title: An ensemble-based system for microaneurysm detection and diabetic retinopathy grading.
abstract: Reliable microaneurysm detection in digital fundus images is still an open issue in medical image processing. We propose an ensemble-based framework to improve microaneurysm detection. Unlike the well-known approach of considering the output of multiple classifiers, we propose a combination of internal components of microaneurysm detectors, namely preprocessing methods and candidate extractors. We have evaluated our approach for microaneurysm detection in an online competition, where this algorithm is currently ranked as first, and also on two other databases. Since microaneurysm detection is decisive in diabetic retinopathy (DR) grading, we also tested the proposed method for this task on the publicly available Messidor database, where a promising AUC 0.90  ± 0.01 is achieved in a "DR/non-DR"-type classification based on the presence or absence of the microaneurysms.
authors.affiliation: Faculty of Informatics, University of Debrecen, Debrecen H-4032, Hungary. antal.balint@inf.unideb.hu
authors.forename: Bálint
authors.initials: B
authors.lastname: Antal
authors.forename: András
authors.initials: A
authors.lastname: Hajdu
mesh_terms: D000465:Algorithms; D000783:Aneurysm / Q000473:pathology*; D003925:Diabetic Angiopathies / 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