Department of Computer Engineering, College of Electrical & Mechanical Engineering, National University of Sciences & Technology, Islamabad, Pakistan.
Department of Computer & Software Engineering, Bahria University, Islamabad, Pakistan.
Comput Biol Med. 2014 Feb;45:161-71. doi: 10.1016/j.compbiomed.2013.11.014. Epub 2013 Dec 1.
Diabetic Retinopathy (DR) is an eye abnormality in which the human retina is affected due to an increasing amount of insulin in blood. The early detection and diagnosis of DR is vital to save the vision of diabetes patients. The early signs of DR which appear on the surface of the retina are microaneurysms, haemorrhages, and exudates. In this paper, we propose a system consisting of a novel hybrid classifier for the detection of retinal lesions. The proposed system consists of preprocessing, extraction of candidate lesions, feature set formulation, and classification. In preprocessing, the system eliminates background pixels and extracts the blood vessels and optic disc from the digital retinal image. The candidate lesion detection phase extracts, using filter banks, all regions which may possibly have any type of lesion. A feature set based on different descriptors, such as shape, intensity, and statistics, is formulated for each possible candidate region: this further helps in classifying that region. This paper presents an extension of the m-Mediods based modeling approach, and combines it with a Gaussian Mixture Model in an ensemble to form a hybrid classifier to improve the accuracy of the classification. The proposed system is assessed using standard fundus image databases with the help of performance parameters, such as, sensitivity, specificity, accuracy, and the Receiver Operating Characteristics curves for statistical analysis.
糖尿病性视网膜病变（DR）是一种眼部异常，由于血液中胰岛素含量增加，人类视网膜受到影响。早期发现和诊断 DR 对于挽救糖尿病患者的视力至关重要。DR 在视网膜表面出现的早期迹象是微动脉瘤、出血和渗出物。在本文中，我们提出了一种由用于检测视网膜病变的新型混合分类器组成的系统。所提出的系统包括预处理、候选病变提取、特征集制定和分类。在预处理中，系统消除背景像素并从数字视网膜图像中提取血管和视盘。候选病变检测阶段使用滤波器组提取可能存在任何类型病变的所有区域。为每个可能的候选区域制定基于不同描述符（如形状、强度和统计信息）的特征集：这进一步有助于对该区域进行分类。本文提出了一种基于 m-Mediods 的建模方法的扩展，并将其与高斯混合模型结合在一个集合中，形成一个混合分类器，以提高分类的准确性。该系统使用标准眼底图像数据库进行评估，并使用性能参数（如敏感性、特异性、准确性和接收者操作特征曲线）进行统计分析。
