Motivated by the need for multimodal image registration in ophthalmology, this paper introduces an algorithm which is tailored to jointly align in a common reference space all the images in a complete fluorescein angiogram (FA) sequence, which contains both red-free (RF) and FA images. Our work is inspired by Generalized Dual-Bootstrap Iterative Closest Point (GDB-ICP), which rank-orders Lowe keypoint matches and refines the transformation, going from local and low-order to global and

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... er model, computed from each keypoint match in succession. Albeit GDB-ICP has been shown to be robust in registering images taken under different lighting conditions, the performance is not satisfactory for image pairs with substantial, non-linear intensity differences. Our algorithm, named Edge-Driven DB-ICP, targeting the least reliable component of GDB-ICP, 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 60 randomly-selected pathological sequences, each on average having up to two RF and 13 FA images. Edge-Driven DB-ICP successfully registered 92.4% of all pairs, and 81.1% multimodal pairs, whereas GDB-ICP registered 80.1% and 40.1%, respectively. For the joint registration of all images in a sequence, Edge-Driven DB-ICP succeeded in 59 sequences, which is a 23% improvement over GDB-ICP.