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%.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. 2009:2009:1453-6. doi: 10.1109/IEMBS.2009.5332917
Automatic fundus image classification for computer-aided diagonsis
用于辅助诊断的自动眼底图像分类方法 翻译改进
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DOI: 10.1109/IEMBS.2009.5332917 PMID: 19963750
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Keywords：computer-aided diagnosis
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