# Fundus Image Registration Technique Based on Local Feature of Retinal Vessels
## 5 Results
### Registration Performance
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature-Based** & \multicolumn{2}{c}{**Total**} & \multirow{2}{*}{**Mean**} & \multicolumn{2}{c}{**Standard**} & \multicolumn{2}{c}{**TRE (Pixels)**} \\ \cline{3-6}
**RIR Technique** & & & & **Deviation** & **Min** & **Max** \\ \hline \multicolumn{6}{c}{**Overall**} \\ \hline \multicolumn{6}{c}{**GDB-ICP**} & 37 & 27.612 & 44.875 & 2.354 & 10.416 \\ Harris-PIIFD & 5 & 3.731 & 19.024 & 3.319 & 1486.255 \\ Ghassabi’s-SIFT & 17 & 12.687 & 33.407 & 3.082 & 322.616 \\ H-M 16 & 22 & 16.418 & 37.183 & 2.857 & 410.087 \\ H-M 17 & 26 & 19.403 & 39.694 & 2.920 & 60.875 \\ D-Saddle-HOG & 16 & 11.940 & 32.548 & 4.583 & 27.266 \\ CURVE-SIFT & 59 & **44.030** & 49.829 & 1.928 & 1016.330 \\ \hline \multicolumn{6}{c}{**Super-resolution**} \\ \hline \multicolumn{6}{c}{**GDB-ICP**} & 17 & 23.944 & 42.978 & 0.486 & 4.575 \\ Harris-PIIFD & 2 & 2.817 & 16.663 & 0.785 & 12.850 \\ Ghassabi’s-SIFT & 13 & 18.310 & 38.950 & 0.665 & 15.798 \\ H-M 16 & 18 & 25.352 & 43.812 & 0.554 & 13.903 \\ H-M 17 & 20 & 28.169 & 45.302 & 0.489 & 5.696 \\ D-Saddle-HOG & 10 & 14.085 & 35.034 & 0.748 & 9.327 \\ CURVE-SIFT & 28 & **39.437** & 49.219 & 0.613 & 9.696 \\ \multicolumn{6}{c}{**Image Mosaicking**} \\ \hline \multicolumn{6}{c}{**GDB-ICP**} & 16 & 32.653 & 47.380 & 1.946 & 6.323 \\ Harris-PIIFD & 0 & 0.000 & 0.000 & 10.041 & 3870.632 \\ Ghassabi’s-SIFT & 0 & 0.000 & 0.000 & 7.358 & 578.494 \\ H-M 16 & 0 & 0.000 & 0.000 & 7.976 & 129.658 \\ H-M 17 & 1 & 2.041 & 14.286 & 3.327 & 41.192 \\ D-Saddle-HOG & 2 & 4.082 & 19.991 & 3.082 & 366.401 \\ CURVE-SIFT & 26 & **53.061** & 50.423 & 1.787 & 19.799 \\ \multicolumn{6}{c}{**Longitudinal Study**} \\ \hline \multicolumn{6}{c}{**GDB-ICP**} & 4 & 28.571 & 46.881 & 2.354 & 10.416 \\ Harris-PIIFD & 3 & 21.429 & 42.582 & 3.319 & 1486.255 \\ Ghassabi’s-SIFT & 4 & 28.571 & 46.881 & 3.082 & 322.616 \\ H-M 16 & 4 & 28.571 & 46.881 & 2.857 & 410.087 \\ H-M 17 & 5 & **35.714** & 49.725 & 2.920 & 60.875 \\ D-Saddle-HOG & 4 & 28.571 & 46.881 & 4.583 & 27.266 \\ CURVE-SIFT & 5 & **35.714** & 49.725 & 1.928 & 1016.330 \\ \hline \hline \end{tabular} \({}^{\dagger}\) Total image pairs with successful registration.  
\end{table} 
Table 6: Success rate (%) in the FIRE dataset. overlapping area and rotation compared to Harris-PIIFD, Ghassabi's, H-M 16, H-17 and D-Saddle. In contrast, the existing feature-based RIR techniques recorded a much lower success rate with less than 32.653%, whereas Harris-PIIFD, Ghassabi's and H-M 16 were unable to register any of the image pairs in the image mosaicking application. The image pairs from the super-resolution application are the least challenging in the FIRE dataset as they involve a large overlapping area (86-100%) and small rotation (0\({}^{\circ}\)-12\({}^{\circ}\)). However, the super-resolution application requires a very accurate registration with a TRE of less than one pixel. For this reason, CURVE-SIFT only recorded a success rate of 39.437% in this application, where the TRE of the failed registration ranged between 1.003 pixels to 9.696 pixels. The success rate of CURVE-SIFT outperformed all existing feature-based RIR techniques evaluated in this study but only significant with Harris-PIIFD (\(p<0.001\)*), Ghassabi's-SIFT (\(p=0.030\)*) and D-Saddle-HOG (\(p=0.004\)*). The image pairs from the longitudinal study application are the most challenging for CURVE-SIFT to register, where it obtained the lowest success rate (35.714%) among the applications in the FIRE dataset. Furthermore, no significant difference can be noted between the success rate of CURVE-SIFT and existing feature-based RIR techniques. This shows that the registration performance of CURVE-SIFT is affected when the anatomical appearance is varied between images in the pair. Particularly, CURVE-SIFT failed to register image pairs when the prominent differences of vessel thickness and tortuosity were observed between images.