Improving cone identification using merged non-confocal quadrant-detection adaptive optics scanning light ophthalmoscope images.

Cone photoreceptor inner segments visualized in non-confocal split-detection adaptive optics scanning light ophthalmoscope (AOSLO) images appear as obliquely illuminated domes with bright and dark opposing regions. Previously, the pairing of these bright and dark regions for automated photoreceptor identification has necessitated complex algorithms. Here we demonstrate how the merging of split-detection images captured with a non-confocal quadrant light detection scheme allows automated cone identification using simple, open-source image processing tools, while also improving accuracy in both normal and pathologic retinas.