A comparison of computer based classification methods applied to the detection of microaneurysms in ophthalmic fluorescein angiograms.
We compared the performance of three computer based classification methods when applied to the problem of detecting microaneurysms on digitised angiographic images of the retina. An automated image processing system segmented 'candidate' objects (microaneurysms or spurious objects), and produced a list of features on each candidate for use by the classifiers. We compared an empirically derived rule based system with two automated methods, linear discriminant analysis and a learning vector quantiser artificial neural network, to classify the objects as microaneurysms or otherwise. ROC analysis shows that the rule based system gave a higher performance than the other methods (p = 0.92) although a much greater development time is required.
Frame
A J
AJ
Department of Bio-medical Physics and Bio-engineering, University of Aberdeen, UK. a.frame@biomed.abdn.ac.uk
Undrill
P E
PE
Cree
M J
MJ
Olson
J A
JA
McHardy
K C
KC
Sharp
P F
PF
Forrester
J V
JV
D000783:Aneurysm / Q000175:diagnosis*; D001185:Artificial Intelligence; D003930:Diabetic Retinopathy / Q000175:diagnosis; D003936:Diagnosis, Computer-Assisted; D016002:Discriminant Analysis; D005451:Fluorescein Angiography; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D012372:ROC Curve; D012171:Retinal Vessels / Q000473:pathology*; D012680:Sensitivity and Specificity; D013997:Time Factors