Colour retinal fundus image registration by selecting stable extremum points in the scale-invariant feature transform detector

A fundamental problem of image registration is the determination of corresponding points. The scale-invariant feature transform (SIFT) is a well-known algorithm in this regard. However, SIFT suffers from quantity, quality and distribution of the detected points when facing with high-resolution and low-contrast colour retinal fundus images. This study introduces an improved SIFT algorithm which identifies adequate, stable and distinctive keypoints with uniform distribution in the overlapped areas. The keypoint of the proposed method is a selection strategy of the difference of Gaussian (DoG) extremum points according to a stability score to guarantee the feature qualities. The stability score is based on the DoG values of extremum points and their vesselness measures in the relevant Gaussian images. Since the selected points lie on the vessels, which are relatively stable between image pairs, the points are unaffected by illumination and content variations of retinal backgrounds. The detected points are introduced to an integrated outlier rejection method. Then, the correspondences determine the geometric transformation parameters. The authors examined quantitatively and qualitatively the performance of this algorithm on four datasets including temporal and partially overlapping image pairs. The experimental results show the outperformance of the approach over similar methods in terms of efficiency, positional accuracy and speed.