Retinal Vascular Network Topology Reconstruction and Artery/Vein Classification via Dominant Set Clustering
文献类型：期刊论文
| 作者 | Zhao, Yitian; Liu, Yonghuai; Xie, Jianyang; Zhang, Huaizhong; Zheng, Yalin; Zhao, Yifan; Qi, Hong; Zhao, Yangchun; Su, Pan; Liu, Jiang |
| 刊名 | IEEE TRANSACTIONS ON MEDICAL IMAGING
 |
| 出版日期 | 2020 |
| 卷号 | 39期号:2页码:341-356 |
| 关键词 | VESSEL SEGMENTATION VEIN CLASSIFICATION BLOOD-VESSELS IMAGES ARTERIES |
| DOI | 10.1109/TMI.2019.2926492 |
| 英文摘要 | The estimation of vascular network topology in complex networks is important in understanding the relationship between vascular changes and a wide spectrum of diseases. Automatic classification of the retinal vascular trees into arteries and veins is of direct assistance to the ophthalmologist in terms of diagnosis and treatment of eye disease. However, it is challenging due to their projective ambiguity and subtle changes in appearance, contrast, and geometry in the imaging process. In this paper, we propose a novel method that is capable of making the artery/vein (A/V) distinction in retinal color fundus images based on vascular network topological properties. To this end, we adapt the concept of and formalize the retinal blood vessel topology estimation and the A/V classification as a pairwise clustering problem. The graph is constructed through image segmentation, skeletonization, and identification of significant nodes. The edge weight is defined as the inverse Euclidean distance between its two end points in the feature space of intensity, orientation, curvature, diameter, and entropy. The reconstructed vascular network is classified into arteries and veins based on their intensity and morphology. The proposed approach has been applied to five public databases, namely INSPIRE, IOSTAR, VICAVR, DRIVE, and WIDE, and achieved high accuracies of 95.1%, 94.2%, 93.8%, 91.1%, and 91.0%, respectively. Furthermore, we have made manual annotations of the blood vessel topologies for INSPIRE, IOSTAR, VICAVR, and DRIVE datasets, and these annotations are released for public access so as to facilitate researchers in the community. |
| 学科主题 | Computer Science ; Engineering ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging |
| 源URL | [http://ir.nimte.ac.cn/handle/174433/20021]  |
| 专题 | 2020专题 2020专题_期刊论文 |
| 作者单位 | 1.Liu, YH (corresponding author), Edge Hill Univ, Dept Comp Sci, Ormskirk L39 4QP, England. 2.Su, P (corresponding author), Chinese Acad Sci, Ningbo Inst Ind Technol, Cixi Instuitue Biomed Engn, Ningbo 315201, Peoples R China. |
| 推荐引用方式 GB/T 7714 | Zhao, Yitian,Liu, Yonghuai,Xie, Jianyang,et al. Retinal Vascular Network Topology Reconstruction and Artery/Vein Classification via Dominant Set Clustering[J]. IEEE TRANSACTIONS ON MEDICAL IMAGING,2020,39(2):341-356. |
| APA | Zhao, Yitian.,Liu, Yonghuai.,Xie, Jianyang.,Zhang, Huaizhong.,Zheng, Yalin.,...&Liu, Jiang.(2020).Retinal Vascular Network Topology Reconstruction and Artery/Vein Classification via Dominant Set Clustering.IEEE TRANSACTIONS ON MEDICAL IMAGING,39(2),341-356. |
| MLA | Zhao, Yitian,et al."Retinal Vascular Network Topology Reconstruction and Artery/Vein Classification via Dominant Set Clustering".IEEE TRANSACTIONS ON MEDICAL IMAGING 39.2(2020):341-356. |
入库方式： OAI收割
来源：宁波材料技术与工程研究所
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