The Edge-Driven Dual-Bootstrap Iterative Closest Point Algorithm for Registration of Multimodal Fluorescein Angiogram Sequence

Abstract

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 higher-order 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, nonlinear 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.