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首页> 外文期刊>Multimedia Tools and Applications >Using orthogonal locality preserving projections to find dominant features for classifying retinal blood vessels
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Using orthogonal locality preserving projections to find dominant features for classifying retinal blood vessels

机译：使用正交局部保留投影来找到用于视网膜血管分类的主要特征

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摘要

Automatically classifying retinal blood vessels appearing in fundus camera imaging into arterioles and venules can be problematic due to variations between people as well as in image quality, contrast and brightness. Using the most dominant features for retinal vessel types in each image rather than predefining the set of characteristic features prior to classification may achieve better performance. In this paper, we present a novel approach to classifying retinal vessels extracted from fundus camera images which combines an Orthogonal Locality Preserving Projections for feature extraction and a Gaussian Mixture Model with Expectation-Maximization unsupervised classifier. The classification rate with 47 features (the largest dimension tested) using OLPP on our own ORCADES dataset and the publicly available DRIVE dataset was 90.56% and 86.7% respectively.
机译：由于人与人之间以及图像质量，对比度和亮度方面的差异，将眼底照相机成像中出现的视网膜血管自动分类为小动脉和小静脉可能会出现问题。在每个图像中使用视网膜血管类型的最主要特征，而不是在分类之前预先定义一组特征，可能会获得更好的性能。在本文中，我们提出了一种从眼底照相机图像中提取视网膜血管的新方法，该方法结合了用于特征提取的正交局部保留投影和带有期望最大化无监督分类器的高斯混合模型。在我们自己的ORCADES数据集和公开可用的DRIVE数据集上，使用OLPP进行的47个特征分类（测试的最大维度）的分类率分别为90.56％和86.7％。

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