Eyes are important organs of humans that detect light and form spatial and color vision. Knowing the exact number of
cones in retinal image has great importance in helping us understand the mechanism of eyes’ function and the pathology
of some eye disease. In order to analyze data in real time and process large-scale data, an automated algorithm is
designed to label cone photoreceptors in adaptive optics (AO) retinal images. Images acquired by the flood-illuminated
AO system are taken to test the efficiency of this algorithm. We labeled these images both automatically and manually,
and compared the results of the two methods. A 94.1% to 96.5% agreement rate between the two methods is achieved in
this experiment, which demonstrated the reliability and efficiency of the algorithm.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xu Liu ; Yudong Zhang and Dai Yun
An automated algorithm for photoreceptors counting in adaptive optics retinal images
", Proc. SPIE 8419, 6th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronic Materials and Devices for Sensing, Imaging, and Solar Energy, 84191Z (October 15, 2012); doi:10.1117/12.975947; http://dx.doi.org/10.1117/12.975947