive (TP), true negative (TN), false positive (FP), and false negative (FN) are obtained by comparing the ground truth image with the resulting image of the given method. Figure 3 shows corresponding terms between the original wound labeled by medical experts and the result of the image processing algorithm. Figure 3: Terms for accuracy evaluation. [PERSON] and [PERSON] [35] evaluated 20 different metrics for image segmentation in medical imaging. Their aim was to provide a standard for evaluating medical image analysis but since metrics have different properties, selecting a suitable metric is not a trivial task. Comparing the results of a certain method, it is possible to evaluate metrics like accuracy, sensitivity or recall, specificity, dice similarity coefficient (DSC), precision, etc., presented by Equations (1)-(5), respectively. \[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{1}\]  
\[Sensitivity=\frac{TP}{TP+FN} \tag{2}\]  
\[Specificity=\frac{TN}{TN+FP} \tag{3}\]  
\[Dice=\frac{2 TP}{2 TP+FP+FN} \tag{4}\]  
\[Precision=\frac{TP}{TP+FP} \tag{5}\]  
In order to increase ground truth precision, labeling should be done by more than one expert so the result is not susceptible to a singular opinion of the individual [8]. Annotated images affect the analysis result and its accuracy rates so it should be done as detailed as possible. The shape of the wound and tissue distribution is usually irregular which creates problems during the annotation of the wound and its tissue samples. Manual marking in a tool like Photoshop [36] can be time-consuming, so some researchers like [9] developed their own software for labeling the specific tissue types. Some research like [PERSON] et al. [37] even calculates the agreement in ground truth labeling between three medical experts to determine the minimum, maximum, and mean value of ground truth agreement. The performed classification gave a result of 75.1% considering that the average agreement between the clinicians was between 67.4 and 84.3%. An important metric of digital image analysis is spent CPU and GPU computational time [36]. By cropping the wound part on the test image or dividing the image into smaller parts (segments) computational time can be reduced significantly. The wound should be estimated by the most important factors which depict wound state like wound size, volume, wound edge, infection, tissue structure, pain, etc. [38]. In imaging, visual data is discretized into image pixels. Similarly, image regions composed of repeated elements are called textures [39]. The most important features a pixel can give are color and texture, if we consider other pixels in the neighborhood, from which valuable information can be extracted and used for classification and clustering [8]. Extracted descriptors or features of image segments help to differentiate between wound or tissue pixels. Researchers select which features are the most useful, as in the study by [PERSON] et al. [40] for the better result of their methods. [PERSON] et al. [9] even calculated 105 features for each pixel and used them for classification in their proposed method. Algorithms for CW image analysis can be divided into supervised and unsupervised, while for reinforcement learning algorithms we were not able to find examples where it was used for any stage of wound assessment. Supervised algorithms need predefined human-labeled training data for image segmentation and classification. A database in supervised learning is often divided into training and testing data, mostly in the ratio 3:1 as in [12]. Typical examples of supervised learning are neural networks (NN), support vector machine (SVM), etc. In unsupervised algorithms it is unknown what data belongs to what cluster, so we can observe it as the grouping of raw data into clusters.