# Fundus Image Registration Technique Based on Local Feature of Retinal Vessels
## 2 Related Works  
The majority of the existing feature-based RIR techniques [13; 16; 17; 18] mainly utilized the SIFT detector [15] to extract the feature points. SIFT detector finds extrema from local patches in a hierarchical difference of Gaussian (DoG) scale-space to allow feature points to be found based on the structure of various sizes. Then, the extrema that are low contrast and on edges are rejected to ensure the final feature points are distinctive and repeatable. However, the retinal vessels exhibit inconsistent contrast levels throughout the fundus image. Therefore, [PERSON] et al. [11] utilized robust uniform SIFT (UR-SIFT) [19] to overcome this issue.  
The UR-SIFT is an improvement of the SIFT detector, where the feature points are selected according to the strength of the texture surrounding the points. This enables UR-SIFT to be more efficient in extracting feature points on retinal vessels compared to the standard SIFT detector. Furthermore, UR-SIFT ensures the extracted feature points are distributed throughout the hierarchical DoG scale space. The distribution is set in reverse from the scale coefficients of the scale space. This results in more feature points being extracted in the lower part of the hierarchical DoG scale space where the images are larger and finer. Opposite to this, fewer feature points are extracted in the upper part of the hierarchical DoG scale space where the images are smaller and coarser.  
[PERSON] et al. further improved their work by introducing a stability score as part of the selection criterion [8]. The stability score incorporates [PERSON]'s vesselness measure (FVM) [20], a vessel enhancement filter that suppresses noise in the image. Incorporating FVM enabled the ability of [8] to discriminate between retinal vessels and noises.  
Extracting feature points on retinal vessels from the underexposed region in the fundus image is addressed in [12], where the illumination invariant Difference of Gaussian (_ii_DoG) operator was incorporated into the hierarchical scale space [21]. _ii_DoG operator is composed of normalized difference of Gaussian (_n_DoG) and DoG operators based on a piecewise function. The combination of these operators increases the visibility of the underexposed region while leaving the correctly exposed region unchanged. This work utilized a similar approach as in SIFT detector to extract extrema from the hierarchical _ii_DoG scale space. A threshold is introduced to discard the extrema on the retinal surface before the final feature points are selected. The threshold is based on the distribution of the intensity in the local patch.  
Other than SIFT, the existing feature-based RIR techniques [10; 22; 23; 24] extract geometric corner [25], Harris corner [26] and speeded up robust features (SURF) [27; 28]. Meanwhile, [PERSON] et al. [14] introduced D-Saddle to extract feature points from the low-quality region based on distinctive structural information.  
There are several issues that can be outlined from the highlighted feature extraction methods. First, feature enhancement algorithms such as DoG and _ii_DoG operators are mainly incorporated in building the hierarchical scale space. These operators increase the visibility of the retinal vessels as well as the noises, which make it more challenging for the feature extraction method to discriminate between them.  
Second, the feature extraction methods are mainly without a proper selection module to select feature points on retinal vessels. A proper selection module should consider both retinal vessels and noise information as they may appear similarly within a local patch. Therefore, considering both in the selection module allows for more robust discrimination between retinal vessels and noises.