BACKGROUND As one of the major complications of diabetes, diabetic retinopathy (DR) is a leading cause of visual impairment and blindness due to delayed diagnosis and intervention. Microaneurysms appear as the earliest symptom of DR. Accurate and reliable detection of microaneurysms in color fundus images has great importance for DR screening. METHODS A microaneurysms' detection method using machine learning based on directional local contrast (DLC) is proposed for the early diagnosis of DR. First, blood vessels were enhanced and segmented using improved enhancement function based on analyzing eigenvalues of Hessian matrix. Next, with blood vessels excluded, microaneurysm candidate regions were obtained using shape characteristics and connected components analysis. After image segmented to patches, the features of each microaneurysm candidate patch were extracted, and each candidate patch was classified into microaneurysm or non-microaneurysm. The main contributions of our study are (1) making use of directional local contrast in microaneurysms' detection for the first time, which does make sense for better microaneurysms' classification. (2) Applying three different machine learning techniques for classification and comparing their performance for microaneurysms' detection. The proposed algorithm was trained and tested on e-ophtha MA database, and further tested on another independent DIARETDB1 database. Results of microaneurysms' detection on the two databases were evaluated on lesion level and compared with existing algorithms. RESULTS The proposed method has achieved better performance compared with existing algorithms on accuracy and computation time. On e-ophtha MA and DIARETDB1 databases, the area under curve (AUC) of receiver operating characteristic (ROC) curve was 0.87 and 0.86, respectively. The free-response ROC (FROC) score on the two databases was 0.374 and 0.210, respectively. The computation time per image with resolution of 2544×1969, 1400×960 and 1500×1152 is 29 s, 3 s and 2.6 s, respectively. CONCLUSIONS The proposed method using machine learning based on directional local contrast of image patches can effectively detect microaneurysms in color fundus images and provide an effective scientific basis for early clinical DR diagnosis.

中文翻译：

---

使用基于定向局部对比度的机器学习对彩色眼底图像中的微动脉瘤进行检测。

背景技术作为糖尿病的主要并发症之一，由于延迟的诊断和干预，糖尿病性视网膜病（DR）是视力障碍和失明的主要原因。微动脉瘤是DR的最早症状。准确可靠地检测彩色眼底图像中的微动脉瘤对于DR筛查非常重要。方法提出了一种基于方向性局部对比度（DLC）的机器学习微动脉瘤检测方法，用于DR的早期诊断。首先，基于Hessian矩阵的特征值，使用改进的增强函数对血管进行增强和分割。接下来，在排除血管的情况下，使用形状特征和连接成分分析获得微动脉瘤候选区域。图像分割成补丁后，提取每个微动脉瘤候选斑块的特征，将每个候选斑块分为微动脉瘤或非微动脉瘤。我们的研究的主要贡献是（1）首次在微动脉瘤的检测中使用了局部局部对比，这对于更好地进行微动脉瘤的分类是有意义的。（2）应用三种不同的机器学习技术进行分类，并比较它们在微动脉瘤检测中的性能。该算法在e-ophtha MA数据库中经过培训和测试，并在另一个独立的DIARETDB1数据库上进行了进一步测试。对两个数据库中的微动脉瘤的检测结果进行了病灶水平评估，并与现有算法进行了比较。结果与现有算法相比，该方法在精度和计算时间上均取得了较好的性能。在e-ophtha MA和DIARETDB1数据库上，接收器工作特性（ROC）曲线的曲线下面积（AUC）分别为0.87和0.86。这两个数据库的自由应答ROC（FROC）得分分别为0.374和0.210。分辨率为2544×1969、1400×960和1500×1152的每个图像的计算时间分别为29 s，3 s和2.6 s。结论所提出的基于图像斑块方向局部对比度的机器学习方法可以有效地检测彩色眼底图像中的微动脉瘤，为早期临床DR诊断提供有效的科学依据。接收器工作特性（ROC）曲线的曲线下面积（AUC）分别为0.87和0.86。这两个数据库的自由应答ROC（FROC）得分分别为0.374和0.210。分辨率为2544×1969、1400×960和1500×1152的每个图像的计算时间分别为29 s，3 s和2.6 s。结论所提出的基于图像斑块方向局部对比度的机器学习方法可以有效地检测彩色眼底图像中的微动脉瘤，为早期临床DR诊断提供有效的科学依据。接收器工作特性（ROC）曲线的曲线下面积（AUC）分别为0.87和0.86。这两个数据库的自由应答ROC（FROC）得分分别为0.374和0.210。分辨率为2544×1969、1400×960和1500×1152的每个图像的计算时间分别为29 s，3 s和2.6 s。结论所提出的基于图像斑块方向局部对比度的机器学习方法可以有效地检测彩色眼底图像中的微动脉瘤，为早期临床DR诊断提供有效的科学依据。