Computer-assisted, interactive fundus image processing for macular drusen quantitation.
OBJECTIVE
To design and validate a software package to quantitate the area subtended by drusen in color fundus photographs for the conduct of efficient, accurate clinical trials in age-related macular degeneration.


METHODS
Algorithm and software development. Comparisons with manual methodologies.


METHODS
Evaluation and testing on color fundus photographs from patient records and from eyes enrolled in the Choroidal Neovascularization Prevention Trial.


METHODS
Fundus photographs of eyes with drusen were digitized. The green channel was selected for maximum contrast and preprocessed with filtering and shade correction to minimize noise, improve contrast, and correct for illumination and background inhomogeneities. Local thresholding and region-growing algorithms identified drusen. Multiple levels of supervision were incorporated to maximize robustness, accuracy, and validity. Validation studies compared computer-assisted with manual grading by an experienced grader. Intraclass correlation coefficients were calculated as a measure of the concordance between manual and computer-assisted fundus gradings.


METHODS
Drusen area and concordance with manual grading.


RESULTS
Automated supervised image analysis offers extreme robustness and accuracy. Most images were segmented with little or no supervision, with processing times on the order of 5 seconds. More complicated images required supervision and a total analysis time varying from 20 seconds to 5 minutes, with most of this time devoted to inspection and comparison. Interactive image processing affords arbitrarily close concordance with manual drusen identification, with calculated intraclass correlation coefficients of 0.92 and 0.93 for comparison of manual with automated, supervised grading by two observers.


CONCLUSIONS
Automated supervised fundus image analysis is an efficient, robust, valid technique for drusen quantitation from color fundus photographs. This approach should prove useful in the conduct of efficient accurate clinical trials for age-related macular degeneration.
Shin
D S
DS
Computer Vision Laboratory, Scheie Eye Institute, University of Pennsylvania School of Medicine, Philadelphia 19104, USA.
Javornik
N B
NB
Berger
J W
JW
D000465:Algorithms; D005654:Fundus Oculi; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008266:Macula Lutea / Q000473:pathology*; D010781:Photography; D015203:Reproducibility of Results; D015593:Retinal Drusen / Q000473:pathology*