Retina lesion and microaneurysm segmentation using morphological reconstruction methods with ground-truth data.
In this work we report on a method for lesion segmentation based on the morphological reconstruction methods of Sbeh et. al. We adapt the method to include segmentation of dark lesions with a given vasculature segmentation. The segmentation is performed at a variety of scales determined using ground-truth data. Since the method tends to over-segment imagery, ground-truth data was used to create post-processing filters to separate nuisance blobs from true lesions. A sensitivity and specificity of 90% of classification of blobs into nuisance and actual lesion was achieved on two data sets of 86 images and 1296 images.
Karnowski
Thomas P
TP
Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA. karnowskitp@ornl.gov
Govindasamy
V
V
Tobin
Kenneth W
KW
Chaum
Edward
E
Abramoff
M D
MD
D000465:Algorithms; D000783:Aneurysm / Q000473:pathology*; D003930:Diabetic Retinopathy / Q000150:complications / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012160:Retina / Q000473:pathology*; D012161:Retinal Artery / Q000473:pathology*; D042262:Retinoscopy / Q000379:methods*; D012680:Sensitivity and Specificity