Lu Shijian, Liu Jiang, Lim Joo Hwee, Zhang Zhuo, Meng Tan Ngan, Wong Wing Kee, Li Huiqi, Wong Tian Yin
Institute for Infocomm Research, A*STAR, Singapore.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:1453-6. doi: 10.1109/IEMBS.2009.5332917.
With the advances of computer technology, more and more computer-aided diagnosis (CAD) systems have been developed to provide the "second opinion". This paper reports an automatic fundus image classification technique that is designed to screen out the severely degraded fundus images that cannot be processed by traditional CAD systems. The proposed technique classifies fundus images based on the image range property. In particular, it first calculates a number of range images from a fundus image at different resolutions. A feature vector is then constructed based on the histogram of the calculated range images. Finally, fundus images can be classified by a linear discriminant classifier that is built by learning from a large number of normal and abnormal training fundus images. Experiments over 644 fundus images of different qualities show that the classification accuracy of the proposed technique reaches above 96%.
随着计算机技术的进步，越来越多的计算机辅助诊断（CAD）系统被开发出来以提供“第二意见”。本文报告了一种自动眼底图像分类技术，该技术旨在筛选出传统CAD系统无法处理的严重退化的眼底图像。所提出的技术基于图像范围属性对眼底图像进行分类。具体而言，它首先从不同分辨率的眼底图像计算出多个范围图像。然后基于计算出的范围图像的直方图构建特征向量。最后，通过从大量正常和异常训练眼底图像中学习构建的线性判别分类器对眼底图像进行分类。对644张不同质量的眼底图像进行的实验表明，所提出技术的分类准确率达到96%以上。
