An automated image processing system for the detection of photoreceptor cells in adaptive optics retinal images

This paper presents an automated image processing framework for facilitating the accurate detection of photoreceptor cells. The performance of the proposed method was evaluated in terms of cone density calculated on synthetic and high-resolution retinal images. The validation study on the synthetic data showed an average accuracy of 98.8% for the proposed method in comparison with 93.9% obtained by the Li and Roorda algorithm. The cone density calculated on the high-resolution retinal images demonstrated satisfactory agreement with the histological data as well as previously published data on photoreceptor packing density at a given location.