# Fully Leveraging Deep Learning Methods for Constructing Retinal Fundus Photomatages
## 3 Experimental Results
### Qualitative Evaluation  
We first present a qualitative comparison between a sample result for the different preprocessing methods in Figure 6. The min/max normalization can in this case increase their differences due to the presence and absence of the optic disc in the frame pair, which results in insufficient keypoint matches. For the modified top-hat transform of [28], the preprocessing mostly enhances local textures, which are not distinctive enough to be used as keypoint matches. The proposed preprocessing method is not affected by the presence or absence of the optic disc, and it enhances vessel textures that are suitable to be applied as keypoints. We present comparative qualitative evaluations for three methods, namely, the montage function of KOWA's VK-2 [29], the AutoStitch application [30], and the proposed \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Preprocessing** & **Frame Sorting Criterion** & **Avg. Frames (std)** & \(p\)**-Value \({}^{\ddagger}\)** & **\% of Frames** & **Avg. TRE (std)** \\ \hline Min/max norm. & Number of keypoint matches & 3.83 (1.48) & \(2.96\times 10^{-16}\) & 51.87\% & 26.16 (30.93) \\ Min/max norm. & Optic disc/fovea detection & 3.62 (1.41) & \(9.66\times 10^{-19}\) & 49.28\% & 24.97 (33.23) \\ Modified top-hat [28] & Optic disc/fovea detection & 5.1 (1.98) & \(1.12\times 10^{-4}\) & 69.33\% & 24.22 (30.1) \\ Vessel contrast & Number of keypoint matches & 6.04 (1.31) & 0.24 & 82.82\% & 23.98 (30.41) \\ Vessel contrast \({}^{\ddagger}\) & Optic disc/fovea detection & 6.34 (1.46) & \(-\) & 86.25\% & 23.67 (31.1) \\ \hline \hline \end{tabular}  
* P-value: the p-value of the null hypothesis for the number of frames measured by the paired t-test of comparative methods and the proposed method in last row. \(\ddagger\) This row refers to the proposed method.  
\end{table} 
Table 1: Comparison of preprocessing frame sorting methods for constructing a photomontage on 62 image sequences. Figure 6: Qualitative comparison of results from different preprocessing methods: min/max normalization where pixel values are rescaled, so that the minimum and maximum values of each image are normalized, modified top-hat transform proposed in [28], and the proposed method. The lower row shows the results of applying the SURF method [12] for keypoint matching on the corresponding input frame pairs above. method. Figures 7 and 8 show the results where all of the methods generate montages of similar size, but with differences in the quality of the generated montage. Overall, all three methods seem to generate similar montages, as depicted in (a-c); we can see that the proposed method reduces artifacts that occur in other methods, as depicted in the zoomed view of local artifact regions in (d-g). Figure 7d and Figure 8d, ghosting artifacts are visible from the results of the KOWA VK-2 montage, which are corrected in the results of the proposed method in Figure 7e and Figure 8e. Similarly, in Figure 7f and Figure 8f, misalignment causes ghosting of the same blood vessel from the results of AutoGitch, which are corrected in Figure 7g and Figure 8g. In Figure 9, we present more challenging image sets where the comparative methods fails, leading to insufficient coverage of the montage and various artifacts. For the montages of the KOWA VK-2, many artifacts are evident, even within the insufficiently formed montage. For the results of AutoGitch, fewer artifacts are visible, but it seems that only two or three frames have been integrated. The proposed method is able to generate a more complete montage when compared to these methods. Figure 8: Retinal photomontages constructed by (**a**) the montage function of KOWA VK-2 [29], (**b**) AutoStitch [30], and (**c**) the proposed method, respectively. Local regions highlighted as boxes in (**a**–**c**) are enlarged in (**d**–**g**). (**d**) and (**f**) show the comparison between KOWA VK-2 montage and the proposed method, and (**e**,**g**) show the comparison between AutoStitch and the proposed method, respectively.