Faculty of Informatics, University of Debrecen, Debrecen H-4032, Hungary.
IEEE Trans Biomed Eng. 2012 Jun;59(6):1720-6. doi: 10.1109/TBME.2012.2193126. Epub 2012 Apr 3.
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.
在医学图像处理中，可靠的眼底数字图像微动脉瘤检测仍然是一个尚未解决的问题。我们提出了一种基于集成的框架来提高微动脉瘤检测的性能。与众所周知的考虑多个分类器输出的方法不同，我们提出了微动脉瘤检测器的内部组件的组合，即预处理方法和候选提取器。我们已经在一个在线竞赛中评估了我们的微动脉瘤检测方法，该算法目前排名第一，并且在另外两个数据库上也进行了评估。由于微动脉瘤检测在糖尿病视网膜病变（DR）分级中至关重要，我们还在公开的 Messidor 数据库上针对该任务测试了所提出的方法，在基于微动脉瘤存在与否的“DR/非 DR”分类中，该方法实现了有希望的 AUC 0.90±0.01。
