Identification of Diabetic Retinal Exudates in Digital Color Images Using Support Vector Machine

R. Mansour, E. M. Abdelrahim, A. Al‐Johani
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引用次数: 15

Abstract

Support vector machine (SVM) has become an increasingly popular tool for machine learning tasks involving classification. In this paper, we present a simple and effective method of detect and classify hard exudates. Automatic detection of hard exudates from retinal images is worth-studying problem since hard exudates are associated with diabetic retinopathy and have been found to be one of the most prevalent earliest signs of retinopathy. The algorithm is based on Discrete Cosine Transform (DCT) analysis and SVM makes use of color information to perform the classification of retinal exudates. We prospectively assessed the algorithm performance using a database containing 1200 retinal images with variable color, brightness, and quality. Results of the proposed system can achieve a diagnostic accuracy with 97.0% sensitivity and 98.7% specificity for the identification of images containing any evidence of retinopathy.
基于支持向量机的彩色数字图像中糖尿病视网膜渗出物的识别
支持向量机(SVM)已经成为一种越来越受欢迎的机器学习工具。本文提出了一种简单有效的硬渗出物检测与分类方法。从视网膜图像中自动检测硬渗出物是一个值得研究的问题,因为硬渗出物与糖尿病视网膜病变有关,并且已被发现是视网膜病变最普遍的早期征兆之一。该算法基于离散余弦变换(DCT)分析,支持向量机利用颜色信息对视网膜渗出物进行分类。我们使用包含1200张不同颜色、亮度和质量的视网膜图像的数据库前瞻性地评估了算法的性能。结果表明,该系统在识别含有视网膜病变证据的图像时,具有97.0%的灵敏度和98.7%的特异性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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