## 4 Evaluation
### Feature Extraction

Table 3: F1 score results of the test series with normalized image segments (scaled ROI) for k-nearest neighbors (KNN) and radial basis function (RBF) modes. with an aspect ratio larger than 5, and most aspect ratios were below 1. This fact could be used to increase the available resolution for the significant range of values. We investigated the performance of three different transfer functions in combination with the geometric contour features (Table 5). For each transfer function, we first identified the actual range of values in the training dataset and defined an expected value range. Values outside this expected range would be mapped to 0 for values lower than expected and 255 for higher values. Figure 8 shows these transfer functions and the histogram of the original and resulting values for the example of the aspect ratio. The first transfer function is a linear transformation, normalizing the expected values into the range between 0 and 255. Figure 8 shows that the shape and distribution of the histogram remained unchanged. The second transfer function we investigated was the histogram equalization. Figure 8 shows that the resulting histogram then spread over the entire value range, which should have resulted in a more efficient use of the available feature size. However, histogram equalization is computation-intensive. As a compromise between efficient linearization and histogram equalization, we investigated a third transfer function: partial linearization. For this, we first identified the 10 th and 90 th percentiles and defined a normalization for the equivalent value range in the feature vector. The results of partial linearization are presented in Figure 8.  
**Moments**: The last method of feature extraction we investigated was the use of image moments. In addition to the common moments defined by Equation (5), multiple subsets of  
Figure 8: Visualization of the transfer function and the value range before and after mapping at the example for the aspect ratio. (**a**) Original value range and transfer functions (**b**) with linear mapping, (**c**) with histogram equalization, and (**d**) with partial linear mapping. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{2}{c}{**Linear**} & \multicolumn{2}{c}{**Part. Linear**} & \multicolumn{2}{c}{**Hist. Euq.**} \\ \cline{2-7}  & **RBF** & **KNN** & **RBF** & **KNN** & **RBF** & **KNN** \\ \hline Median & 68 & 70 & 68 & 69 & 28 & 30 \\ Sigma & 1.3 & 1.2 & 1.0 & 1.6 & 3.7 & 3.9 \\ \hline \end{tabular}
\end{table} 
Table 5: F1 score results of the test series with contour properties. moments can be derived with different properties [15]. We investigated three additional sets of moments: (1) the translation-invariant central moments calculated using Equation (7), (2) the scaling-invariant normalized moments (see Equation (8)), and (3) the rotation-invariant Hu moments (see Equations (9)-(15)):  
\[M_{\bar{\mu}} =\sum_{x,y}pv_{x,y}\cdot x^{j}\cdot y^{i} \tag{5}\] \[\mu_{ji} =\sum_{x,y}pv_{x,y}\cdot(x-\bar{x})^{j}\cdot(y-\bar{g})^{i}\] (6) \[\bar{x} =\frac{M_{10}}{M_{00}}\quad;\quad\bar{y}=\frac{M_{01}}{M_{00}}\] (7) \[\eta_{ji} =\frac{\mu_{ji}}{M_{00}^{(i+j)/2+1}}\] (8) \[I_{1} =\eta_{20}+\eta_{02}\] (9) \[I_{2} =(\eta_{20}-\eta_{02})^{2}+4\eta_{11}^{2}\] (10) \[I_{3} =(\eta_{30}-3\eta_{12})^{2}+(3\eta_{21}-\eta_{03})^{2}\] (11) \[I_{4} =(\eta_{30}+\eta_{12})^{2}+(\eta_{21}+\eta_{03})^{2}\] (12) \[I_{5} =(\eta_{30}-3\eta_{12})(\eta_{30}+\eta_{12})[(\eta_{30}+\eta_{12 })^{2}-3(\eta_{21}+\eta_{03})^{2}]\] (13) \[M_{\bar{\mu}} =\sum_{x,y}pv_{x,y}\cdot x^{j}\cdot y^{i} \tag{5}\]0 \[M_{\bar{\mu}} =\sum_{x,y}pv_{x,y}\cdot x^{j}\cdot y^{i} \tag{5}\]1 (14) \[M_{\bar{\mu}} =\sum_{x,y}pv_{x,y}\cdot x^{j}\cdot y^{i} \tag{5}\]2 (15) \[M_{\bar{\mu}} =\sum_{x,y}pv_{x,y}\cdot x^{j}\cdot y^{i} \tag{5}\]3  
In the following, we used moments up to the third order and omitted moments that were either duplicates or constants, as these contained no additional information.