Abstract
The detection of significant retinal regions (segmentation) constitutes an indispensible need for computer aided diagnosis of retinal based diseases. At this point, image segmentation algorithm is wanted to be quick in order to spare time for feature selection and classification parts. In this paper, we deal with the fast and accurate segmentation process of optic discs in retinal images. For this purpose, a cascade multithresholding (CMT) process is proposed by a novel optimization algorithm (Scout Particle Swarm Optimization) and an efficient cost function (Kapur).
Scout Particle Swarm Optimization (ScPSO) is originated from Particle Swarm Optimization (PSO) and improves standard PSO by using a necessary part taken from Artificial Bee Colony (ABC) Optimization. In other words, the most important handicap of PSO (regeneration of useless particles) is eliminated via the formation of ScPSO that can be obtained by adding the scout bee phase from ABC into standard PSO. In this study, this novel method (ScPSO) constitutes the optimization part of multithresholding process. Kapur function is preferred as being the cost function to be used in ScPSO, since Kapur provides low standard deviations on output of optimization based multithresholding techniques in literature. In this manner, a well-combined structure (Kapur-ScPSO) is generated for cascade multithresholding. Optic disc images taken from DRIVE database are used for statistical and visual comparison. As a result, Kapur-ScPSO based CMT can define the optic disc quickly (7–8 s) with the rates of 77.08 % precision, 57.89 % overlap and 95.59 % accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Niemeijer, M., Abràmoff, M.D., Van Ginneken, B.: Fast detection of the optic disc and fovea in color fundus photographs. Med. Image Anal. 13(6), 859–870 (2009)
Singh, A., Dutta, M.K., Parthasarathi, M., Burget, R., Riha, K.: An efficient automatic method of optic disc segmentation using region growing technique in retinal images. In: 2014 IEEE International Conference on Contemporary Computing and Informatics (IC3I), pp. 480–484. IEEE Press, New York (2014)
Marin, D., Gegundez-Arias, M.E., Suero, A., Bravo, J.M.: Obtaining optic disc center and pixel region by automatic thresholding methods on morphologically processed fundus images. Comput. Meth. Prog. Bio. 118(2), 173–185 (2015)
Welfer, D., Scharcanski, J., Kitamura, C.M., Dal Pizzol, M.M., Ludwig, L.W., Marinho, D.R.: Segmentation of the optic disk in color eye fundus images using an adaptive morphological approach. Comput. Biol. Med. 40(2), 124–137 (2010)
Muramatsu, C., Nakagawa, T., Sawada, A., Hatanaka, Y., Hara, T., Yamamoto, T., Fujita, H.: Automated segmentation of optic disc region on retinal fundus photographs: comparison of contour modeling and pixel classification methods. Comput. Meth. Prog. Bio. 101(1), 23–32 (2011)
Esmaeili, M., Rabbani, H., Dehnavi, A.M.: Automatic optic disk boundary extraction by the use of curvelet transform and deformable variational level set model. Pattern Recogn. 45(7), 2832–2842 (2012)
Mendonca, A.M., Sousa, A., Mendonca, L., Campilho, A.: Automatic localization of the optic disc by combining vascular and intensity information. Comput. Med. Imag. Grap. 37(5), 409–417 (2013)
Hsiao, H.K., Liu, C.C., Yu, C.Y., Kuo, S.W., Yu, S.S.: A novel optic disc detection scheme on retinal images. Expert Syst. Appl. 39(12), 10600–10606 (2012)
Zhou, H., Li, X., Schaefer, G., Celebi, M.E., Miller, P.: Mean shift based gradient vector flow for image segmentation. Comput. Vis. Image Und. 117(9), 1004–1016 (2013)
Kumara, M.R.S.P., Meegama, R.G.N.: Active contour-based segmentation and removal of optic disk from retinal images. In: 2013 International Conference on Advances in ICT for Emerging Regions (ICTer), pp. 15–20. IEEE Press, New York (2013)
Issac, A., Parthasarthi, M., Dutta, M. K.: An adaptive threshold based algorithm for optic disc and cup segmentation in fundus images. In: 2nd International Conference on Signal Processing and Integrated Networks (SPIN), pp. 143–147. IEEE, New York (2015)
Mohamed, N. A., Zulkifley, M. A., Hussain, A.: On analyzing various density functions of local binary patterns for optic disc segmentation. In: 2015 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE), pp. 37–41. IEEE, New York (2015)
Koyuncu H., Ceylan R.: Scout particle swarm optimization. In: Proceedings of 6th European Conference of the IFMBE, pp. 82–85. Springer, Switzerland (2015)
Karaboga, D., Akay, B.: A comparative study of artificial bee colony algorithm. Appl. Math. Comput. 214, 108–132 (2009)
Kapur, J.N., Sahoo, P.K., Wong, A.K.: A new method for gray-level picture thresholding using the entropy of the histogram. Comput. Vision Graph. 29(3), 273–285 (1985)
Tuba, M.: Multilevel image thresholding by nature-inspired algorithms: a short review. Comput. Sci. J. Moldova. 22(3), 318–338 (2014)
Staal, J.J., Abramoff, M.D., Niemeijer, M., Viergever, M.A., van Ginneken, B.: Ridge based vessel segmentation in color images of the retina. IEEE T. Med. Imaging. 23, 501–509 (2004)
Acknowledgement
This work is supported by the Coordinatorship of Selcuk University’s Scientific Research Projects.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Koyuncu, H., Ceylan, R. (2016). Optic Disc Segmentation with Kapur-ScPSO Based Cascade Multithresholding. In: Ortuño, F., Rojas, I. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2016. Lecture Notes in Computer Science(), vol 9656. Springer, Cham. https://doi.org/10.1007/978-3-319-31744-1_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-31744-1_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-31743-4
Online ISBN: 978-3-319-31744-1
eBook Packages: Computer ScienceComputer Science (R0)