Retinal Artery-Vein Classification via Topology Estimation.
Retinal Artery-Vein Classification via Topology Estimation.
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DOI：
10.1109/tmi.2015.2443117
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发表时间：
2015-12
影响因子：
10.6
通讯作者：
Farsiu S
中科院分区：
文献类型：
--
作者：
Estrada R;Allingham MJ;Mettu PS;Cousins SW;Tomasi C;Farsiu S
我们提出了一种新的图论框架来区分眼底图像中的动脉和静脉。我们利用底层的血管拓扑来更好地对中小型血管进行分类。我们扩展了我们之前提出的树拓扑估计框架，通过结合专家、领域特定的特征来构建一个简单但功能强大的全局似然模型。我们通过迭代地探索与投影血管一致的可能解的空间来有效地最大化该模型。我们在四个视网膜数据集上测试了我们的方法，分类准确率分别为91.0%、93.5%、91.7%和90.9%，优于现有的方法。我们的结果表明，我们的方法是有效的，它能够在大视野眼底照片中分析整个血管系统，包括周围血管。这种基于拓扑的方法是诊断视网膜血管疾病的一种潜在的重要工具。
We propose a novel, graph-theoretic framework for distinguishing arteries from veins in a fundus image. We make use of the underlying vessel topology to better classify small and midsized vessels. We extend our previously proposed tree topology estimation framework by incorporating expert, domain-specific features to construct a simple, yet powerful global likelihood model. We efficiently maximize this model by iteratively exploring the space of possible solutions consistent with the projected vessels. We tested our method on four retinal datasets and achieved classification accuracies of 91.0%, 93.5%, 91.7%, and 90.9%, outperforming existing methods. Our results show the effectiveness of our approach, which is capable of analyzing the entire vasculature, including peripheral vessels, in wide field-of-view fundus photographs. This topology-based method is a potentially important tool for diagnosing diseases with retinal vascular manifestation.