Abstract
In this paper, we propose to use the Particle Swarm Optimization (PSO) algorithm to improve the Multi-Scale Line Detection (MSLD) method for the retinal blood vessel segmentation problem. The PSO algorithm is applied to find the best arrangement of scales in the basic line detector method. The segmentation performance was validated using a public high-resolution fundus images database containing healthy subjects. The optimized MSLD method demonstrates fast convergence to the optimal solution reducing the execution time by approximately 35%. For the same level of specificity, the proposed approach improves the sensitivity rate by 3.1% compared to the original MSLD method. The proposed method will allow to reduce the amount of missing vessels segments that might lead to false positives of red lesions detection in CAD systems used for diabetic retinopathy diagnosis.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Ciulla, T.A., Amador, A.G., Zinman, B.: Diabetic retinopathy and diabetic macular edema pathophysiology, screening, and novel therapies. Diab. Care 26(9), 2653–2664 (2003)
Fraz, M.M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A.R., Owen, C.G., Barman, S.A.: Blood vessel segmentation methodologies in retinal images - a survey. Comput. Methods Programs Biomed. 108(1), 407–433 (2012)
Marin, D., Aquino, A., Gegundez-Arias, M.E., Bravo, J.M.: A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Trans. Med. Imaging 30(1), 146–158 (2011)
Ricci, E., Perfetti, R.: Retinal blood vessel segmentation using line operators and support vector classication. IEEE Trans. Med. Imaging 26(10), 1357–1365 (2007)
Soares, J.V., Leandro, J.J., Cesar Jr., R.M., Jelinek, H.F., Cree, M.J.: Retinal vessel segmentation using the 2-D gabor wavelet and supervised classification. IEEE Trans. Med. Imaging 25(9), 1214–1222 (2006)
Hoover, A., Kouznetsova, V., Goldbaum, M.: Locating blood vessels in retinal images by piecewise thresh-old probing of a matched fillter response. IEEE Trans. Med. Imaging 19(3), 203–210 (2000)
Mendonca, A.M., Campilho, A.: Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Trans. Med. Imaging 25(9), 1200–1213 (2006)
Nguyen, U.T., Bhuiyan, A., Park, L.A., Ramamohanarao, K.: An effective retinal blood vessel segmentation method using multi-scale line detection. Pattern Recogn. 46(3), 703–715 (2013)
Vlachos, M., Dermatas, E.: Multi-scale retinal vessel segmentation using line tracking. Comput. Med. Imaging Graph. 34, 213–227 (2010)
Zhao, Y., Rada, L., Chen, K., Harding, S.P., Zheng, Y.: Automated vessel segmentation using infinite perimeter active contour model with hybrid region information with application to retinal images. IEEE Trans. Med. Imaging 34(9), 1797–1807 (2015)
Emary, E., Zawbaa, H.M., Hassanien, A.E., Schaefer, G., Azar, A.T.: Retinal vessel segmentation based on possibilistic fuzzy c-means clustering optimised with cuckoo search. In: International Joint Conference on Neural Networks (IJCNN), pp. 1792–1796. IEEE (2014)
Zwiggelaar, R., Astley, S.M., Boggis, C.R., Taylor, C.J.: Linear structures in mammographic images: detection and classification. IEEE Trans. Med. Imaging 23(9), 1077–1086 (2004)
Kennedy, J., Eberhart, R.: A new optimizer using particle swarm theory. In: Proceedings of the IEEE Sixth International Symposium on Micro Machine and Human Science, pp. 39–43 (1995)
Del Valle, Y., Venayagamoorthy, G.K., Mohagheghi, S., Hernandez, J.C., Harley, R.G.: Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Trans. Evol. Comput. 12(2), 171–195 (2008)
Huo, Y., Zhuang, Y., Gu, J., Ni, S.: Elite-guided multi-objective artificial bee colony algorithm. Appl. Soft Comput. 32, 199–210 (2015)
Odstrcilik, J., Kolar, R., Budai, A., Hornegger, J., Jan, J., Gazarek, J., Kubena, T., Cernosek, P., Svoboda, O., Angelopoulou, E.: Retinal vessel segmentationby improved matched filtering: evaluation on a new high-resolution fundusimage database. IET Image Process. 7(4), 373–383 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Khomri, B., Christodoulidis, A., Djerou, L., Babahenini, M.C., Cheriet, F. (2017). Particle Swarm Optimization Approach for the Segmentation of Retinal Vessels from Fundus Images. In: Karray, F., Campilho, A., Cheriet, F. (eds) Image Analysis and Recognition. ICIAR 2017. Lecture Notes in Computer Science(), vol 10317. Springer, Cham. https://doi.org/10.1007/978-3-319-59876-5_61
Download citation
DOI: https://doi.org/10.1007/978-3-319-59876-5_61
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59875-8
Online ISBN: 978-3-319-59876-5
eBook Packages: Computer ScienceComputer Science (R0)