Automated method for identification and artery-venous classification of vessel trees in retinal vessel networks.

Automated method for identification and artery-venous classification of vessel trees in retinal vessel networks.
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DOI:
10.1371/journal.pone.0088061
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发表时间:
2014
期刊:
影响因子:
3.7
通讯作者:
Abramoff MD
Abramoff MD
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Joshi VS;Reinhardt JM;Garvin MK;Abramoff MD

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将视网膜血管网络分离成不同的动脉和静脉血管树是很有意义的。我们提出了一种自动识别和分离视网膜彩色图像中视网膜血管树的方法,该方法将血管分割图像转换为血管分段图,并通过图搜索来识别单个血管树。利用每个血管段的方向、宽度和强度来寻找最优的血管段图。分离的脉管树被标记为主要脉管或分支。我们利用分离的血管树来进行动-静脉(AV)分类,基于每个树图中血管的颜色属性。我们将我们的方法应用于来自50名受试者的50张眼底图像的数据集。该方法正确地将血管像素分类为动脉或静脉,准确率为91.44。对主干血管节段的正确分类准确率为96.42。
The separation of the retinal vessel network into distinct arterial and venous vessel trees is of high interest. We propose an automated method for identification and separation of retinal vessel trees in a retinal color image by converting a vessel segmentation image into a vessel segment map and identifying the individual vessel trees by graph search. Orientation, width, and intensity of each vessel segment are utilized to find the optimal graph of vessel segments. The separated vessel trees are labeled as primary vessel or branches. We utilize the separated vessel trees for arterial-venous (AV) classification, based on the color properties of the vessels in each tree graph. We applied our approach to a dataset of 50 fundus images from 50 subjects. The proposed method resulted in an accuracy of 91.44 correctly classified vessel pixels as either artery or vein. The accuracy of correctly classified major vessel segments was 96.42.
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