## 4 Discussion  
HS images allow the acquisition of a large number of bands with spectral information, which is collected for each pixel in the image. The HSI technology scans beyond the skin surface, revealing what is not visible with the human eye. This can be beneficial for surgical guidance, when accurate and continuous patient tracking is needed. Therefore, HSI can offer an attractive solution for noninvasive skin feature detection and patient position tracking. The currently available surgical navigation systems use either invasive or noninvasive reference markers or dynamic reference frames for patient position tracking. However, tracking can be compromised if the markers are covered or displaced during surgery, resulting in navigational inaccuracies; thus, using inherent skin features instead of markers can offer a potential improvement of patient position tracking and robustness for surgical navigation. In this work, we have presented a human-subject study, where HSI was used to detect and match features while the subject was breathing normally. The 2D displacements between matched skin features were measured and benchmarked with adhesive optical markers. A mean error of 0.25 mm over all subjects was found when deep local features (DELF) were detected, while the system performance decreased when SURF was used as a handcrafted feature detection algorithm. The HS images involve large data sizes in the range of Gigabytes, requiring high-performance computing power to facilitate a feasible processing time [47]. To increase the feasibility of the approach, we have evaluated different algorithms (PCA, SSBS, VGBS) for the extraction of the most informative bands to perform skin feature detection and matching. We have compared the results of extracting the most meaningful bands when PCA and two different band selection algorithms--VGBS and SSBS-were used. PCA is well established as a dimensionality reduction method for HS images; however, it leads to a loss of meaningful spectral information. We found that the SSBS method, which selects the spectral bands based on the number of local extrema, gave the best results. One of the major reasons for this result is that the conceptual idea of finding extreme local points is the basis for a scale-selection algorithm which is employed to find local features in an image. In order to demonstrate the added value of detecting features with an HS camera, we synthesized RGB color images from the HS images for comparison. HSI images were transformed into color images, after which DELF was applied to detect and match the best possible skin features. However, the 2D skin feature localization accuracy decreased. It should be noticed that the image acquisition system does not operate in real-time. A postprocessing phase is required to create the HS data cube for each image acquisition. Figure 10: DELF correspondences after RANSAC geometric verification for subject no. 3 in the RGB-transformed image. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c} \hline \hline
**Subject No.** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** & **14** & **15** & **16** & **17** \\ \hline
**Error (RGB-SURF) (mm)** & 0.76 & 0.40 & 0.32 & 0.67 & 0.23 & n.a\({}^{*}\) & n.a\({}^{*}\) & n.a\({}^{*}\) & 0.89 & 1.35 & n.a\({}^{*}\) & 1.69 & 1.68 & 1.29 & 0.86 & 0.97 & 0.65 \\
**Error (RGB-DELF) (mm)** & 0.96 & 0.91 & 0.96 & 0.67 & 0.20 & n.a\({}^{*}\) & n.a\({}^{*}\) & n.a\({}^{*}\) & 0.73 & n.a\({}^{*}\) & n.a\({}^{*}\) & 1.24 & 1.51 & 1.35 & 0.48 & 1.55 & 1.04 \\
**Error (HSI-DELF-SSBS) (mm)** & 0.22 & 0.21 & 0.24 & 0.32 & 0.35 & 0.20 & 0.15 & 0.19 & 0.23 & 0.28 & 0.25 & 0.19 & 0.31 & 0.28 & 0.20 & 0.25 & 0.28 \\ \hline \hline \end{tabular} \({}^{a}\) Not applicable. The algorithm failed when applied to subjects 7, 8, 9 and 12, identified as n.a. in the Table.  
\end{table} 
Table 3: Results of 2D mean error per subject for all the detected skin features using RGB-transformed images.