title: Automated detection of macular drusen using geometric background leveling and threshold selection.
abstract: BACKGROUND
  Age-related macular degeneration (ARMD) is the most prevalent cause of visual loss in patients older than 60 years in the United States. Observation of drusen is the hallmark finding in the clinical evaluation of ARMD.
  
  
  OBJECTIVE
  To segment and quantify drusen found in patients with ARMD using image analysis and to compare the efficacy of image analysis segmentation with that of stereoscopic manual grading of drusen.
  
  
  METHODS
  Retrospective study.
  
  
  METHODS
  University referral center.Patients Photographs were randomly selected from an available database of patients with known ARMD in the ongoing Columbia University Macular Genetics Study. All patients were white and older than 60 years.
  
  
  METHODS
  Twenty images from 17 patients were selected as representative of common manifestations of drusen. Image preprocessing included automated color balancing and, where necessary, manual segmentation of confounding lesions such as geographic atrophy (3 images). The operator then chose among 3 automated processing options suggested by predominant drusen type. Automated processing consisted of elimination of background variability by a mathematical model and subsequent histogram-based threshold selection. A retinal specialist using a graphic tablet while viewing stereo pairs constructed digital drusen drawings for each image.
  
  
  METHODS
  The sensitivity and specificity of drusen segmentation using the automated method with respect to manual stereoscopic drusen drawings were calculated on a rigorous pixel-by-pixel basis.
  
  
  RESULTS
  The median sensitivity and specificity of automated segmentation were 70% and 81%, respectively. After preprocessing and option choice, reproducibility of automated drusen segmentation was necessarily 100%.
  
  
  CONCLUSIONS
  Automated drusen segmentation can be reliably performed on digital fundus photographs and result in successful quantification of drusen in a more precise manner than is traditionally possible with manual stereoscopic grading of drusen. With only minor preprocessing requirements, this automated detection technique may dramatically improve our ability to monitor drusen in ARMD.
authors.affiliation: Edward S. Harkness Eye Institute, New York Presbyterian Medical Center, New York, NY 10032, USA. rts1@columbia.edu
authors.forename: R Theodore
authors.initials: RT
authors.lastname: Smith
authors.forename: Jackie K
authors.initials: JK
authors.lastname: Chan
authors.forename: Takayuki
authors.initials: T
authors.lastname: Nagasaki
authors.forename: Umer F
authors.initials: UF
authors.lastname: Ahmad
authors.forename: Irene
authors.initials: I
authors.lastname: Barbazetto
authors.forename: Janet
authors.initials: J
authors.lastname: Sparrow
authors.forename: Marta
authors.initials: M
authors.lastname: Figueroa
authors.forename: Joanna
authors.initials: J
authors.lastname: Merriam
mesh_terms: D003941:Diagnostic Techniques, Ophthalmological; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008268:Macular Degeneration / Q000175:diagnosis*; D008875:Middle Aged; D010781:Photography / Q000379:methods; D015203:Reproducibility of Results; D015593:Retinal Drusen / Q000175:diagnosis*; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D012684:Sensory Thresholds