Photoreceptor counting and montaging of en-face retinal images from an adaptive optics fundus camera

Bai Xue, Stacey C. Choi, Nathan Doble, John S Werner

Research output: Contribution to journalArticlepeer-review

53 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1364-1372
Number of pages9
JournalJournal of the Optical Society of America A: Optics and Image Science, and Vision
Issue number5
StatePublished - 2007

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Computer Vision and Pattern Recognition


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