title: Retina lesion and microaneurysm segmentation using morphological reconstruction methods with ground-truth data.
abstract: 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.
authors.affiliation: Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA. karnowskitp@ornl.gov
authors.forename: Thomas P
authors.initials: TP
authors.lastname: Karnowski
authors.forename: V
authors.initials: V
authors.lastname: Govindasamy
authors.forename: Kenneth W
authors.initials: KW
authors.lastname: Tobin
authors.forename: Edward
authors.initials: E
authors.lastname: Chaum
authors.forename: M D
authors.initials: MD
authors.lastname: Abramoff
mesh_terms: 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