期刊
PATTERN RECOGNITION
卷 44, 期 10-11, 页码 2314-2324出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2011.01.007
关键词
Radial projection; Retinal images; Steerable complex wavelet
资金
- National Basic Research Program of China (973 Program) [2011CB707104]
- National Natural Science Foundation of China [607731871, 60973154, 61075015]
- Ministry of Education, China [NCET-07-0338]
- Fundamental Research Funds for the Central Universities HUST [2010ZD025]
- Research Grant of Hubei Province [2010CDA006]
- Research Grant Council of Hong Kong SAR [HKBU 210309]
- Hong Kong Baptist University [FRG2/08-09/122]
Automatic 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.
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