Next Article in Journal
Optical Helicity and Optical Chirality in Free Space and in the Presence of Matter
Previous Article in Journal
The Entanglement Generation in P T -Symmetric Optical Quadrimer System
Article

Automatic Retinal Blood Vessel Segmentation Based on Fully Convolutional Neural Networks

by , *,†, and
College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Symmetry 2019, 11(9), 1112; https://doi.org/10.3390/sym11091112
Received: 24 July 2019 / Revised: 26 August 2019 / Accepted: 27 August 2019 / Published: 3 September 2019
(This article belongs to the Special Issue Advances in Medical Image Segmentation 2019)
Automated retinal vessel segmentation technology has become an important tool for disease screening and diagnosis in clinical medicine. However, most of the available methods of retinal vessel segmentation still have problems such as poor accuracy and low generalization ability. This is because the symmetrical and asymmetrical patterns between blood vessels are complicated, and the contrast between the vessel and the background is relatively low due to illumination and pathology. Robust vessel segmentation of the retinal image is essential for improving the diagnosis of diseases such as vein occlusions and diabetic retinopathy. Automated retinal vein segmentation remains a challenging task. In this paper, we proposed an automatic retinal vessel segmentation framework using deep fully convolutional neural networks (FCN), which integrate novel methods of data preprocessing, data augmentation, and full convolutional neural networks. It is an end-to-end framework that automatically and efficiently performs retinal vessel segmentation. The framework was evaluated on three publicly available standard datasets, achieving F1 score of 0.8321, 0.8531, and 0.8243, an average accuracy of 0.9706, 0.9777, and 0.9773, and average area under the Receiver Operating Characteristic (ROC) curve of 0.9880, 0.9923 and 0.9917 on the DRIVE, STARE, and CHASE_DB1 datasets, respectively. The experimental results show that our proposed framework achieves state-of-the-art vessel segmentation performance in all three benchmark tests. View Full-Text
Keywords: retinal image; vessel segmentation; fully convolutional neural network retinal image; vessel segmentation; fully convolutional neural network
Show Figures

Figure 1

MDPI and ACS Style

Jiang, Y.; Zhang, H.; Tan, N.; Chen, L. Automatic Retinal Blood Vessel Segmentation Based on Fully Convolutional Neural Networks. Symmetry 2019, 11, 1112. https://doi.org/10.3390/sym11091112

AMA Style

Jiang Y, Zhang H, Tan N, Chen L. Automatic Retinal Blood Vessel Segmentation Based on Fully Convolutional Neural Networks. Symmetry. 2019; 11(9):1112. https://doi.org/10.3390/sym11091112

Chicago/Turabian Style

Jiang, Yun; Zhang, Hai; Tan, Ning; Chen, Li. 2019. "Automatic Retinal Blood Vessel Segmentation Based on Fully Convolutional Neural Networks" Symmetry 11, no. 9: 1112. https://doi.org/10.3390/sym11091112

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop