Red-free (RF) fundus retinal images and fluorescein angiogram (FA) sequence are often captured from an eye for
diagnosis and treatment of abnormalities of the retina. With the aid of multimodal image registration, physicians can
combine information to make accurate surgical planning and quantitative judgment of the progression of a disease. The
goal of our work is to jointly align the RF images with the FA sequence of the same eye in a common reference space.
Our work is inspired by Generalized Dual-Bootstrap Iterative Closest Point (GDB-ICP), which is a fully-automatic,
feature-based method using structural similarity. GDB-ICP rank-orders Lowe keypoint matches and refines the
transformation computed from each keypoint match in succession. Albeit GDB-ICP has been shown robust to image
pairs with illumination difference, the performance is not satisfactory for multimodal and some FA pairs which exhibit
substantial non-linear illumination changes. Our algorithm, named Edge-Driven DBICP, modifies generation of
keypoint matches for initialization by extracting the Lowe keypoints from the gradient magnitude image, and enriching
the keypoint descriptor with global-shape context using the edge points. Our dataset consists of 61 randomly selected
pathological sequences, each on average having two RF and 13 FA images. There are total of 4985 image pairs, out of
which 1323 are multimodal pairs. Edge-Driven DBICP successfully registered 93% of all pairs, and 82% multimodal
pairs, whereas GDB-ICP registered 80% and 40%, respectively. Regarding registration of the whole image sequence in
a common reference space, Edge-Driven DBICP succeeded in 60 sequences, which is 26% improvement over GDB-ICP.