Automated segmentation of retinal blood vessels and identification of proliferative diabetic retinopathy.
Proliferative diabetic retinopathy can lead to blindness. However, early recognition allows appropriate, timely intervention. Fluorescein-labeled retinal blood vessels of 27 digital images were automatically segmented using the Gabor wavelet transform and classified using traditional features such as area, perimeter, and an additional five morphological features based on the derivatives-of-Gaussian wavelet-derived data. Discriminant analysis indicated that traditional features do not detect early proliferative retinopathy. The best single feature for discrimination was the wavelet curvature with an area under the curve (AUC) of 0.76. Linear discriminant analysis with a selection of six features achieved an AUC of 0.90 (0.73-0.97, 95% confidence interval). The wavelet method was able to segment retinal blood vessels and classify the images according to the presence or absence of proliferative retinopathy.
Jelinek
Herbert F
HF
School of Community Health, Charles Sturt University, and Albury Eye Clinic, Australia. hjelinek@csu.edu.au
Cree
Michael J
MJ
Leandro
Jorge J G
JJ
Soares
João V B
JV
Cesar
Roberto M
RM
Luckie
A
A
D000465:Algorithms; D001185:Artificial Intelligence; D003930:Diabetic Retinopathy / Q000473:pathology*; D005451:Fluorescein Angiography / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012171:Retinal Vessels / Q000473:pathology*; D012680:Sensitivity and Specificity