Multimodal Registration of Multiple Retinal Images Based on Line Structures

We propose a framework to perform multimodal registration of multiple images. In retinal imaging, this alignment enables the physician to correlate the features across modalities, which can help formulate a diagnosis. The images appear very different and there are few reliable modality-invariant features. We base our registration on the salient line structures extracted with a tensor-voting approach and aligned to minimize the Chamfer distance. For every pair of images, we match the line junctions and extremities to get a candidate transformation that is further refined with an Iterative Closest Point approach. We use a global chained registration framework to recover from failed registration and we account for non-planarities with a Thin-Plate Splines deformation. Our approach can handle large variations across modalities and is evaluated on real-world retinal images with 5 modalities per eye. We achieve an average error of 52 μm on our dataset.