## 4 Results and Discussion
### Sensitivity of the Proposed Approach to the Parameter Settings  
The next test is concerned with the setting of the number \(N_{bin}\) of bins, used in the histogram evaluation. As already mentioned, the number of bins, used to construct the histogram should attain a suitable trade-off between numerical stability of the distance measurement and its discriminating capability. Table 6 summarizes the obtained results using the cosine distance in Equation (3). In this test, we again use \(N_{T}=500\) target images, while the constant \(\eta\), in Equation (2), has been set to \(\eta=2\) for the CT dataset and to \(\eta=1.4\) for the CXRs, respectively. Table 6 shows that the performance of the proposed approach is quite independent of the choice of the number \(N_{bins}\) of histogram bins for the CXR dataset, since it remains quite stable. However, its effect is more noticeable for the CT dataset. Although the standard deviation \(\sigma_{d}\) decreases with a smaller number of bins, the best performance in terms of all the considered metrics is obtained with a number of bins from 50 to 100 in the case of CT dataset. Hence, in order to obtain the best performance in both the datasets and maintain a limited computational complexity, we select \(N_{bin}=50\) definitively. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \(N_{bins}\) & \(d_{m}\) & \(\sigma_{d}\) & \(TH\) & **Accuracy** & **Precision** & **Recall** & **F-Measure** \\ \hline \multicolumn{8}{c}{**Computed Tomography (\(\eta=2\))**} \\ \hline
5 & 0.0222 & 0.0169 & 0.0560 & 0.7840 & 0.7927 & 0.7840 & 0.7824 \\
10 & 0.0331 & 0.0258 & 0.0847 & 0.9570 & 0.9604 & 0.9570 & 0.9569 \\
25 & 0.0453 & 0.0441 & 0.1335 & 0.9880 & 0.9883 & 0.9880 & 0.9880 \\
50 & 0.0544 & 0.0664 & 0.1872 & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\
100 & 0.0558 & 0.0832 & 0.2222 & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\
250 & 0.0646 & 0.1225 & 0.3096 & 0.9960 & 0.9960 & 0.9960 & 0.9960 \\
500 & 0.0764 & 0.1454 & 0.3672 & 0.9960 & 0.9960 & 0.9960 & 0.9960 \\ \hline \multicolumn{8}{c}{**X-Rays (\(\eta=1.4\))**} \\ \hline
5 & 0.0005 & 0.0045 & 0.0068 & 0.9260 & 0.9261 & 0.9260 & 0.9260 \\
10 & 0.0133 & 0.0143 & 0.0333 & 0.9880 & 0.9883 & 0.9880 & 0.9880 \\
25 & 0.0236 & 0.0259 & 0.0598 & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\
50 & 0.0339 & 0.0325 & 0.0794 & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\
100 & 0.0076 & 0.0380 & 0.0608 & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\
250 & 0.0122 & 0.0430 & 0.0724 & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\
500 & 0.0135 & 0.0568 & 0.0931 & 0.9898 & 0.9898 & 0.9898 & 0.9898 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results obtained by the proposed approach for different number \(N_{bin}\) of histogram bins. The reported performance metrics refer to the cosine distance at \(N_{T}=500\) reference images. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Model** & \(d_{m}\) & \(\sigma_{d}\) & \(TH\) & **Accuracy** & **Precision** & **Recall** & **F-Measure** \\ \hline \multicolumn{8}{c}{**Computed Tomography (\(\eta=2\))**} \\ \hline
**Proposed (Cosine)** & 0.0458 & 0.0436 & 0.1330 & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\
**Proposed (KL)** & 0.1231 & 0.1044 & 0.3319 & 0.9949 & 0.9950 & 0.9949 & 0.9949 \\
**Proposed ([PERSON])** & 0.0048 & 0.0983 & 0.2014 & 0.9870 & 0.9870 & 0.9870 & 0.9870 \\
**Proposed (\(\chi^{2}\))** & 0.1783 & 0.0970 & 0.3723 & 0.9747 & 0.9748 & 0.9747 & 0.9747 \\ \hline \multicolumn{8}{c}{**X-Rays (\(\eta=1.4\))**} \\ \hline
**Proposed (Cosine)** & 0.0283 & 0.0292 & 0.0692 & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\
**Proposed (KL)** & 0.0018 & 0.0509 & 0.0731 & 0.9898 & 0.9898 & 0.9898 & 0.9898 \\
**Proposed ([PERSON])** & 0.0023 & 0.0415 & 0.0604 & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\
**Proposed (\(\chi^{2}\))** & 0.0015 & 0.0398 & 0.0572 & 0.9870 & 0.9870 & 0.9870 & 0.9870 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results obtained by the proposed approach under the two considered datasets.