Locating the optic nerve in retinal images: comparing model-based and Bayesian decision methods.
In this work we compare two methods for automatic optic nerve (ON) localization in retinal imagery. The first method uses a Bayesian decision theory discriminator based on four spatial features of the retina imagery. The second method uses a principal component-based reconstruction to model the ON. We report on an improvement to the model-based technique by incorporating linear discriminant analysis and Bayesian decision theory methods. We explore a method to combine both techniques to produce a composite technique with high accuracy and rapid throughput. Results are shown for a data set of 395 images with 2-fold validation testing.
Karnowski
Thomas P
TP
Oak Ridge Nat. Lab., Oak Ridge, TN 37831-6285, USA. karnowskitp@ornl.gov
Govindasamy
V Priya
VP
Tobin
Kenneth W
KW
Chaum
Edward
E
D000465:Algorithms; D001331:Automation; D001499:Bayes Theorem; D005123:Eye; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D016013:Likelihood Functions; D015233:Models, Statistical; D008962:Models, Theoretical; D009900:Optic Nerve / Q000473:pathology*; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012160:Retina / Q000473:pathology*; D012164:Retinal Diseases / Q000473:pathology*; D012680:Sensitivity and Specificity