An Ensemble-Based System for Microaneurysm Detection and Diabetic Retinopathy Grading

• Published in: IEEE transactions on biomedical engineering Vol. 59; no. 6; pp. 1720 - 1726
• Main Authors: Antal, Bálint, Hajdu, András
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-06-2012; Institute of Electrical and Electronics Engineers
• Subjects: Algorithms; Aneurysm - pathology; Applied sciences; Biological and medical sciences; Detectors; Diabetes. Impaired glucose tolerance; Diabetic Angiopathies - pathology; Diabetic retinopathy (DR) grading; Endocrine pancreas. Apud cells (diseases); Endocrinopathies; ensemble-based systems; Etiopathogenesis. Screening. Investigations. Target tissue resistance; Exact sciences and technology; fundus image processing; Helium; Histograms; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image processing; Information, signal and communications theory; Medical sciences; microaneurysm (MA) detection; Ophthalmology; Pattern Recognition, Automated - methods; Reproducibility of Results; Retinal Artery - pathology; Retinopathies; Retinopathy; Retinoscopy - methods; Sensitivity; Sensitivity and Specificity; Signal processing; Telecommunications and information theory; Training; Endocrinopathy; Diabetic retinopathy (DR) grading; Eye disease; Retinopathy; Image processing; Diabetes mellitus; ensemble-based systems; fundus image processing; microaneurysm (MA) detection; Diagnosis; Biomedical engineering
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2012.2193126

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.