# Fundus Image Registration Technique Based on Local Feature of Retinal Vessels
## 6 Conclusions  
This paper introduces a new feature extraction method known as CURVE for the feature-based RIR technique. The proposed CURVE aims to extract feature points on retinal vessels and throughout the fundus image, which is important to ensure accurate registration of fundus images. However, in the local patches, the noises, such as retinal nerve fiber layer, underlying choroidal vessels, microaneurysm and exudates can also appear similar to retinal vessels. Therefore, CURVE incorporates both characteristics of the retinal vessels and noises in its modules to enable accurate discrimination between them. The ability of CURVE to extract feature points on retinal vessels was demonstrated on the CHASE_DB1 [33,34], DRIVE [35,36], HRF [37,38] and STARE [39,40] datasets. Then, the CURVE performance was compared with five feature extraction methods from the existing feature-based RIR techniques, namely, Harris corner [26], SIFT detector [15], SURF [27,28], Ghassabi's [8] and D-Saddle [14]. From the experiment, CURVE accurately extracts an average of 86.021% of the feature points on retinal vessels and significantly outperformed the existing feature extraction methods (\(p<0.001\)*). Further analysis shows that the impact of image size on CURVE performance is minimal (\(r_{s}=-0.032\), \(p=0.712\)) but significantly affected in the presence of non-uniform intensity distribution in the image (\(r_{s}=0.342\), \(p<0.001\)*). The registration performance when utilizing CURVE feature points in the feature-based RIR technique was demonstrated on the FIRE dataset. CURVE was paired with the SIFT descriptor [41], and the registration performance of CURVE-SIFT was compared with five existing feature-based RIR techniques; GDB-ICP [13], Harris-PIIFD [10], Ghassabi's-SIFT [8], H-M 16 [16], H-M 17 [9] and D-Saddle-HOG [14]. Overall, CURVE-SIFT successfully registered 44.030% of the image pairs in the FIRE dataset, while the success rate of the existing feature-based RIR techniques is less than 27.612%. The one-way ANOVA analysis showed that CURVE-SIFT is 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 \(p<0.001\)*. CURVE  
Figure 15: Examples of the successfully registered image pairs for CURVE-SIFT in the FIRE dataset. The green markers are inliers on the fixed image, while red/blue markers are inliers on moving image. Right images: Close-up for yellow square area as checkerboard image containing alternating rectangular regions from fixed image and moving image. SIFT obtained the highest success rate (53.061%) in the image mosaicking application, while the success rates of the existing feature-based RIR techniques were only between 0% to 32.653%. The image mosaicking application consists of image pairs with smaller overlapping areas compared to other applications in the FIRE dataset. Thus demonstrating the ability of CURVE to extract feature points on retinal vessels throughout the image. This is crucial to increase the chances of the inliers being established within the overlapping area to estimate an accurate geometrical transformation. In the future, we will focus our efforts to improve CURVE in extracting feature points from fundus images with non-uniform intensity distribution. Moreover, we will explore the possibility of a fusion strategy to combine deep convolutional neural network (CNN) with local feature point for feature extraction [58]. However, at the time of this study, the size of the public RIR dataset was small, which may result in model overfitting or underfitting [59]. The study will begin once a larger dataset or suitable pre-trained model for RIR is available publicly. Conceptualization, R.R. and N.K.A.K. ; methodology, R.R. and M.Y.L.I. ; software, R.R. and A.W.A.W. ; validation, R.R. and K.H. ; investigation, R.R., M.Y.L.I., K.H., A.W.A.W. and N.K.A.K. ; writing--original draft preparation, R.R. and M.Y.L.I; writing--review and editing, R.R. and K.H. All authors have read and agreed to the published version of the manuscript.