title: An Automated Reference Frame Selection (ARFS) Algorithm for Cone Imaging with Adaptive Optics Scanning Light Ophthalmoscopy.
abstract: OBJECTIVE
  To develop an automated reference frame selection (ARFS) algorithm to replace the subjective approach of manually selecting reference frames for processing adaptive optics scanning light ophthalmoscope (AOSLO) videos of cone photoreceptors.
  
  
  METHODS
  Relative distortion was measured within individual frames before conducting image-based motion tracking and sorting of frames into distinct spatial clusters. AOSLO images from nine healthy subjects were processed using ARFS and human-derived reference frames, then aligned to undistorted AO-flood images by nonlinear registration and the registration transformations were compared. The frequency at which humans selected reference frames that were rejected by ARFS was calculated in 35 datasets from healthy subjects, and subjects with achromatopsia, albinism, or retinitis pigmentosa. The level of distortion in this set of human-derived reference frames was assessed.
  
  
  RESULTS
  The average transformation vector magnitude required for registration of AOSLO images to AO-flood images was significantly reduced from 3.33 ± 1.61 pixels when using manual reference frame selection to 2.75 ± 1.60 pixels (mean ± SD) when using ARFS (P= 0.0016). Between 5.16% and 39.22% of human-derived frames were rejected by ARFS. Only 2.71% to 7.73% of human-derived frames were ranked in the top 5% of least distorted frames.
  
  
  CONCLUSIONS
  ARFS outperforms expert observers in selecting minimally distorted reference frames in AOSLO image sequences. The low success rate in human frame choice illustrates the difficulty in subjectively assessing image distortion.
  
  
  CONCLUSIONS
  Manual reference frame selection represented a significant barrier to a fully automated image-processing pipeline (including montaging, cone identification, and metric extraction). The approach presented here will aid in the clinical translation of AOSLO imaging.
authors.affiliation: Department of Cell Biology, Neurobiology, & Anatomy, Medical College of Wisconsin, Milwaukee, WI, USA.
authors.forename: Alexander E
authors.initials: AE
authors.lastname: Salmon
authors.affiliation: Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA ; Department of Ophthalmology, University of Pennsylvania, Philadelphia, PA, USA.
authors.forename: Robert F
authors.initials: RF
authors.lastname: Cooper
authors.affiliation: Department of Cell Biology, Neurobiology, & Anatomy, Medical College of Wisconsin, Milwaukee, WI, USA.
authors.forename: Christopher S
authors.initials: CS
authors.lastname: Langlo
authors.affiliation: Department of Electrical Engineering, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.
authors.forename: Ahmadreza
authors.initials: A
authors.lastname: Baghaie
authors.affiliation: Department of Cell Biology, Neurobiology, & Anatomy, Medical College of Wisconsin, Milwaukee, WI, USA ; Department of Ophthalmology & Visual Sciences, Medical College of Wisconsin, Milwaukee, WI, USA ; Current affiliation: Department of Ophthalmology, Stanford University, 2452 Watson Court, Palo Alto, CA, USA.
authors.forename: Alfredo
authors.initials: A
authors.lastname: Dubra
authors.affiliation: Department of Cell Biology, Neurobiology, & Anatomy, Medical College of Wisconsin, Milwaukee, WI, USA ; Department of Ophthalmology & Visual Sciences, Medical College of Wisconsin, Milwaukee, WI, USA.
authors.forename: Joseph
authors.initials: J
authors.lastname: Carroll
keywords: adaptive optics; cone mosaic; image processing; retina