Abstract
Loss of cone photoreceptor neurons is a leading cause of many blinding retinal diseases. Direct visualization of these cells in the living human eye is now feasible using adaptive optics scanning light ophthalmoscopy (AOSLO). However, it remains challenging to monitor the state of specific cells across multiple visits, due to inherent eye-motion-based distortions that arise during data acquisition, artifacts when overlapping images are montaged, as well as substantial variability in the data itself. This paper presents an accurate graph matching framework that integrates (1) robust local intensity order patterns (LIOP) to describe neuron regions with illumination variation from different visits; (2) a sparse-coding based voting process to measure visual similarities of neuron pairs using LIOP descriptors; and (3) a graph matching model that combines both visual similarity and geometrical cone packing information to determine the correspondence of repeated imaging of cone photoreceptor neurons across longitudinal AOSLO datasets. The matching framework was evaluated on imaging data from ten subjects using a validation dataset created by removing 15% of the neurons from 713 neuron correspondences across image pairs. An overall matching accuracy of 98% was achieved. The framework was robust to differences in the amount of overlap between image pairs. Evaluation on a test dataset showed that the matching accuracy remained at 98% on approximately 3400 neuron correspondences, despite image quality degradation, illumination variation, large image deformation, and edge artifacts. These experimental results show that our graph matching approach can accurately identify cone photoreceptor neuron correspondences on longitudinal AOSLO images.