基于多尺度匹配滤波和集成学习的眼底图像微脉瘤检测

针对微脉瘤的灰度分布特性，提出一种新的微脉瘤检测算法。首先通过多尺度匹配滤波筛选出候选微脉瘤病变点，并作为种子点利用区域生长技术分割出病变区域;然后提取病变区域特征向量;最终采用Adaboost神经网络集成分类器检测真实的微脉瘤病变。在公开的ROC数据集测试表明，所提方法检测的平均正确率达到40.92%，优于以往的双环滤波和形态学方法。

Microaneurysm detection based on multi-scale match filtering and ensemble learning

According to the gray distribution characteristics of microaneurysms, a new microaneurysm detection algorithm was proposed. First, by multi-scale matched filtering, candidate microaneurysm lesions were picked out as seed points. And region growing technology was applied to segment the lesion areas. Then the features of the lesion areas were extracted. Finally the Adaboost neural network ensemble was designed to distinguish the real microaneurysm from all of the candidate lesions. The proposed method was tested on public ROC database. The experimental results show that the average detection accuracy is 40.92%, which is better than that of previous doublering filtering and morphological methods.