title: Photoreceptor counting and montaging of en-face retinal images from an adaptive optics fundus camera.
abstract: A fast and efficient method for quantifying photoreceptor density in images obtained with an en-face flood-illuminated adaptive optics (AO) imaging system is described. To improve accuracy of cone counting, en-face images are analyzed over extended areas. This is achieved with two separate semiautomated algorithms: (1) a montaging algorithm that joins retinal images with overlapping common features without edge effects and (2) a cone density measurement algorithm that counts the individual cones in the montaged image. The accuracy of the cone density measurement algorithm is high, with >97% agreement for a simulated retinal image (of known density, with low contrast) and for AO images from normal eyes when compared with previously reported histological data. Our algorithms do not require spatial regularity in cone packing and are, therefore, useful for counting cones in diseased retinas, as demonstrated for eyes with Stargardt's macular dystrophy and retinitis pigmentosa.
authors: Department of Ophthalmology and Vision Science, University of California Davis, Sacramento 95817, USA. bxue@ucdavis.edu
authors: Bai
authors: B
authors: Xue
authors: Stacey S
authors: SS
authors: Choi
authors: Nathan
authors: N
authors: Doble
authors: John S
authors: JS
authors: Werner
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002452:Cell Count / Q000379:methods; D016000:Cluster Analysis; D005451:Fluorescein Angiography / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007909:Lenses; D010363:Pattern Recognition, Automated / Q000379:methods*; D010786:Photoreceptor Cells / Q000033:anatomy & histology*; D015203:Reproducibility of Results; D042261:Retinoscopes; D042262:Retinoscopy / Q000379:methods*; D012680:Sensitivity and Specificity