Akram M Usman, Tariq Anam, Khan Shoab A, Javed M Younus
Department of Computer Engineering College of E&ME, National University of Sciences and Technology, Peshawar Road, Rawalpindi, Pakistan.
Comput Methods Programs Biomed. 2014 Apr;114(2):141-52. doi: 10.1016/j.cmpb.2014.01.010. Epub 2014 Jan 21.
Medical systems based on state of the art image processing and pattern recognition techniques are very common now a day. These systems are of prime interest to provide basic health care facilities to patients and support to doctors. Diabetic macular edema is one of the retinal abnormalities in which diabetic patient suffers from severe vision loss due to affected macula. It affects the central vision of the person and causes total blindness in severe cases. In this article, we propose an intelligent system for detection and grading of macular edema to assist the ophthalmologists in early and automated detection of the disease. The proposed system consists of a novel method for accurate detection of macula using a detailed feature set and Gaussian mixtures model based classifier. We also present a new hybrid classifier as an ensemble of Gaussian mixture model and support vector machine for improved exudate detection even in the presence of other bright lesions which eventually leads to reliable classification of input retinal image in different stages of macular edema. The statistical analysis and comparative evaluation of proposed system with existing methods are performed on publicly available standard retinal image databases. The proposed system has achieved average value of 97.3%, 95.9% and 96.8% for sensitivity, specificity and accuracy respectively on both databases.
如今，基于先进图像处理和模式识别技术的医疗系统非常普遍。这些系统对于为患者提供基本医疗设施以及为医生提供支持至关重要。糖尿病性黄斑水肿是一种视网膜异常疾病，糖尿病患者会因黄斑受影响而遭受严重视力丧失。它会影响人的中心视力，在严重情况下会导致完全失明。在本文中，我们提出了一种用于黄斑水肿检测和分级的智能系统，以协助眼科医生早期自动检测该疾病。所提出的系统包括一种使用详细特征集和基于高斯混合模型的分类器来准确检测黄斑的新方法。我们还提出了一种新的混合分类器，它是高斯混合模型和支持向量机的集成，即使在存在其他明亮病变的情况下也能改进渗出物检测，最终实现对输入视网膜图像在不同黄斑水肿阶段的可靠分类。在所公开的标准视网膜图像数据库上对所提出的系统与现有方法进行了统计分析和比较评估。在所提出的系统在两个数据库上的灵敏度、特异性和准确率分别达到了97.3%、95.9%和96.8%的平均值。
