# Fundus Image Registration Technique Based on Local Feature of Retinal Vessels
## 5 Results  
### Feature Extraction Performance  
CURVE extracts an average of 2482 feature points from the CHASE, DRIVE, HRF and STARE datasets, where 2149 of them are accurately associated with retinal vessels. This constitutes an average feature extraction accuracy of 86.021% with a variation of 9.199% between images, as outlined in Table 4. Furthermore, the one-way ANOVA analysis shows that the feature extraction accuracy of CURVE was significantly outperformed by all existing feature extraction methods (\(p\) < 0.001*). CURVE obtained the biggest accuracy difference with SIFT detector (69.857%) and the smallest difference with Harris corner (44.408%). Examples of CURVE feature points extracted from four datasets are depicted in Figure 14.  
The high feature extraction accuracy of CURVE is contributed to by the utilization of both the retinal vessels and noise characteristics in the feature detection and selection modules. Thus, enabling accurate discrimination between the retinal vessels and noises. Contrarily, [PERSON]'s and D-Saddle enhanced the fundus image to increase the visibility of the retinal vessels. However, this enhancement also increases the visibility of the noise, which led both methods to yield low feature extraction accuracy. The extracted feature points located on the noises for these methods were observed to be on the edge of the optic disc, retinal nerve fiber layer, underlying choroidal vessels and macula. The other feature extraction methods such as Harris corner, SIFT detector and SURF are without a specific feature selection module to extract feature points on retinal vessels. These feature extraction methods were used in the existing feature-based RIR techniques [10; 13; 16], where the authors focused on the development of the feature descriptor and transformation model.  
Other than that, the minimal usage of rigid thresholds or variables allows CURVE to accurately extract feature points from fundus images with varying sizes. This is shown by the smallest Spearman's rho among all methods and insignificant correlation between the changes in image size and the extraction accuracy of CURVE (\(r_{s}=-0.032\), \(p=0.712\)) as presented in Table 5. Furthermore, the extraction accuracy of D-Saddle (\(r_{s}=-0.138\), \(p=0.114\)) and Ghassabi's (\(r_{s}=-0.142\), \(p=0.104\)) exhibit insignificant correlation with the changes in image size but their correlations are stronger than CURVE. In contrast, SIFT detector is very sensitive to the changes in image size among all methods where its feature extraction accuracy decreases in larger images (\(r_{s}=-0.649\), \(p<0.001\)*).  
However, CURVE performance is significantly affected in the presence of non-uniform intensity distribution in the image (\(r_{s}=0.342\), \(p<0.001\)*). CURVE is sensitive towards the non-uniform intensity distribution because it highly depends on the intensity changes to locate the curvature of the retinal vessels in the feature detection module. Furthermore, CURVE does not incorporate any feature enhancement algorithm. The feature enhancement algorithms, such as DoG and _i_IDoG operators, can suppress the non-uniform intensity distribution and increase the visibility of the retinal vessels but at the cost of increasing the visibility of the noises. Thus, it is avoided in the proposed CURVE. Contrarily, the correlation between SIFT detector and the intensity distribution is not significant and the weakest among all feature extraction methods (\(r_{s}=0.138\), \(p=0.113\)).