Construction and verification of color fundus image retinal vessels segmentation algorithm under BP neural network

For the purpose of analyzing the application of back propagation (BP) neural network model in retinal vascular segmentation of color fundus images, in this study, the traditional BP neural network was first improved by adopting the additional momentum method. Second, adaptive histogram, morphologica...

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Bibliographic Details
Published in:The Journal of supercomputing Vol. 77; no. 7; pp. 7171 - 7183
Main Author: Liu, Zhao
Format: Journal Article
Language:English
Published: New York Springer US 01.07.2021
Springer Nature B.V
Subjects:
Algorithms
Artificial neural networks
Back propagation networks
Capillaries
Color
Compilers
Computer Science
Datasets
Deep Learning
Feature extraction
Histograms
Image enhancement
Image segmentation
Interpreters
Neural networks
Parallel Computing in Biomed Sciences & Healthcare
Processor Architectures
Programming Languages
Subtraction
BP neural network model
Color fundus images
CNN algorithm
Rsetinal vascular segmentation
PCNN algorithm
ISSN:0920-8542, 1573-0484
Online Access:Get full text
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Summary:For the purpose of analyzing the application of back propagation (BP) neural network model in retinal vascular segmentation of color fundus images, in this study, the traditional BP neural network was first improved by adopting the additional momentum method. Second, adaptive histogram, morphological background subtraction, Gauss preprocessing matching filter and Heisen matrix were used to enhance the image and extract the features. Third, the retinal vascular segmentation algorithm for color fundus images was constructed based on optimized BP neural network. Finally, the DRIVE and MESSIDOR data sets of color fundus images were introduced to compare the proposed algorithm with the convolutional neural network (CNN) and pulse-coupled neural network (PCNN) algorithms in terms of performance. In addition, the three algorithms were also compared in terms of the sensitivity (Se), specificity (Sp), accuracy (Ac), average operation time for each image and F1 value. The results show that the BP neural network algorithm proposed in this study shows obvious advantage over the other two algorithms in the segmentation of color fundus images. In the DRIVE data set, the Se (81.37%), Sp (90.55%) and Ac (95.82%) of BP algorithm are the highest among the three; in the MESSIDOR data set, the Se (85.22%), Sp (91.08%) and Ac (96.16%) of BP algorithm are also highest; in the DRIVE and MESSIDOR data sets, the operation time of BP algorithm is (28.46 ± 3.19 ms; 24.73 ± 4.81 ms, respectively), which are significantly less than the other two algorithms. Besides, the F1 value of the proposed algorithm is obviously higher than that the other two algorithms. As a result, it is concluded that compared with the CNN algorithm and the PCNN algorithm, the proposed algorithm is more effective in the retinal vascular segmentation of color fundus images; the capillaries have better connectivity, and the proposed algorithm can improve the segmentation Ac while reducing the operation time.
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ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-020-03551-0