Aiming at the problem of large workload and strong subjectivity for manual retinal vessels extraction, this paper proposes a retinal vessel segmentation method that combines regional growing strategy, pulse coupled neural network (PCNN), a Gaussian filter bank and a Gabor filter. First, 2D Gaussian filter bank and 2D Gabor filter are combined to enhance the shape retinal blood vessel region and strengthen the contrast between the blood vessel and the background. Then, PCNN with fast linking mechanism and region growing idea is implemented to achieve automatic retinal vessel segmentation in which the unprocessed pixel with highest intensity is set as the seed, and the adaptive linking weight and stop conditions are adopted. The experimental results on the DRIVE fundus database show that the average accuracy, sensitivity and specificity are 93.96%, 78.64%, 95.64%, respectively. The segmentation results have less vascular breakpoints and clear micro-vessels. This work has promising application value.
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Opto-Electronic Engineering
ISSN: 1003-501X
CN: 51-1346/O4
Monthly, included in CA, Scopus, CSCD
CN: 51-1346/O4
Monthly, included in CA, Scopus, CSCD
Retinal vascular segmentation combined with PCNN and morphological matching enhancement
Author Affiliations
Correspondence: Bangjun.lei@ieee.org
First published at:Apr 01, 2019
Abstract
Overview
References
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Keywords:
Funds:
National Natural Science Foundation of China (61402259, 61272236, U1401252) and Yichang Applied Basic Research Project (A19-302-13)
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Xu Guangzhu, Wang Yawen, Hu Song, et al. Retinal vascular segmentation combined with PCNN and morphological matching enhancement[J]. Opto-Electronic Engineering, 2019, 46(4): 180466.
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