# Fundus Image Registration Technique Based on Local Feature of Retinal Vessels
## 1 Introduction  
Retinal fundus image registration (RIR) is an essential tool in facilitating the diagnosis and treatment of retinal diseases [1]. RIR aligns fundus images according to geometrical transformation estimated from correspondence between fixed and moving images. Existing RIR techniques can be grouped based on the type of correspondence utilized in estimating the geometrical transformation, namely, intensity-based and feature-based.  
The intensity-based RIR technique searches the similarity between the intensity patterns in fixed and moving images to estimate the geometrical transformation. The similarity between the intensity patterns is established using a similarity metric such as mutual information [2], cross-correlation [3] and phase correlation [4, 5]. However, the registration performance of the intensity-based RIR technique is limited in the presence of non-uniform intensity distribution and homogenous texture [6], which is commonly observed in fundus images. Furthermore, the intensity patterns from the non-overlapping area can mislead the similarity metric in estimating inaccurate geometrical transformation.  
Generally, the feature-based RIR technique is more reliable and robust in registering fundus images compared to the intensity-based RIR technique. This is because the feature-based RIR technique estimates the geometrical transformation according to the correspondence of local features such as feature points. However, the feature-based RIR technique requires the feature points to be extracted from reliable information to ensure accurate registration. Reliable information is distributed throughout an image and repeatable despite the changes in viewpoint or intensity [7]. The feature-based RIR technique is mainly comprised of feature extraction, feature descriptor, matching and estimating geometrical transformation. Feature extraction plays a crucial role in ensuring the feature points are detected and selected from reliable information by examining the local patches.  
The feature extraction method in the existing feature-based RIR techniques extracts feature points from retinal vessels [8], vessel bifurcations [9], corner [10], extrema [11; 12; 13] or distinctive structure information [14]. Among this information, the retinal vessel is the most reliable because it can be found throughout the fundus image and is repeatable despite the changes in viewpoint or intensity. Additionally, the appearance of the retinal vessels within the local patches are consistent as a continuous line in 2-dimensional, and curvature shape in 3-dimensional, despite its size and contrast. However, the noises such as the retinal nerve fiber layer, underlying choroidal vessels, microaneurysm and exudates can also appear as curvature shapes in the local patches.  
Therefore, this paper introduces a new feature extraction method based on the local feature of retinal vessels (CURVE). The proposed CURVE extracts feature points throughout the fundus image with the ability to discriminate the aforementioned noises. To register the fundus images, a feature-based RIR technique framework (CURVE-SIFT) is described where CURVE is paired with the scale-invariant feature transform (SIFT) descriptor [15].  
The remainder of this paper is organized as follows. Section 2 highlights and discusses the issues of the feature extraction method in the existing feature-based RIR techniques. Section 3 describes the methodology of the CURVE-SIFT technique. The experimental settings in developing and evaluating the CURVE-SIFT technique are presented in Section 4. Section 5 reports and discusses the experimental results. Finally, the conclusion and future work are given in Section 6.