Diabetic retinopathy (DR) is one of the most frequent microvascular complications of diabetes mellitus, which damages micro- and macrovascular systems. Hence, early detection and grading are important for its effective treatment. This study presents a comprehensive micro-macro feature extraction algorithm for the grading of DR using retinal images. The method employed is a mutliresolutional microtechnique, based on dual-tree quaternion wavelet transform fused with local mesh patterns. Since the pixel level model is unable to capture macrolevel features and is difficult for efficient decision-making, this process additionally proposes a macrolevel feature extraction technique based on feature gradients. The macrolevel descriptor considers a group of pixels to find feature gradients of macrolevel lesions. Feature extracted using the micro-macro approaches is summarized, and a comparison study using three machine learning classifiers is considered. Performance of the classifiers is determined by conducting a 10-fold cross-validation procedure. Among the classifiers, the highest classification accuracy of 93.2% is exhibited by radial basis function kernel extreme learning machine. Simulation results illustrate the adaptability and competency of the novel micro-macro approach with high accuracy and sensitivity. The promising result assures the excellence of the proposed method for automated DR grading over other state-of-the-art techniques.

中文翻译：

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使用极限学习机分类器融合双树四元数小波变换和基于局部网格的特征用于糖尿病视网膜病变分级

糖尿病视网膜病变 (DR) 是糖尿病最常见的微血管并发症之一，会损害微血管和大血管系统。因此，早期发现和分级对其有效治疗很重要。本研究提出了一种综合的微宏观特征提取算法，用于使用视网膜图像对 DR 进行分级。所采用的方法是一种多分辨率微技术，基于与局部网格模式融合的双树四元数小波变换。由于像素级模型无法捕捉宏观特征，难以高效决策，因此该过程额外提出了基于特征梯度的宏观特征提取技术。宏观描述子考虑一组像素来寻找宏观病变的特征梯度。总结了使用微宏方法提取的特征，并考虑了使用三种机器学习分类器的比较研究。分类器的性能通过进行 10 倍交叉验证程序来确定。在分类器中，径向基函数核极限学习机的分类准确率最高，达到93.2%。仿真结果说明了具有高精度和灵敏度的新型微宏方法的适应性和能力。有希望的结果确保了所提出的自动 DR 分级方法优于其他最先进技术。在分类器中，径向基函数核极限学习机的分类准确率最高，达到93.2%。仿真结果说明了具有高精度和灵敏度的新型微宏方法的适应性和能力。有希望的结果确保了所提出的自动 DR 分级方法优于其他最先进技术。在分类器中，径向基函数核极限学习机的分类准确率最高，达到93.2%。仿真结果说明了具有高精度和灵敏度的新型微宏方法的适应性和能力。有希望的结果确保了所提出的自动 DR 分级方法优于其他最先进技术。