  Retinal template matching and registration is an important challenge in
teleophthalmology with low-cost imaging devices. However, the images from such
devices generally have a small field of view (FOV) and image quality
degradations, making matching difficult. In this work, we develop an efficient
and accurate retinal matching technique that combines dimension reduction and
mutual information (MI), called RetinaMatch. The dimension reduction
initializes the MI optimization as a coarse localization process, which narrows
the optimization domain and avoids local optima. The effectiveness of
RetinaMatch is demonstrated on the open fundus image database STARE with
simulated reduced FOV and anticipated degradations, and on retinal images
acquired by adapter-based optics attached to a smartphone. RetinaMatch achieves
a success rate over 94\% on human retinal images with the matched target
registration errors below 2 pixels on average, excluding the observer
variability. It outperforms the standard template matching solutions. In the
application of measuring vessel diameter repeatedly, single pixel errors are
expected. In addition, our method can be used in the process of image
mosaicking with area-based registration, providing a robust approach when the
feature based methods fail. To the best of our knowledge, this is the first
template matching algorithm for retina images with small template images from
unconstrained retinal areas. In the context of the emerging mixed reality
market, we envision automated retinal image matching and registration methods
as transformative for advanced teleophthalmology and long-term retinal
monitoring.
