# Fundus Image Registration Technique Based on Local Feature of Retinal Vessels
## 5 Results
### Registration Performance  
The evaluation continues by accessing the registration performance of CURVE-SIFT and six existing feature-based RIR techniques [8; 9; 10; 13; 14; 16]. From the experimental results outlined in Table 6, CURVE-SIFT successfully registered a total of 59 image pairs in the FIRE dataset with a success rate of 44.030%. The one-way ANOVA analysis shows that the success rate of CURVE-SIFT significantly outperformed GDB-ICP at \(p=0.007\)*, whereas Harris-PIIFD, Ghassabi's-SIFT, H-M 16, H-M 17 and D-Saddle-HOG at \(p<0.001\)*. The biggest success rate difference was observed between CURVE-SIFT and Harris-PIIFD (40.299%), while the smallest difference was with GDB-ICP (16.418%). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Feature Extraction Method**} & \multicolumn{2}{c}{**Image Size**} & \multicolumn{2}{c}{**Intensity Distribution**} \\ \cline{2-5}  & \(r_{s}\) & \(p\)**-Value** & \(r_{s}\) & \(p\)**-Value** \\ \hline Harris corner & \(-0.178\) & 0.041 * & 0.360 & \(<\)0.001 ** \\ SIFT detector & \(-0.649\) & \(<\)0.001 ** & **0.138** & **0.113** \\ SURF & 0.590 & \(<\)0.001 ** & \(-0.398\) & \(<\)0.001 ** \\ Ghassabi’s & \(-0.142\) & 0.104 & 0.314 & \(<\)0.001 ** \\ D-Saddle & \(-0.138\) & 0.114 & 0.386 & \(<\)0.001 ** \\ CURVE & \(-\)**0.032** & **0.712** & 0.342 & \(<\)0.001 ** \\ \hline \hline \end{tabular} _rs_: Spearman’s rho. **: Correlation is significant at the 0.01 level (2-tailed). *: Correlation is significant at the 0.05 level (2-tailed).  
\end{table} 
Table 5: Correlation between extraction accuracy and factors. Moreover, the overall success rate of D-Saddle-HOG (14.085%) reported in this study is much lower than in [14] because this study evaluates D-Saddle performance on the FIRE dataset at the original image size of 2912 \(\times\) 2912 pixels. Contrarily, the work presented in [14] evaluates D-Saddle-HOG performance on the FIRE dataset at the smaller image size of 583 \(\times\) 583 pixels. The extraction accuracy of D-Saddle is insignificantly correlated to the changes in image size, as shown in Table 5. However, D-Saddle-HOG employed a Histogram of Oriented Gradients (HOG) descriptor [55] in its framework where a larger image can decrease the number of correct matches or inliers established between the computed HOG descriptor [56]. Insitfficient amounts of the established inliers can lead to the estimation of inaccurate geometrical transformation. The most noticeable performance of CURVE-SIFT is observed in the image mosaicking application. The image pairs from the image mosaicking application involved the combination of smaller overlapping areas (17-89%) and larger rotation (6\({}^{\circ}\)-52\({}^{\circ}\)) in the dataset. Despite these challenges, the success rate of CURVE-SIFT (53.061%) is significantly outperformed for all existing feature-based RIR techniques (\(p\) < 0.001*). This performance is contributed to by CURVE's ability to accurately extract feature points on retinal vessels and distribute them throughout the image to increase the chances of the inliers being established within the overlapping area. Furthermore, the employed SIFT descriptor has the ability to establish over 60% of inliers when the rotation is below 90\({}^{\circ}\)[57]. These abilities are also expressed in the established Spearman's rank-order correlations in Table 7, where CURVE-SIFT yields smaller Spearman's rho values indicating weaker correlations with the