Abstract
The retinal image analysis has been of great interest because of its efficiency and reliability for optical diagnosis. Different techniques have been designed for the segmentation of the eye structures and lesions. In this paper we present an unsupervised method for the segmentation of the optic disc. Blood vessels represent the main obstruction in the optic disc segmentation process. We made use of our previous work in blood vessel segmentation to perform an image reconstruction using the Markov Random Field formulation (MRF). As a result the optic disc appears as a well defined structure. A traditional graph is then constructed using spatial pixel connections as boundary term and the likelihood of the pixels belonging to the foreground and background seeds as regional term. Our algorithm was implemented and tested on two public data sets, DIARETDB1 and DRIVE. The results are evaluated and compared with other methods in the literature.
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
Preview
Unable to display preview. Download preview PDF.
References
Boykov, Y., Funka-Lea, G.: Graph cuts and efficient n-d image segmentation. International Journal of Computer Vision 70(2), 109–131 (2006)
Chittajallu, D.R., Brunner, G., Kurkure, U., Yalamanchili, R.P., Kakadiaris, I.A.: Fuzzy-cuts: A knowledge-driven graph-based method for medical image segmentation. In: Proceedings of the Twenty Third IEEE Coference on Computer, Vision and Pattern Recognition, pp. 715–722 (2009)
Chrastek, R., Wolf, M., Donath, K., Niemann, H., Paulus, D., Hothorn, T., Lausen, B., Lammer, R., Mardin, C.Y., Michelson, G.: Automated segmentation of the optic nerve head for diagnosis of glaucoma. Medical Image Analysis 9(1), 297–314 (2005)
Efros, A.A., Leung, T.K.: Texture synthesis by non-parametric sampling. In: Proceedings of the ICCV, pp. 1033–1038 (1999)
Aquino, A., et al.: Detecting the optic disc boundary in digital fundus images using morphological, edge detection and feature extraction techniques. IEEE Transactions on Medical Imaging 29(10), 1860–1869 (2010)
Kauppi, T., Kalesnykiene, V., Kamarainen, J., Lensu, L., Sorri, I., Raninen, A., Voitilainen, R., Uusitalo, H., Kalviainen, H., Pietila, J.: Diaretdb1 diabetic retinopathy database and evaluation protocol. In: Proceedings of British Machine Vision Conference (2007)
Lowell, J., Hunter, A., Steel, D., Basu, A., Ryder, R., Fletcher, E., Kennedy, L.: Optic nerve head segmentation. IEEE Transactions on Medical Imaging 23(2), 256–264 (2004)
Salazar-Gonzalez, A., Li, Y., Liu, X.: Retinal blood vessel segmentation via graph cut. In: Proceedings of the 11th International Conference on Control, Automation, Robotics and Vision, ICARCV, vol. 1, pp. 225–230 (2010)
Sanchez, C.I., Garcia, M., Mayo, A., Lopez, M.I., Hornero, R.: Retinal image analysis based on mixture models to detect hard exudates. Medical Image Analysis 13, 650–658 (2009)
Staal, J., Abramoff, M.D., Niemeijer, M., Viergever, M.A., van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Transactions on Medical Imaging 23(4), 501–509 (2004)
Welfer, D., Scharcanski, J., Kitamura, C., Dal Pizzol, M., Ludwig, L., Marinho, D.: Segmentation of the optic disc in color eye fundus images using an adaptive morphological approach. Computers in Biology and Medicine 40(1), 124–137 (2010)
Youssif, A., Ghalwash, A., Ghoneim, A.: Optic disc detection from normalized digital fundus images by means of a vessels’s directed matched filter. IEEE Transactions on Medical Imaging 27(1), 11–18 (2008)
Zeng, Y., Samaras, D., Chen, W., Peng, Q.: Topology cuts: a novel min-cut/max-flow algorithm for topology preserving segmentation in n-d images. Journal of computer vision and image understanding 112(1), 81–90 (2008)
Zhu-Jacquot, J., Zabih, R.: Graph cuts segmentation with statistical shape prior for medical image. In: Proceedings of the Third International IEEE Conference on Signal-Image Technologies and Internet-Based System, pp. 631–635 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Salazar-Gonzalez, A., Li, Y., Kaba, D. (2012). MRF Reconstruction of Retinal Images for the Optic Disc Segmentation. In: He, J., Liu, X., Krupinski, E.A., Xu, G. (eds) Health Information Science. HIS 2012. Lecture Notes in Computer Science, vol 7231. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29361-0_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-29361-0_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29360-3
Online ISBN: 978-3-642-29361-0
eBook Packages: Computer ScienceComputer Science (R0)