title: Microaneurysm detection with radon transform-based classification on retina images.
abstract: The creation of an automatic diabetic retinopathy screening system using retina cameras is currently receiving considerable interest in the medical imaging community. The detection of microaneurysms is a key element in this effort. In this work, we propose a new microaneurysms segmentation technique based on a novel application of the radon transform, which is able to identify these lesions without any previous knowledge of the retina morphological features and with minimal image preprocessing. The algorithm has been evaluated on the Retinopathy Online Challenge public dataset, and its performance compares with the best current techniques. The performance is particularly good at low false positive ratios, which makes it an ideal candidate for diabetic retinopathy screening systems.
authors.affiliation: Oak Ridge National Laboratory, University of Burgundy. giancardol@ornl.gov
authors.forename: L
authors.initials: L
authors.lastname: Giancardo
authors.forename: F
authors.initials: F
authors.lastname: Meriaudeau
authors.forename: T P
authors.initials: TP
authors.lastname: Karnowski
authors.forename: Y
authors.initials: Y
authors.lastname: Li
authors.forename: K W
authors.initials: KW
authors.lastname: Tobin
authors.forename: E
authors.initials: E
authors.lastname: Chaum
mesh_terms: D000465:Algorithms; D000783:Aneurysm / Q000473:pathology*; D003930:Diabetic Retinopathy / 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; D012161:Retinal Artery / Q000473:pathology*; D042262:Retinoscopy / Q000379:methods*; D012680:Sensitivity and Specificity