Abstract
In this paper we propose a new approach to the optic disc segmentation process in digital retinal images by means of Topological Active Nets (TAN). This is a deformable model used for image segmentation that integrates features of region-based and edge-based segmentation techniques, being able to fit the edges of the objects and model their inner topology. The optimization of the Active Nets is performed by a genetic algorithm, with adapted or new ad hoc genetic operators to the problem. The active nets incorporate new energy terms for the optic disc segmentations, without the need of any pre-processing of the images. We present results of optic disc segmentations showing the advantages of the approach.
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.
Similar content being viewed by others
References
Lalonde, M., Beaulieu, M., Gagnon, L.: Fast and robust optic disk detection using pyramidal decomposition and Hausdorff-based template matching. IEEE Transaction on Medical Imaging 20, 1193–1200 (2001)
Chrástek, R., Wolf, M., Donath, K., Michelson, G., Niemann, H.: Optic disc segmentation in retinal images. In: Bildverarbeitung fur die Medizin, pp. 263–266 (2002)
Abdel-Ghafar, R.A., Morris, T.: Progress towards automated detection and characterization of the optic disc in glaucoma and diabetic retinopathy. Informatics for Health and Social Care 32(1), 19–25 (2007)
Jelinek, H.F., Depardieu, C., Lucas, C., Cornforth, D.J., Huang, W., Cree, M.J.: Towards vessel characterisation in the vicinity of the optic disc in digital retinal images. In: McCane (ed.) Proceedings of the Image and Vision Computing Conference, New Zealand (2005)
Mendels, F., Heneghan, C., Thiran, J.-P.: Identification of the optic disk boundary in retinal images using active contours. In: Proceedings of the Irish Machine Vision and Image Processing Conference, pp. 103–115 (1999)
Osareh, A., Mirmehdi, M., Thomas, B., Markham, R.: Colour morphology and snakes for optic disc localisation. In: Houston, A., Zwiggelaar, R. (eds.) The 6th Medical Image Understanding and Analysis Conference, pp. 21–24 (2002)
Chanwimaluang, T., Fan, G.: An efficient algorithm for extraction of anatomical structures in retinal images. IEEE International Conference on Image Processing 23, 1093–1096 (2003)
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)
Tsumiyama, K.S.Y., Yamamoto, K.: Active net: Active net model for region extraction. IPSJ SIG notes 89(96), 1–8 (1989)
Ansia, F.M., Penedo, M.G., Mariño, C., Mosquera, A.: A new approach to active nets. Pattern Recognition and Image Analysis 2, 76–77 (1999)
Ballerini, L.: Medical image segmentation using genetic snakes. In: Proceedings of SPIE: Application and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II, vol. 3812, pp. 13–23 (1999)
Fan, Y., Jiang, T.Z., Evans, D.J.: Volumetric segmentation of brain images using parallel genetic algorithms. IEEE Tran. on Medical Imaging 21(8), 904–909 (2002)
Ibáñez, O., Barreira, N., Santos, J., Penedo, M.G.: Topological Active Nets Optimization Using Genetic Algorithms. In: Campilho, A., Kamel, M. (eds.) ICIAR 2006. LNCS, vol. 4141, pp. 272–282. Springer, Heidelberg (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Novo, J., Penedo, M.G., Santos, J. (2008). Optic Disc Segmentation by Means of GA-Optimized Topological Active Nets. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2008. Lecture Notes in Computer Science, vol 5112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69812-8_80
Download citation
DOI: https://doi.org/10.1007/978-3-540-69812-8_80
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69811-1
Online ISBN: 978-3-540-69812-8
eBook Packages: Computer ScienceComputer Science (R0)