一种改进的视网膜图像中微小动脉瘤的检测算法

Improved Method of Microaneurysm Detection Algorithm Based on Digital Fundus Images

基于糖尿病性视网膜病变中最早出现的微小动脉瘤病症进行了研究,提出一种有效的微小动脉瘤检测算法。首先在传统模板匹配算法的基础上提出了一种动态多参数模板匹配算法,并且使用相对误差和与相关系数来共同制约匹配度,从而实现了更为精确的匹配提取;其次提出了基于分布特性的计分策略和自适应加权的汇总策略,避免了单纯采用各个特征量作为独立约束指标进行筛选时忽视各个特征量的约束力大小的弊端。实验结果表明,该检测算法能够有效地提高微小动脉瘤的检测真阳性率。

This paper presented a new approach to detect micmaneurysms （MAs） in digital fundus images.The contributions of this approach are mainly twofold.First,the dynamic multi-parameter template matching scheme was proposed in this paper,which is more realistic compared to conventional schemes.We applied the dual constraints scheme to measure the matching degree by combining the sum of errors and correlation coefficients.Second,an adaptive weighted scoring algorithm with distribution character based scoring scheme was proposed on feature extraction for the MAs detection,which can not only reduce false positive （FP）,but also maintain the true positive （TP） effectively.