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Segmentation of retinal blood vessels using the radial projection and semi-supervised approach
You, Xinge2; Peng, Qinmu2; Yuan, Yuan1; Cheung, Yiu-ming3; Lei, Jiajia2
Department光学影像分析与学习中心
2011-10-01
Source PublicationPATTERN RECOGNITION
ISSN0031-3203
Volume44Issue:10-11Pages:2314-2324
AbstractAutomatic segmentation of retinal blood vessels has become a necessary diagnostic procedure in ophthalmology. The blood vessels consist of two types of vessels, i.e., thin vessels and wide vessels. Therefore, a segmentation method may require two different processes to treat different vessels. However, traditional segmentation algorithms hardly draw a distinction between thin and wide vessels, but deal with them together. The major problems of these methods are as follows: (1) If more emphasis is placed on the extraction of thin vessels, the wide vessels tend to be over detected; and more artificial vessels are generated, too. (2) If more attention is paid on the wide vessels, the thin and low contrast vessels are likely to be missing. To overcome these problems, a novel scheme of extracting the retinal vessels based on the radial projection and semi-supervised method is presented in this paper. The radial projection method is used to locate the vessel centerlines which include the low-contrast and narrow vessels. Further, we modify the steerable complex wavelet to provide better capability of enhancing vessels under different scales, and construct the vector feature to represent the vessel pixel by line strength. Then, semi-supervised self-training is used for extraction of the major structures of vessels. The final segmentation is obtained by the union of the two types of vessels. Our approach is tested on two publicly available databases. Experiment results show that the method can achieve improved detection of thin vessels and decrease false detection of vessels in pathological regions compared to rival solutions. (C) 2011 Published by Elsevier Ltd.
SubtypeArticle
KeywordRadial Projection Retinal Images Steerable Complex Wavelet
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.patcog.2011.01.007
Indexed BySCI ; EI
WOS KeywordVASCULAR SEGMENTATION ; MATCHED-FILTERS ; IMAGES ; CLASSIFICATION ; MODEL ; ALGORITHM ; WAVELET
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000292849000010
Citation statistics
Cited Times:160[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/10571
Collection光学影像学习与分析中心
Affiliation1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710119, Shaanxi, Peoples R China
2.Huazhong Univ Sci & Technol, Dept Elect & Informat Engn, Wuhan 430074, Peoples R China
3.Hong Kong Baptist Univ, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China
Recommended Citation
GB/T 7714
You, Xinge,Peng, Qinmu,Yuan, Yuan,et al. Segmentation of retinal blood vessels using the radial projection and semi-supervised approach[J]. PATTERN RECOGNITION,2011,44(10-11):2314-2324.
APA You, Xinge,Peng, Qinmu,Yuan, Yuan,Cheung, Yiu-ming,&Lei, Jiajia.(2011).Segmentation of retinal blood vessels using the radial projection and semi-supervised approach.PATTERN RECOGNITION,44(10-11),2314-2324.
MLA You, Xinge,et al."Segmentation of retinal blood vessels using the radial projection and semi-supervised approach".PATTERN RECOGNITION 44.10-11(2011):2314-2324.
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