Efficient BFCN for Automatic Retinal Vessel Segmentation
Joint Authors
Jiang, Yun
Liu, Wenhuan
Wang, Falin
Gao, Jing
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-09-17
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Retinal vessel segmentation has high value for the research on the diagnosis of diabetic retinopathy, hypertension, and cardiovascular and cerebrovascular diseases.
Most methods based on deep convolutional neural networks (DCNN) do not have large receptive fields or rich spatial information and cannot capture global context information of the larger areas.
Therefore, it is difficult to identify the lesion area, and the segmentation efficiency is poor.
This paper presents a butterfly fully convolutional neural network (BFCN).
First, in view of the low contrast between blood vessels and the background in retinal blood vessel images, this paper uses automatic color enhancement (ACE) technology to increase the contrast between blood vessels and the background.
Second, using the multiscale information extraction (MSIE) module in the backbone network can capture the global contextual information in a larger area to reduce the loss of feature information.
At the same time, using the transfer layer (T_Layer) can not only alleviate gradient vanishing problem and repair the information loss in the downsampling process but also obtain rich spatial information.
Finally, for the first time in the paper, the segmentation image is postprocessed, and the Laplacian sharpening method is used to improve the accuracy of vessel segmentation.
The method mentioned in this paper has been verified by the DRIVE, STARE, and CHASE datasets, with the accuracy of 0.9627, 0.9735, and 0.9688, respectively.
American Psychological Association (APA)
Jiang, Yun& Wang, Falin& Gao, Jing& Liu, Wenhuan. 2020. Efficient BFCN for Automatic Retinal Vessel Segmentation. Journal of Ophthalmology،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1189561
Modern Language Association (MLA)
Jiang, Yun…[et al.]. Efficient BFCN for Automatic Retinal Vessel Segmentation. Journal of Ophthalmology No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1189561
American Medical Association (AMA)
Jiang, Yun& Wang, Falin& Gao, Jing& Liu, Wenhuan. Efficient BFCN for Automatic Retinal Vessel Segmentation. Journal of Ophthalmology. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1189561
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1189561